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<h1 class="title">The Effects of market conditions on recruitment and completion of clinical trials</h1>
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<div>
<div class="quarto-title-meta-heading">Author</div>
<div class="quarto-title-meta-contents">
<p>Will King </p>
</div>
</div>
</div>
</header>
<section id="setup" class="level1">
<h1>Setup</h1>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(bayesplot)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>This is bayesplot version 1.11.1</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>- Online documentation and vignettes at mc-stan.org/bayesplot</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>- bayesplot theme set to bayesplot::theme_default()</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code> * Does _not_ affect other ggplot2 plots</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code> * See ?bayesplot_theme_set for details on theme setting</code></pre>
</div>
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">available_mcmc</span>(<span class="at">pattern =</span> <span class="st">"_nuts_"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>bayesplot MCMC module:
(matching pattern '_nuts_')
mcmc_nuts_acceptance
mcmc_nuts_divergence
mcmc_nuts_energy
mcmc_nuts_stepsize
mcmc_nuts_treedepth</code></pre>
</div>
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(patchwork)</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.3 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ lubridate 1.9.2 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.0</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all conflicts to become errors</code></pre>
</div>
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rstan)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Loading required package: StanHeaders
rstan version 2.32.6 (Stan version 2.32.2)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
change `threads_per_chain` option:
rstan_options(threads_per_chain = 1)
Attaching package: 'rstan'
The following object is masked from 'package:tidyr':
extract</code></pre>
</div>
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyr)</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ghibli)</span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(xtable)</span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="co">#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started</span></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a><span class="co">#save unchanged models instead of recompiling</span></span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="fu">rstan_options</span>(<span class="at">auto_write =</span> <span class="cn">TRUE</span>)</span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a><span class="co">#allow for multithreaded sampling</span></span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">mc.cores =</span> parallel<span class="sc">::</span><span class="fu">detectCores</span>())</span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a><span class="co">#test installation, shouldn't get any errors</span></span>
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a><span class="co">#example(stan_model, package = "rstan", run.dontrun = TRUE)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="do">################ Pull data from database ######################</span></span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(RPostgreSQL)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Loading required package: DBI</code></pre>
</div>
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>driver <span class="ot">&lt;-</span> <span class="fu">dbDriver</span>(<span class="st">"PostgreSQL"</span>)</span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>get_data <span class="ot">&lt;-</span> <span class="cf">function</span>(driver) {</span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>con <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> driver,</span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> <span class="at">user=</span><span class="st">'root'</span>,</span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> <span class="at">password=</span><span class="st">'root'</span>,</span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a> <span class="at">dbname=</span><span class="st">'aact_db'</span>,</span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a> <span class="at">host=</span><span class="st">'will-office'</span></span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a><span class="fu">on.exit</span>(<span class="fu">dbDisconnect</span>(con))</span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="fu">dbSendQuery</span>(</span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a><span class="co"># "select * from formatted_data_with_planned_enrollment;"</span></span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a><span class="st">"</span></span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a><span class="st">select </span></span>
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a><span class="st"> fdqpe.nct_id</span></span>
<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.start_date</span></span>
<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.current_enrollment</span></span>
<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.enrollment_category</span></span>
<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.current_status </span></span>
<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.earliest_date_observed </span></span>
<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.elapsed_duration</span></span>
<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.n_brands as identical_brands</span></span>
<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a><span class="st"> ,ntbtu.brand_name_count </span></span>
<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.category_id</span></span>
<span id="cb17-29"><a href="#cb17-29" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.final_status</span></span>
<span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.h_sdi_val</span></span>
<span id="cb17-31"><a href="#cb17-31" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.h_sdi_u95</span></span>
<span id="cb17-32"><a href="#cb17-32" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.h_sdi_l95</span></span>
<span id="cb17-33"><a href="#cb17-33" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.hm_sdi_val</span></span>
<span id="cb17-34"><a href="#cb17-34" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.hm_sdi_u95</span></span>
<span id="cb17-35"><a href="#cb17-35" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.hm_sdi_l95</span></span>
<span id="cb17-36"><a href="#cb17-36" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.m_sdi_val</span></span>
<span id="cb17-37"><a href="#cb17-37" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.m_sdi_u95</span></span>
<span id="cb17-38"><a href="#cb17-38" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.m_sdi_l95</span></span>
<span id="cb17-39"><a href="#cb17-39" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.lm_sdi_val</span></span>
<span id="cb17-40"><a href="#cb17-40" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.lm_sdi_u95</span></span>
<span id="cb17-41"><a href="#cb17-41" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.lm_sdi_l95</span></span>
<span id="cb17-42"><a href="#cb17-42" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.l_sdi_val</span></span>
<span id="cb17-43"><a href="#cb17-43" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.l_sdi_u95</span></span>
<span id="cb17-44"><a href="#cb17-44" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.l_sdi_l95</span></span>
<span id="cb17-45"><a href="#cb17-45" aria-hidden="true" tabindex="-1"></a><span class="st">from formatted_data_with_planned_enrollment fdqpe</span></span>
<span id="cb17-46"><a href="#cb17-46" aria-hidden="true" tabindex="-1"></a><span class="st"> join </span><span class="sc">\"</span><span class="st">Formularies</span><span class="sc">\"</span><span class="st">.nct_to_brands_through_uspdc ntbtu</span></span>
<span id="cb17-47"><a href="#cb17-47" aria-hidden="true" tabindex="-1"></a><span class="st"> on fdqpe.nct_id = ntbtu.nct_id </span></span>
<span id="cb17-48"><a href="#cb17-48" aria-hidden="true" tabindex="-1"></a><span class="st">order by fdqpe.nct_id, fdqpe.earliest_date_observed </span></span>
<span id="cb17-49"><a href="#cb17-49" aria-hidden="true" tabindex="-1"></a><span class="st">;</span></span>
<span id="cb17-50"><a href="#cb17-50" aria-hidden="true" tabindex="-1"></a><span class="st">"</span></span>
<span id="cb17-51"><a href="#cb17-51" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb17-52"><a href="#cb17-52" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">fetch</span>(query, <span class="at">n =</span> <span class="sc">-</span><span class="dv">1</span>)</span>
<span id="cb17-53"><a href="#cb17-53" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(df)</span>
<span id="cb17-54"><a href="#cb17-54" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-55"><a href="#cb17-55" aria-hidden="true" tabindex="-1"></a>query2 <span class="ot">&lt;-</span><span class="fu">dbSendQuery</span>(con,<span class="st">"select count(*) from </span><span class="sc">\"</span><span class="st">DiseaseBurden</span><span class="sc">\"</span><span class="st">.icd10_categories ic where </span><span class="sc">\"</span><span class="st">level</span><span class="sc">\"</span><span class="st">=1;"</span>)</span>
<span id="cb17-56"><a href="#cb17-56" aria-hidden="true" tabindex="-1"></a>n_categories <span class="ot">&lt;-</span> <span class="fu">fetch</span>(query2, <span class="at">n =</span> <span class="sc">-</span><span class="dv">1</span>)</span>
<span id="cb17-57"><a href="#cb17-57" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-58"><a href="#cb17-58" aria-hidden="true" tabindex="-1"></a><span class="fu">return</span>(<span class="fu">list</span>(<span class="at">data=</span>df,<span class="at">ncat=</span>n_categories))</span>
<span id="cb17-59"><a href="#cb17-59" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb17-60"><a href="#cb17-60" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-61"><a href="#cb17-61" aria-hidden="true" tabindex="-1"></a>d <span class="ot">&lt;-</span> <span class="fu">get_data</span>(driver)</span>
<span id="cb17-62"><a href="#cb17-62" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> d<span class="sc">$</span>data</span>
<span id="cb17-63"><a href="#cb17-63" aria-hidden="true" tabindex="-1"></a>n_categories <span class="ot">&lt;-</span> d<span class="sc">$</span>ncat</span>
<span id="cb17-64"><a href="#cb17-64" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-65"><a href="#cb17-65" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-66"><a href="#cb17-66" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-67"><a href="#cb17-67" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-68"><a href="#cb17-68" aria-hidden="true" tabindex="-1"></a><span class="do">################ Format Data ###########################</span></span>
<span id="cb17-69"><a href="#cb17-69" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-70"><a href="#cb17-70" aria-hidden="true" tabindex="-1"></a>data_formatter <span class="ot">&lt;-</span> <span class="cf">function</span>(df) {</span>
<span id="cb17-71"><a href="#cb17-71" aria-hidden="true" tabindex="-1"></a>categories <span class="ot">&lt;-</span> df[<span class="st">"category_id"</span>]</span>
<span id="cb17-72"><a href="#cb17-72" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-73"><a href="#cb17-73" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> df[<span class="st">"elapsed_duration"</span>]</span>
<span id="cb17-74"><a href="#cb17-74" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"identical_brands"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>identical_brands)</span>
<span id="cb17-75"><a href="#cb17-75" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"brand_name_counts"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>brand_name_count)</span>
<span id="cb17-76"><a href="#cb17-76" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"h_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>h_sdi_val)</span>
<span id="cb17-77"><a href="#cb17-77" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"hm_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>hm_sdi_val)</span>
<span id="cb17-78"><a href="#cb17-78" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"m_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>m_sdi_val)</span>
<span id="cb17-79"><a href="#cb17-79" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"lm_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>lm_sdi_val)</span>
<span id="cb17-80"><a href="#cb17-80" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"l_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>l_sdi_val)</span>
<span id="cb17-81"><a href="#cb17-81" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-82"><a href="#cb17-82" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-83"><a href="#cb17-83" aria-hidden="true" tabindex="-1"></a><span class="co">#Setup fixed effects</span></span>
<span id="cb17-84"><a href="#cb17-84" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"status_NYR"</span>] <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(df[<span class="st">"current_status"</span>]<span class="sc">==</span><span class="st">"Not yet recruiting"</span>,<span class="dv">1</span>,<span class="dv">0</span>)</span>
<span id="cb17-85"><a href="#cb17-85" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"status_EBI"</span>] <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(df[<span class="st">"current_status"</span>]<span class="sc">==</span><span class="st">"Enrolling by invitation"</span>,<span class="dv">1</span>,<span class="dv">0</span>)</span>
<span id="cb17-86"><a href="#cb17-86" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"status_Rec"</span>] <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(df[<span class="st">"current_status"</span>]<span class="sc">==</span><span class="st">"Recruiting"</span>,<span class="dv">1</span>,<span class="dv">0</span>) </span>
<span id="cb17-87"><a href="#cb17-87" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"status_ANR"</span>] <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(df[<span class="st">"current_status"</span>]<span class="sc">==</span><span class="st">"Active, not recruiting"</span>,<span class="dv">1</span>,<span class="dv">0</span>)</span>
<span id="cb17-88"><a href="#cb17-88" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-89"><a href="#cb17-89" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-90"><a href="#cb17-90" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(df[<span class="st">"final_status"</span>]<span class="sc">==</span><span class="st">"Terminated"</span>,<span class="dv">1</span>,<span class="dv">0</span>)</span>
<span id="cb17-91"><a href="#cb17-91" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-92"><a href="#cb17-92" aria-hidden="true" tabindex="-1"></a><span class="co">#get category list</span></span>
<span id="cb17-93"><a href="#cb17-93" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-94"><a href="#cb17-94" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-95"><a href="#cb17-95" aria-hidden="true" tabindex="-1"></a><span class="fu">return</span>(<span class="fu">list</span>(<span class="at">x=</span>x,<span class="at">y=</span>y))</span>
<span id="cb17-96"><a href="#cb17-96" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb17-97"><a href="#cb17-97" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-98"><a href="#cb17-98" aria-hidden="true" tabindex="-1"></a>train <span class="ot">&lt;-</span> <span class="fu">data_formatter</span>(df)</span>
<span id="cb17-99"><a href="#cb17-99" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-100"><a href="#cb17-100" aria-hidden="true" tabindex="-1"></a>categories <span class="ot">&lt;-</span> df<span class="sc">$</span>category_id</span>
<span id="cb17-101"><a href="#cb17-101" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-102"><a href="#cb17-102" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> train<span class="sc">$</span>x</span>
<span id="cb17-103"><a href="#cb17-103" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> train<span class="sc">$</span>y</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="fit-model" class="level1">
<h1>Fit Model</h1>
<div class="cell">
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="do">################################# FIT MODEL #########################################</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>inherited_cols <span class="ot">&lt;-</span> <span class="fu">c</span>(</span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"elapsed_duration"</span></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a> <span class="co">#,"identical_brands"</span></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a> <span class="co">#,"brand_name_counts"</span></span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"h_sdi_val"</span></span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"hm_sdi_val"</span></span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"m_sdi_val"</span></span>
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"lm_sdi_val"</span></span>
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"l_sdi_val"</span></span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"status_NYR"</span></span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"status_EBI"</span></span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"status_Rec"</span></span>
<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"status_ANR"</span></span>
<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>beta_list <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a> <span class="at">groups =</span> <span class="fu">c</span>(</span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">1</span><span class="st">`</span><span class="ot">=</span><span class="st">"Infections &amp; Parasites"</span>,</span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">2</span><span class="st">`</span><span class="ot">=</span><span class="st">"Neoplasms"</span>,</span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">3</span><span class="st">`</span><span class="ot">=</span><span class="st">"Blood &amp; Immune system"</span>,</span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">4</span><span class="st">`</span><span class="ot">=</span><span class="st">"Endocrine, Nutritional, and Metabolic"</span>,</span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">5</span><span class="st">`</span><span class="ot">=</span><span class="st">"Mental &amp; Behavioral"</span>,</span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">6</span><span class="st">`</span><span class="ot">=</span><span class="st">"Nervous System"</span>,</span>
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">7</span><span class="st">`</span><span class="ot">=</span><span class="st">"Eye and Adnexa"</span>,</span>
<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">8</span><span class="st">`</span><span class="ot">=</span><span class="st">"Ear and Mastoid"</span>,</span>
<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">9</span><span class="st">`</span><span class="ot">=</span><span class="st">"Circulatory"</span>,</span>
<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">10</span><span class="st">`</span><span class="ot">=</span><span class="st">"Respiratory"</span>,</span>
<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">11</span><span class="st">`</span><span class="ot">=</span><span class="st">"Digestive"</span>,</span>
<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">12</span><span class="st">`</span><span class="ot">=</span><span class="st">"Skin &amp; Subcutaneaous tissue"</span>,</span>
<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">13</span><span class="st">`</span><span class="ot">=</span><span class="st">"Musculoskeletal"</span>,</span>
<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">14</span><span class="st">`</span><span class="ot">=</span><span class="st">"Genitourinary"</span>,</span>
<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">15</span><span class="st">`</span><span class="ot">=</span><span class="st">"Pregancy, Childbirth, &amp; Puerperium"</span>,</span>
<span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">16</span><span class="st">`</span><span class="ot">=</span><span class="st">"Perinatal Period"</span>,</span>
<span id="cb19-19"><a href="#cb19-19" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">17</span><span class="st">`</span><span class="ot">=</span><span class="st">"Congential"</span>,</span>
<span id="cb19-20"><a href="#cb19-20" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">18</span><span class="st">`</span><span class="ot">=</span><span class="st">"Symptoms, Signs etc."</span>,</span>
<span id="cb19-21"><a href="#cb19-21" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">19</span><span class="st">`</span><span class="ot">=</span><span class="st">"Injury etc."</span>,</span>
<span id="cb19-22"><a href="#cb19-22" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">20</span><span class="st">`</span><span class="ot">=</span><span class="st">"External Causes"</span>,</span>
<span id="cb19-23"><a href="#cb19-23" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">21</span><span class="st">`</span><span class="ot">=</span><span class="st">"Contact with Healthcare"</span>,</span>
<span id="cb19-24"><a href="#cb19-24" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">22</span><span class="st">`</span><span class="ot">=</span><span class="st">"Special Purposes"</span></span>
<span id="cb19-25"><a href="#cb19-25" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb19-26"><a href="#cb19-26" aria-hidden="true" tabindex="-1"></a> <span class="at">parameters =</span> <span class="fu">c</span>(</span>
<span id="cb19-27"><a href="#cb19-27" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">1</span><span class="st">`</span><span class="ot">=</span><span class="st">"Elapsed Duration"</span>,</span>
<span id="cb19-28"><a href="#cb19-28" aria-hidden="true" tabindex="-1"></a> <span class="co"># brands</span></span>
<span id="cb19-29"><a href="#cb19-29" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">2</span><span class="st">`</span><span class="ot">=</span><span class="st">"asinh(Generic Brands)"</span>,</span>
<span id="cb19-30"><a href="#cb19-30" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">3</span><span class="st">`</span><span class="ot">=</span><span class="st">"asinh(Competitors USPDC)"</span>,</span>
<span id="cb19-31"><a href="#cb19-31" aria-hidden="true" tabindex="-1"></a> <span class="co"># population</span></span>
<span id="cb19-32"><a href="#cb19-32" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">4</span><span class="st">`</span><span class="ot">=</span><span class="st">"asinh(High SDI)"</span>,</span>
<span id="cb19-33"><a href="#cb19-33" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">5</span><span class="st">`</span><span class="ot">=</span><span class="st">"asinh(High-Medium SDI)"</span>,</span>
<span id="cb19-34"><a href="#cb19-34" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">6</span><span class="st">`</span><span class="ot">=</span><span class="st">"asinh(Medium SDI)"</span>,</span>
<span id="cb19-35"><a href="#cb19-35" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">7</span><span class="st">`</span><span class="ot">=</span><span class="st">"asinh(Low-Medium SDI)"</span>,</span>
<span id="cb19-36"><a href="#cb19-36" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">8</span><span class="st">`</span><span class="ot">=</span><span class="st">"asinh(Low SDI)"</span>,</span>
<span id="cb19-37"><a href="#cb19-37" aria-hidden="true" tabindex="-1"></a> <span class="co">#Status</span></span>
<span id="cb19-38"><a href="#cb19-38" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">9</span><span class="st">`</span><span class="ot">=</span><span class="st">"status_NYR"</span>,</span>
<span id="cb19-39"><a href="#cb19-39" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">10</span><span class="st">`</span><span class="ot">=</span><span class="st">"status_EBI"</span>,</span>
<span id="cb19-40"><a href="#cb19-40" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">11</span><span class="st">`</span><span class="ot">=</span><span class="st">"status_Rec"</span>,</span>
<span id="cb19-41"><a href="#cb19-41" aria-hidden="true" tabindex="-1"></a> <span class="st">`</span><span class="at">12</span><span class="st">`</span><span class="ot">=</span><span class="st">"status_ANR"</span></span>
<span id="cb19-42"><a href="#cb19-42" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb19-43"><a href="#cb19-43" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb19-44"><a href="#cb19-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-45"><a href="#cb19-45" aria-hidden="true" tabindex="-1"></a>get_parameters <span class="ot">&lt;-</span> <span class="cf">function</span>(stem,class_list) {</span>
<span id="cb19-46"><a href="#cb19-46" aria-hidden="true" tabindex="-1"></a> <span class="co">#get categories and lengths</span></span>
<span id="cb19-47"><a href="#cb19-47" aria-hidden="true" tabindex="-1"></a> named <span class="ot">&lt;-</span> <span class="fu">names</span>(class_list)</span>
<span id="cb19-48"><a href="#cb19-48" aria-hidden="true" tabindex="-1"></a> lengths <span class="ot">&lt;-</span> <span class="fu">sapply</span>(named, (<span class="cf">function</span> (x) <span class="fu">length</span>(class_list[[x]])))</span>
<span id="cb19-49"><a href="#cb19-49" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-50"><a href="#cb19-50" aria-hidden="true" tabindex="-1"></a> <span class="co">#describe the grid needed</span></span>
<span id="cb19-51"><a href="#cb19-51" aria-hidden="true" tabindex="-1"></a> iter_list <span class="ot">&lt;-</span> <span class="fu">sapply</span>(named, (<span class="cf">function</span> (x) <span class="dv">1</span><span class="sc">:</span>lengths[x]))</span>
<span id="cb19-52"><a href="#cb19-52" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-53"><a href="#cb19-53" aria-hidden="true" tabindex="-1"></a> <span class="co">#generate the list of parameters</span></span>
<span id="cb19-54"><a href="#cb19-54" aria-hidden="true" tabindex="-1"></a> pardf <span class="ot">&lt;-</span> <span class="fu">generate_parameter_df</span>(stem, iter_list)</span>
<span id="cb19-55"><a href="#cb19-55" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-56"><a href="#cb19-56" aria-hidden="true" tabindex="-1"></a> <span class="co">#add columns with appropriate human-readable names</span></span>
<span id="cb19-57"><a href="#cb19-57" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> (name <span class="cf">in</span> named) {</span>
<span id="cb19-58"><a href="#cb19-58" aria-hidden="true" tabindex="-1"></a> pardf[<span class="fu">paste</span>(name,<span class="st">"_hr"</span>,<span class="at">sep=</span><span class="st">""</span>)] <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(</span>
<span id="cb19-59"><a href="#cb19-59" aria-hidden="true" tabindex="-1"></a> <span class="fu">sapply</span>(pardf[name], (<span class="cf">function</span> (i) class_list[[name]][i]))</span>
<span id="cb19-60"><a href="#cb19-60" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb19-61"><a href="#cb19-61" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb19-62"><a href="#cb19-62" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-63"><a href="#cb19-63" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(pardf) </span>
<span id="cb19-64"><a href="#cb19-64" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb19-65"><a href="#cb19-65" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-66"><a href="#cb19-66" aria-hidden="true" tabindex="-1"></a>generate_parameter_df <span class="ot">&lt;-</span> <span class="cf">function</span>(stem, iter_list) {</span>
<span id="cb19-67"><a href="#cb19-67" aria-hidden="true" tabindex="-1"></a> grid <span class="ot">&lt;-</span> <span class="fu">expand.grid</span>(iter_list)</span>
<span id="cb19-68"><a href="#cb19-68" aria-hidden="true" tabindex="-1"></a> grid[<span class="st">"param_name"</span>] <span class="ot">&lt;-</span> grid <span class="sc">%&gt;%</span> <span class="fu">unite</span>(x,<span class="fu">colnames</span>(grid),<span class="at">sep=</span><span class="st">","</span>)</span>
<span id="cb19-69"><a href="#cb19-69" aria-hidden="true" tabindex="-1"></a> grid[<span class="st">"param_name"</span>] <span class="ot">&lt;-</span> <span class="fu">paste</span>(stem,<span class="st">"["</span>,grid<span class="sc">$</span>param_name,<span class="st">"]"</span>,<span class="at">sep=</span><span class="st">""</span>)</span>
<span id="cb19-70"><a href="#cb19-70" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(grid)</span>
<span id="cb19-71"><a href="#cb19-71" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb19-72"><a href="#cb19-72" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-73"><a href="#cb19-73" aria-hidden="true" tabindex="-1"></a>group_mcmc_areas <span class="ot">&lt;-</span> <span class="cf">function</span>(</span>
<span id="cb19-74"><a href="#cb19-74" aria-hidden="true" tabindex="-1"></a> stem,<span class="co"># = "beta"</span></span>
<span id="cb19-75"><a href="#cb19-75" aria-hidden="true" tabindex="-1"></a> class_list,<span class="co"># = beta_list</span></span>
<span id="cb19-76"><a href="#cb19-76" aria-hidden="true" tabindex="-1"></a> stanfit,<span class="co"># = fit</span></span>
<span id="cb19-77"><a href="#cb19-77" aria-hidden="true" tabindex="-1"></a> group_id,<span class="co"># = 2</span></span>
<span id="cb19-78"><a href="#cb19-78" aria-hidden="true" tabindex="-1"></a> <span class="at">rename=</span><span class="cn">TRUE</span>,</span>
<span id="cb19-79"><a href="#cb19-79" aria-hidden="true" tabindex="-1"></a> <span class="at">filter=</span><span class="cn">NULL</span></span>
<span id="cb19-80"><a href="#cb19-80" aria-hidden="true" tabindex="-1"></a> ) {</span>
<span id="cb19-81"><a href="#cb19-81" aria-hidden="true" tabindex="-1"></a> <span class="co">#get all parameter names</span></span>
<span id="cb19-82"><a href="#cb19-82" aria-hidden="true" tabindex="-1"></a> params <span class="ot">&lt;-</span> <span class="fu">get_parameters</span>(stem,class_list)</span>
<span id="cb19-83"><a href="#cb19-83" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-84"><a href="#cb19-84" aria-hidden="true" tabindex="-1"></a> <span class="co">#filter down to parameters of interest</span></span>
<span id="cb19-85"><a href="#cb19-85" aria-hidden="true" tabindex="-1"></a> params <span class="ot">&lt;-</span> <span class="fu">filter</span>(params,groups <span class="sc">==</span> group_id)</span>
<span id="cb19-86"><a href="#cb19-86" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get dataframe with only the rows of interest</span></span>
<span id="cb19-87"><a href="#cb19-87" aria-hidden="true" tabindex="-1"></a> filtdata <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(stanfit)[params<span class="sc">$</span>param_name]</span>
<span id="cb19-88"><a href="#cb19-88" aria-hidden="true" tabindex="-1"></a> <span class="co">#rename columns</span></span>
<span id="cb19-89"><a href="#cb19-89" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (rename) <span class="fu">dimnames</span>(filtdata)[[<span class="dv">2</span>]] <span class="ot">&lt;-</span> params<span class="sc">$</span>parameters_hr</span>
<span id="cb19-90"><a href="#cb19-90" aria-hidden="true" tabindex="-1"></a> <span class="co">#get group name for title</span></span>
<span id="cb19-91"><a href="#cb19-91" aria-hidden="true" tabindex="-1"></a> group_name <span class="ot">&lt;-</span> class_list<span class="sc">$</span>groups[group_id]</span>
<span id="cb19-92"><a href="#cb19-92" aria-hidden="true" tabindex="-1"></a> <span class="co">#create area plot with appropriate title</span></span>
<span id="cb19-93"><a href="#cb19-93" aria-hidden="true" tabindex="-1"></a> p <span class="ot">&lt;-</span> <span class="fu">mcmc_areas</span>(filtdata,<span class="at">prob =</span> <span class="fl">0.8</span>, <span class="at">prob_outer =</span> <span class="fl">0.95</span>) <span class="sc">+</span></span>
<span id="cb19-94"><a href="#cb19-94" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="fu">paste</span>(<span class="st">"Parameter distributions for ICD-10 class:"</span>,group_name)) <span class="sc">+</span></span>
<span id="cb19-95"><a href="#cb19-95" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">0</span>,<span class="at">color=</span><span class="st">"grey"</span>,<span class="at">alpha=</span><span class="fl">0.75</span>)</span>
<span id="cb19-96"><a href="#cb19-96" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-97"><a href="#cb19-97" aria-hidden="true" tabindex="-1"></a> d <span class="ot">&lt;-</span> <span class="fu">pivot_longer</span>(filtdata, <span class="fu">everything</span>()) <span class="sc">|&gt;</span> </span>
<span id="cb19-98"><a href="#cb19-98" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(name) <span class="sc">|&gt;</span> </span>
<span id="cb19-99"><a href="#cb19-99" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(</span>
<span id="cb19-100"><a href="#cb19-100" aria-hidden="true" tabindex="-1"></a> <span class="at">mean=</span><span class="fu">mean</span>(value)</span>
<span id="cb19-101"><a href="#cb19-101" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q025 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.025</span>)</span>
<span id="cb19-102"><a href="#cb19-102" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q975 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.975</span>)</span>
<span id="cb19-103"><a href="#cb19-103" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q05 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.05</span>)</span>
<span id="cb19-104"><a href="#cb19-104" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q95 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.95</span>)</span>
<span id="cb19-105"><a href="#cb19-105" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb19-106"><a href="#cb19-106" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(<span class="fu">list</span>(<span class="at">plot=</span>p,<span class="at">quantiles=</span>d,<span class="at">name=</span>group_name))</span>
<span id="cb19-107"><a href="#cb19-107" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb19-108"><a href="#cb19-108" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-109"><a href="#cb19-109" aria-hidden="true" tabindex="-1"></a>parameter_mcmc_areas <span class="ot">&lt;-</span> <span class="cf">function</span>(</span>
<span id="cb19-110"><a href="#cb19-110" aria-hidden="true" tabindex="-1"></a> stem,<span class="co"># = "beta"</span></span>
<span id="cb19-111"><a href="#cb19-111" aria-hidden="true" tabindex="-1"></a> class_list,<span class="co"># = beta_list</span></span>
<span id="cb19-112"><a href="#cb19-112" aria-hidden="true" tabindex="-1"></a> stanfit,<span class="co"># = fit</span></span>
<span id="cb19-113"><a href="#cb19-113" aria-hidden="true" tabindex="-1"></a> parameter_id,<span class="co"># = 2</span></span>
<span id="cb19-114"><a href="#cb19-114" aria-hidden="true" tabindex="-1"></a> <span class="at">rename=</span><span class="cn">TRUE</span></span>
<span id="cb19-115"><a href="#cb19-115" aria-hidden="true" tabindex="-1"></a> ) {</span>
<span id="cb19-116"><a href="#cb19-116" aria-hidden="true" tabindex="-1"></a> <span class="co">#get all parameter names</span></span>
<span id="cb19-117"><a href="#cb19-117" aria-hidden="true" tabindex="-1"></a> params <span class="ot">&lt;-</span> <span class="fu">get_parameters</span>(stem,class_list)</span>
<span id="cb19-118"><a href="#cb19-118" aria-hidden="true" tabindex="-1"></a> <span class="co">#filter down to parameters of interest</span></span>
<span id="cb19-119"><a href="#cb19-119" aria-hidden="true" tabindex="-1"></a> params <span class="ot">&lt;-</span> <span class="fu">filter</span>(params,parameters <span class="sc">==</span> parameter_id)</span>
<span id="cb19-120"><a href="#cb19-120" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get dataframe with only the rows of interest</span></span>
<span id="cb19-121"><a href="#cb19-121" aria-hidden="true" tabindex="-1"></a> filtdata <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(stanfit)[params<span class="sc">$</span>param_name]</span>
<span id="cb19-122"><a href="#cb19-122" aria-hidden="true" tabindex="-1"></a> <span class="co">#rename columns</span></span>
<span id="cb19-123"><a href="#cb19-123" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (rename) <span class="fu">dimnames</span>(filtdata)[[<span class="dv">2</span>]] <span class="ot">&lt;-</span> params<span class="sc">$</span>groups_hr</span>
<span id="cb19-124"><a href="#cb19-124" aria-hidden="true" tabindex="-1"></a> <span class="co">#get group name for title</span></span>
<span id="cb19-125"><a href="#cb19-125" aria-hidden="true" tabindex="-1"></a> parameter_name <span class="ot">&lt;-</span> class_list<span class="sc">$</span>parameters[parameter_id]</span>
<span id="cb19-126"><a href="#cb19-126" aria-hidden="true" tabindex="-1"></a> <span class="co">#create area plot with appropriate title</span></span>
<span id="cb19-127"><a href="#cb19-127" aria-hidden="true" tabindex="-1"></a> p <span class="ot">&lt;-</span> <span class="fu">mcmc_areas</span>(filtdata,<span class="at">prob =</span> <span class="fl">0.8</span>, <span class="at">prob_outer =</span> <span class="fl">0.95</span>) <span class="sc">+</span></span>
<span id="cb19-128"><a href="#cb19-128" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggtitle</span>(parameter_name,<span class="st">"Parameter Distribution"</span>)</span>
<span id="cb19-129"><a href="#cb19-129" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-130"><a href="#cb19-130" aria-hidden="true" tabindex="-1"></a> d <span class="ot">&lt;-</span> <span class="fu">pivot_longer</span>(filtdata, <span class="fu">everything</span>()) <span class="sc">|&gt;</span> </span>
<span id="cb19-131"><a href="#cb19-131" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(name) <span class="sc">|&gt;</span> </span>
<span id="cb19-132"><a href="#cb19-132" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(</span>
<span id="cb19-133"><a href="#cb19-133" aria-hidden="true" tabindex="-1"></a> <span class="at">mean=</span><span class="fu">mean</span>(value)</span>
<span id="cb19-134"><a href="#cb19-134" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q025 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.025</span>)</span>
<span id="cb19-135"><a href="#cb19-135" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q975 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.975</span>)</span>
<span id="cb19-136"><a href="#cb19-136" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q05 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.05</span>)</span>
<span id="cb19-137"><a href="#cb19-137" aria-hidden="true" tabindex="-1"></a> ,<span class="at">q95 =</span> <span class="fu">quantile</span>(value,<span class="at">probs =</span> <span class="fl">0.95</span>)</span>
<span id="cb19-138"><a href="#cb19-138" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb19-139"><a href="#cb19-139" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(<span class="fu">list</span>(<span class="at">plot=</span>p,<span class="at">quantiles=</span>d,<span class="at">name=</span>parameter_name))</span>
<span id="cb19-140"><a href="#cb19-140" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co">#generics intervention</span></span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>brand_intervention_ib <span class="ot">&lt;-</span> x[<span class="fu">c</span>(inherited_cols,<span class="st">"brand_name_counts"</span>)]</span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>brand_intervention_ib[<span class="st">"identical_brands"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(<span class="fu">sinh</span>(x<span class="sc">$</span>identical_brands)<span class="sc">+</span><span class="dv">1</span>) <span class="co">#add a single generic brand</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>counterfact_marketing_ib <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a> <span class="at">D =</span> <span class="fu">ncol</span>(x),<span class="co">#</span></span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a> <span class="at">N =</span> <span class="fu">nrow</span>(x),</span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a> <span class="at">L =</span> n_categories<span class="sc">$</span>count,</span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="fu">as.vector</span>(y),</span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a> <span class="at">ll =</span> <span class="fu">as.vector</span>(categories),</span>
<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">as.matrix</span>(x),</span>
<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a> <span class="at">mu_mean =</span> <span class="dv">0</span>,</span>
<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a> <span class="at">mu_stdev =</span> <span class="fl">0.05</span>,</span>
<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a> <span class="at">sigma_shape =</span> <span class="dv">4</span>,</span>
<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a> <span class="at">sigma_rate =</span> <span class="dv">20</span>,</span>
<span id="cb21-12"><a href="#cb21-12" aria-hidden="true" tabindex="-1"></a> <span class="at">Nx =</span> <span class="fu">nrow</span>(x),</span>
<span id="cb21-13"><a href="#cb21-13" aria-hidden="true" tabindex="-1"></a> <span class="at">llx =</span> <span class="fu">as.vector</span>(categories),</span>
<span id="cb21-14"><a href="#cb21-14" aria-hidden="true" tabindex="-1"></a> <span class="at">counterfact_x_tilde =</span> <span class="fu">as.matrix</span>(brand_intervention_ib),</span>
<span id="cb21-15"><a href="#cb21-15" aria-hidden="true" tabindex="-1"></a> <span class="at">counterfact_x =</span> <span class="fu">as.matrix</span>(x)</span>
<span id="cb21-16"><a href="#cb21-16" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>fit <span class="ot">&lt;-</span> <span class="fu">stan</span>(</span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a> <span class="at">file=</span><span class="st">'Hierarchal_Logistic.stan'</span>, </span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> counterfact_marketing_ib,</span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a> <span class="at">chains =</span> <span class="dv">4</span>,</span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a> <span class="at">iter =</span> <span class="dv">5000</span>,</span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a> <span class="at">seed =</span> <span class="dv">11021585</span></span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>recompiling to avoid crashing R session</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Trying to compile a simple C file</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>Running /usr/local/lib/R/bin/R CMD SHLIB foo.c
using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
gcc -I"/usr/local/lib/R/include" -DNDEBUG -I"/usr/local/lib/R/site-library/Rcpp/include/" -I"/usr/local/lib/R/site-library/RcppEigen/include/" -I"/usr/local/lib/R/site-library/RcppEigen/include/unsupported" -I"/usr/local/lib/R/site-library/BH/include" -I"/usr/local/lib/R/site-library/StanHeaders/include/src/" -I"/usr/local/lib/R/site-library/StanHeaders/include/" -I"/usr/local/lib/R/site-library/RcppParallel/include/" -I"/usr/local/lib/R/site-library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DUSE_STANC3 -DSTRICT_R_HEADERS -DBOOST_PHOENIX_NO_VARIADIC_EXPRESSION -D_HAS_AUTO_PTR_ETC=0 -include '/usr/local/lib/R/site-library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fpic -g -O2 -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -g -c foo.c -o foo.o
In file included from /usr/local/lib/R/site-library/RcppEigen/include/Eigen/Core:19,
from /usr/local/lib/R/site-library/RcppEigen/include/Eigen/Dense:1,
from /usr/local/lib/R/site-library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp:22,
from &lt;command-line&gt;:
/usr/local/lib/R/site-library/RcppEigen/include/Eigen/src/Core/util/Macros.h:679:10: fatal error: cmath: No such file or directory
679 | #include &lt;cmath&gt;
| ^~~~~~~
compilation terminated.
make: *** [/usr/local/lib/R/etc/Makeconf:191: foo.o] Error 1</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
https://mc-stan.org/misc/warnings.html#bfmi-low</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning: Examine the pairs() plot to diagnose sampling problems</code></pre>
</div>
</div>
<section id="explore-data" class="level2">
<h2 class="anchored" data-anchor-id="explore-data">Explore data</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="do">################################# DATA EXPLORATION ############################</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>driver <span class="ot">&lt;-</span> <span class="fu">dbDriver</span>(<span class="st">"PostgreSQL"</span>)</span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>con <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(</span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a> driver,</span>
<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a> <span class="at">user=</span><span class="st">'root'</span>,</span>
<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a> <span class="at">password=</span><span class="st">'root'</span>,</span>
<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a> <span class="at">dbname=</span><span class="st">'aact_db'</span>,</span>
<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a> <span class="at">host=</span><span class="st">'will-office'</span></span>
<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb28-11"><a href="#cb28-11" aria-hidden="true" tabindex="-1"></a><span class="co">#Plot histogram of count of snapshots</span></span>
<span id="cb28-12"><a href="#cb28-12" aria-hidden="true" tabindex="-1"></a>df3 <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(</span>
<span id="cb28-13"><a href="#cb28-13" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb28-14"><a href="#cb28-14" aria-hidden="true" tabindex="-1"></a> <span class="st">"select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe </span></span>
<span id="cb28-15"><a href="#cb28-15" aria-hidden="true" tabindex="-1"></a><span class="st"> group by nct_id,final_status ;"</span></span>
<span id="cb28-16"><a href="#cb28-16" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb28-17"><a href="#cb28-17" aria-hidden="true" tabindex="-1"></a><span class="co">#df3 &lt;- fetch(query3, n = -1)</span></span>
<span id="cb28-18"><a href="#cb28-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-19"><a href="#cb28-19" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data=</span>df3, <span class="fu">aes</span>(<span class="at">x=</span>count, <span class="at">fill=</span>final_status)) <span class="sc">+</span> </span>
<span id="cb28-20"><a href="#cb28-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb28-21"><a href="#cb28-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Histogram of snapshots per trial (matched trials)"</span>) <span class="sc">+</span></span>
<span id="cb28-22"><a href="#cb28-22" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"Snapshots per trial"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-8-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/HistSnapshots.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Plot duration for terminated vs completed</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>df4 <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(</span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span></span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a><span class="st"> select </span></span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a><span class="st"> nct_id, </span></span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a><span class="st"> start_date , </span></span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date, </span></span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a><span class="st"> overall_status ,</span></span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date - start_date as duration</span></span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a><span class="st"> from ctgov.studies s </span></span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a><span class="st"> where nct_id in (select distinct nct_id from http.download_status ds)</span></span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a><span class="st"> ;"</span></span>
<span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb31-15"><a href="#cb31-15" aria-hidden="true" tabindex="-1"></a><span class="co">#df4 &lt;- fetch(query4, n = -1)</span></span>
<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data=</span>df4, <span class="fu">aes</span>(<span class="at">x=</span>duration,<span class="at">fill=</span>overall_status)) <span class="sc">+</span></span>
<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>()<span class="sc">+</span></span>
<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Histogram of trial durations"</span>) <span class="sc">+</span></span>
<span id="cb31-20"><a href="#cb31-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"duration"</span>)<span class="sc">+</span></span>
<span id="cb31-21"><a href="#cb31-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(<span class="sc">~</span>overall_status)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
</div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-8-2.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/HistTrialDurations_Faceted.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
</div>
<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>df5 <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(</span>
<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span></span>
<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a><span class="st"> with cte1 as (</span></span>
<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a><span class="st"> select </span></span>
<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a><span class="st"> nct_id, </span></span>
<span id="cb35-7"><a href="#cb35-7" aria-hidden="true" tabindex="-1"></a><span class="st"> start_date , </span></span>
<span id="cb35-8"><a href="#cb35-8" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date, </span></span>
<span id="cb35-9"><a href="#cb35-9" aria-hidden="true" tabindex="-1"></a><span class="st"> overall_status ,</span></span>
<span id="cb35-10"><a href="#cb35-10" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date - start_date as duration</span></span>
<span id="cb35-11"><a href="#cb35-11" aria-hidden="true" tabindex="-1"></a><span class="st"> from ctgov.studies s </span></span>
<span id="cb35-12"><a href="#cb35-12" aria-hidden="true" tabindex="-1"></a><span class="st"> where nct_id in (select distinct nct_id from http.download_status ds)</span></span>
<span id="cb35-13"><a href="#cb35-13" aria-hidden="true" tabindex="-1"></a><span class="st"> ), cte2 as (</span></span>
<span id="cb35-14"><a href="#cb35-14" aria-hidden="true" tabindex="-1"></a><span class="st"> select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe</span></span>
<span id="cb35-15"><a href="#cb35-15" aria-hidden="true" tabindex="-1"></a><span class="st"> group by nct_id</span></span>
<span id="cb35-16"><a href="#cb35-16" aria-hidden="true" tabindex="-1"></a><span class="st"> )</span></span>
<span id="cb35-17"><a href="#cb35-17" aria-hidden="true" tabindex="-1"></a><span class="st"> select a.nct_id, a.overall_status, a.duration,b.snapshot_count</span></span>
<span id="cb35-18"><a href="#cb35-18" aria-hidden="true" tabindex="-1"></a><span class="st"> from cte1 as a</span></span>
<span id="cb35-19"><a href="#cb35-19" aria-hidden="true" tabindex="-1"></a><span class="st"> join cte2 as b</span></span>
<span id="cb35-20"><a href="#cb35-20" aria-hidden="true" tabindex="-1"></a><span class="st"> on a.nct_id=b.nct_id</span></span>
<span id="cb35-21"><a href="#cb35-21" aria-hidden="true" tabindex="-1"></a><span class="st"> ;"</span></span>
<span id="cb35-22"><a href="#cb35-22" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb35-23"><a href="#cb35-23" aria-hidden="true" tabindex="-1"></a>df5<span class="sc">$</span>overall_status <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(df5<span class="sc">$</span>overall_status)</span>
<span id="cb35-24"><a href="#cb35-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-25"><a href="#cb35-25" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data=</span>df5, <span class="fu">aes</span>(<span class="at">x=</span>duration,<span class="at">y=</span>snapshot_count,<span class="at">color=</span>overall_status)) <span class="sc">+</span></span>
<span id="cb35-26"><a href="#cb35-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_jitter</span>() <span class="sc">+</span></span>
<span id="cb35-27"><a href="#cb35-27" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Comparison of duration, status, and snapshot_count"</span>) <span class="sc">+</span></span>
<span id="cb35-28"><a href="#cb35-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"duration"</span>) <span class="sc">+</span></span>
<span id="cb35-29"><a href="#cb35-29" aria-hidden="true" tabindex="-1"></a> <span class="fu">ylab</span>(<span class="st">"snapshot count"</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-8-3.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/SnapshotsVsDurationVsTermination.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="fu">dbDisconnect</span>(con)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] TRUE</code></pre>
</div>
<div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="co">#get number of trials and snapshots in each category</span></span>
<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>group_trials_by_category <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(<span class="fu">aggregate</span>(category_id <span class="sc">~</span> nct_id, df, max))</span>
<span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>group_trials_by_category <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(group_trials_by_category)</span>
<span id="cb40-4"><a href="#cb40-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb40-5"><a href="#cb40-5" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> group_trials_by_category, <span class="fu">aes</span>(<span class="at">x=</span>category_id)) <span class="sc">+</span></span>
<span id="cb40-6"><a href="#cb40-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">binwidth=</span><span class="dv">1</span>,<span class="at">color=</span><span class="st">"black"</span>,<span class="at">fill=</span><span class="st">"seagreen"</span>) <span class="sc">+</span></span>
<span id="cb40-7"><a href="#cb40-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_x_continuous</span>(<span class="at">breaks=</span>scales<span class="sc">::</span><span class="fu">pretty_breaks</span>(<span class="at">n=</span><span class="dv">22</span>)) <span class="sc">+</span> </span>
<span id="cb40-8"><a href="#cb40-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb40-9"><a href="#cb40-9" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"bar chart of trial categories"</span></span>
<span id="cb40-10"><a href="#cb40-10" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Category ID"</span></span>
<span id="cb40-11"><a href="#cb40-11" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Count"</span></span>
<span id="cb40-12"><a href="#cb40-12" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning in geom_bar(binwidth = 1, color = "black", fill = "seagreen"): Ignoring
unknown parameters: `binwidth`</code></pre>
</div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-8-4.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/CategoryCounts.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb44"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(df5)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> nct_id overall_status duration snapshot_count
Length:162 Completed :134 Min. : 61.0 Min. : 1.000
Class :character Terminated: 28 1st Qu.: 618.5 1st Qu.: 4.000
Mode :character Median :1022.5 Median : 6.000
Mean :1202.4 Mean : 8.315
3rd Qu.:1637.0 3rd Qu.:11.000
Max. :3332.0 Max. :48.000 </code></pre>
</div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>category_count <span class="ot">&lt;-</span> group_trials_by_category <span class="sc">|&gt;</span> <span class="fu">group_by</span>(category_id) <span class="sc">|&gt;</span> <span class="fu">count</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="fit-results" class="level2">
<h2 class="anchored" data-anchor-id="fit-results">Fit Results</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb47"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="do">################################# ANALYZE #####################################</span></span>
<span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(fit)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Inference for Stan model: anon_model.
4 chains, each with iter=5000; warmup=2500; thin=1;
post-warmup draws per chain=2500, total post-warmup draws=10000.
mean se_mean sd 2.5% 25% 50%
mu[1] -0.02 0.00 0.05 -0.12 -0.06 -0.03
mu[2] 0.00 0.00 0.05 -0.10 -0.04 0.00
mu[3] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu[4] -0.04 0.00 0.05 -0.14 -0.08 -0.04
mu[5] -0.04 0.00 0.05 -0.13 -0.07 -0.04
mu[6] -0.03 0.00 0.05 -0.13 -0.06 -0.03
mu[7] -0.01 0.00 0.05 -0.11 -0.04 -0.01
mu[8] 0.00 0.00 0.05 -0.09 -0.03 0.00
mu[9] 0.00 0.00 0.05 -0.10 -0.04 0.00
mu[10] 0.00 0.00 0.05 -0.10 -0.04 0.00
mu[11] 0.00 0.00 0.05 -0.09 -0.03 0.00
mu[12] -0.03 0.00 0.05 -0.13 -0.06 -0.03
sigma[1] 0.27 0.00 0.12 0.07 0.19 0.26
sigma[2] 0.91 0.00 0.19 0.57 0.78 0.90
sigma[3] 0.66 0.00 0.18 0.34 0.54 0.65
sigma[4] 0.31 0.00 0.09 0.15 0.24 0.30
sigma[5] 0.18 0.00 0.09 0.05 0.12 0.17
sigma[6] 0.19 0.00 0.09 0.06 0.12 0.18
sigma[7] 0.18 0.00 0.09 0.05 0.12 0.17
sigma[8] 0.17 0.00 0.08 0.05 0.11 0.16
sigma[9] 0.32 0.01 0.15 0.08 0.21 0.30
sigma[10] 0.19 0.00 0.10 0.05 0.12 0.18
sigma[11] 0.23 0.00 0.12 0.06 0.14 0.21
sigma[12] 0.28 0.00 0.13 0.09 0.19 0.27
beta[1,1] -0.10 0.00 0.25 -0.65 -0.24 -0.09
beta[1,2] -0.42 0.00 0.42 -1.23 -0.71 -0.42
beta[1,3] 0.68 0.00 0.40 -0.07 0.41 0.67
beta[1,4] -0.46 0.00 0.12 -0.71 -0.54 -0.46
beta[1,5] 0.00 0.00 0.18 -0.35 -0.11 -0.01
beta[1,6] 0.05 0.00 0.18 -0.29 -0.07 0.03
beta[1,7] 0.07 0.00 0.17 -0.24 -0.04 0.06
beta[1,8] 0.06 0.00 0.15 -0.23 -0.04 0.05
beta[1,9] 0.32 0.01 0.38 -0.24 0.06 0.25
beta[1,10] -0.03 0.00 0.22 -0.53 -0.14 -0.02
beta[1,11] 0.02 0.00 0.22 -0.43 -0.10 0.02
beta[1,12] -0.22 0.00 0.27 -0.82 -0.37 -0.19
beta[2,1] -0.41 0.01 0.26 -0.99 -0.58 -0.39
beta[2,2] -1.24 0.00 0.27 -1.78 -1.43 -1.24
beta[2,3] 0.47 0.00 0.20 0.08 0.34 0.47
beta[2,4] 0.25 0.00 0.22 -0.14 0.10 0.23
beta[2,5] -0.09 0.00 0.18 -0.51 -0.20 -0.08
beta[2,6] -0.12 0.00 0.19 -0.55 -0.23 -0.11
beta[2,7] -0.07 0.00 0.17 -0.46 -0.17 -0.06
beta[2,8] 0.05 0.00 0.16 -0.25 -0.05 0.04
beta[2,9] -0.48 0.01 0.40 -1.43 -0.71 -0.41
beta[2,10] 0.00 0.00 0.23 -0.48 -0.12 0.00
beta[2,11] -0.14 0.00 0.21 -0.61 -0.26 -0.12
beta[2,12] -0.36 0.01 0.27 -0.96 -0.53 -0.33
beta[3,1] -0.03 0.00 0.30 -0.65 -0.19 -0.03
beta[3,2] -0.12 0.01 0.93 -2.03 -0.71 -0.11
beta[3,3] -0.10 0.01 0.69 -1.52 -0.52 -0.09
beta[3,4] -0.19 0.00 0.29 -0.80 -0.37 -0.18
beta[3,5] -0.10 0.00 0.20 -0.56 -0.20 -0.08
beta[3,6] -0.10 0.00 0.21 -0.57 -0.21 -0.08
beta[3,7] -0.08 0.00 0.20 -0.52 -0.18 -0.06
beta[3,8] -0.06 0.00 0.19 -0.50 -0.16 -0.04
beta[3,9] 0.00 0.00 0.34 -0.70 -0.19 0.00
beta[3,10] 0.00 0.00 0.22 -0.48 -0.11 0.00
beta[3,11] 0.00 0.00 0.26 -0.57 -0.13 0.00
beta[3,12] -0.03 0.00 0.31 -0.67 -0.20 -0.04
beta[4,1] -0.04 0.00 0.29 -0.62 -0.20 -0.04
beta[4,2] -0.43 0.00 0.57 -1.59 -0.80 -0.42
beta[4,3] -0.67 0.01 0.55 -1.85 -1.02 -0.63
beta[4,4] 0.06 0.00 0.25 -0.42 -0.10 0.06
beta[4,5] -0.03 0.00 0.17 -0.39 -0.13 -0.03
beta[4,6] -0.08 0.00 0.19 -0.49 -0.18 -0.07
beta[4,7] 0.00 0.00 0.18 -0.37 -0.10 0.00
beta[4,8] 0.07 0.00 0.18 -0.26 -0.04 0.06
beta[4,9] -0.13 0.00 0.34 -0.91 -0.30 -0.10
beta[4,10] -0.01 0.00 0.22 -0.49 -0.12 -0.01
beta[4,11] 0.21 0.01 0.30 -0.23 0.01 0.15
beta[4,12] -0.21 0.01 0.31 -0.95 -0.37 -0.17
beta[5,1] -0.10 0.00 0.29 -0.75 -0.26 -0.08
beta[5,2] -1.41 0.01 0.92 -3.40 -1.98 -1.35
beta[5,3] 0.26 0.01 0.67 -1.01 -0.18 0.23
beta[5,4] 0.02 0.00 0.24 -0.45 -0.13 0.02
beta[5,5] -0.02 0.00 0.18 -0.38 -0.12 -0.02
beta[5,6] -0.05 0.00 0.19 -0.45 -0.15 -0.04
beta[5,7] 0.05 0.00 0.19 -0.29 -0.06 0.04
beta[5,8] 0.09 0.00 0.19 -0.24 -0.03 0.07
beta[5,9] 0.01 0.00 0.33 -0.67 -0.17 0.00
beta[5,10] -0.01 0.00 0.22 -0.49 -0.12 -0.01
beta[5,11] 0.08 0.00 0.26 -0.38 -0.07 0.05
beta[5,12] -0.18 0.00 0.31 -0.89 -0.33 -0.14
beta[6,1] -0.06 0.00 0.29 -0.67 -0.22 -0.06
beta[6,2] 2.93 0.01 0.88 1.33 2.32 2.89
beta[6,3] 0.21 0.00 0.34 -0.48 -0.02 0.21
beta[6,4] -0.48 0.00 0.28 -1.09 -0.65 -0.46
beta[6,5] -0.14 0.00 0.20 -0.59 -0.25 -0.12
beta[6,6] -0.07 0.00 0.19 -0.48 -0.18 -0.06
beta[6,7] -0.01 0.00 0.18 -0.39 -0.12 -0.01
beta[6,8] 0.05 0.00 0.18 -0.29 -0.06 0.04
beta[6,9] 0.02 0.00 0.33 -0.65 -0.17 0.01
beta[6,10] 0.00 0.00 0.22 -0.47 -0.12 0.00
beta[6,11] -0.10 0.00 0.26 -0.70 -0.22 -0.07
beta[6,12] 0.04 0.00 0.30 -0.52 -0.14 0.02
beta[7,1] -0.14 0.00 0.31 -0.84 -0.30 -0.10
beta[7,2] -0.18 0.01 0.53 -1.24 -0.52 -0.17
beta[7,3] 1.42 0.01 0.73 0.17 0.91 1.36
beta[7,4] -0.08 0.00 0.25 -0.58 -0.24 -0.08
beta[7,5] -0.11 0.00 0.19 -0.54 -0.22 -0.09
beta[7,6] -0.12 0.00 0.20 -0.57 -0.23 -0.10
beta[7,7] -0.08 0.00 0.19 -0.50 -0.18 -0.06
beta[7,8] -0.06 0.00 0.19 -0.50 -0.16 -0.05
beta[7,9] 0.05 0.00 0.34 -0.62 -0.14 0.04
beta[7,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[7,11] -0.03 0.00 0.24 -0.58 -0.15 -0.02
beta[7,12] -0.06 0.00 0.29 -0.68 -0.23 -0.06
beta[8,1] -0.03 0.00 0.31 -0.67 -0.20 -0.03
beta[8,2] 0.01 0.01 0.94 -1.87 -0.59 0.01
beta[8,3] 0.00 0.01 0.69 -1.42 -0.42 0.00
beta[8,4] -0.04 0.00 0.32 -0.70 -0.24 -0.04
beta[8,5] -0.04 0.00 0.20 -0.45 -0.15 -0.04
beta[8,6] -0.03 0.00 0.22 -0.49 -0.15 -0.03
beta[8,7] -0.01 0.00 0.21 -0.44 -0.12 -0.01
beta[8,8] 0.00 0.00 0.20 -0.41 -0.10 0.00
beta[8,9] 0.00 0.00 0.35 -0.74 -0.19 0.00
beta[8,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[8,11] 0.00 0.00 0.25 -0.53 -0.13 0.00
beta[8,12] -0.04 0.00 0.31 -0.69 -0.21 -0.04
beta[9,1] -0.04 0.00 0.30 -0.68 -0.20 -0.04
beta[9,2] -0.64 0.01 0.83 -2.39 -1.16 -0.60
beta[9,3] -0.56 0.01 0.60 -1.84 -0.93 -0.52
beta[9,4] 0.02 0.00 0.26 -0.52 -0.15 0.02
beta[9,5] 0.03 0.00 0.19 -0.32 -0.09 0.02
beta[9,6] 0.10 0.00 0.21 -0.24 -0.04 0.07
beta[9,7] 0.11 0.00 0.20 -0.23 -0.02 0.08
beta[9,8] 0.10 0.00 0.19 -0.23 -0.02 0.07
beta[9,9] 0.05 0.00 0.34 -0.63 -0.14 0.03
beta[9,10] 0.00 0.00 0.23 -0.47 -0.12 0.00
beta[9,11] -0.05 0.00 0.26 -0.63 -0.17 -0.03
beta[9,12] -0.01 0.00 0.31 -0.64 -0.18 -0.02
beta[10,1] -0.03 0.00 0.30 -0.64 -0.19 -0.03
beta[10,2] -0.24 0.01 0.89 -2.03 -0.80 -0.22
beta[10,3] -0.11 0.01 0.68 -1.53 -0.52 -0.09
beta[10,4] -0.22 0.00 0.29 -0.85 -0.39 -0.20
beta[10,5] -0.11 0.00 0.20 -0.56 -0.21 -0.09
beta[10,6] -0.11 0.00 0.21 -0.59 -0.22 -0.09
beta[10,7] -0.09 0.00 0.20 -0.55 -0.19 -0.07
beta[10,8] -0.07 0.00 0.19 -0.51 -0.16 -0.05
beta[10,9] -0.01 0.00 0.35 -0.73 -0.19 -0.01
beta[10,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[10,11] 0.00 0.00 0.26 -0.55 -0.13 0.00
beta[10,12] -0.04 0.00 0.32 -0.68 -0.21 -0.04
beta[11,1] -0.03 0.00 0.30 -0.65 -0.20 -0.03
beta[11,2] -0.15 0.01 0.90 -2.00 -0.71 -0.14
beta[11,3] -0.08 0.01 0.68 -1.47 -0.50 -0.07
beta[11,4] -0.27 0.00 0.28 -0.88 -0.44 -0.25
beta[11,5] -0.12 0.00 0.20 -0.59 -0.22 -0.10
beta[11,6] -0.12 0.00 0.21 -0.62 -0.23 -0.10
beta[11,7] -0.09 0.00 0.21 -0.58 -0.20 -0.07
beta[11,8] -0.07 0.00 0.19 -0.52 -0.18 -0.05
beta[11,9] 0.00 0.00 0.35 -0.75 -0.19 0.00
beta[11,10] -0.01 0.00 0.22 -0.48 -0.12 0.00
beta[11,11] -0.01 0.00 0.26 -0.55 -0.14 0.00
beta[11,12] -0.04 0.00 0.32 -0.70 -0.21 -0.04
beta[12,1] -0.17 0.00 0.29 -0.85 -0.32 -0.14
beta[12,2] -0.71 0.01 0.84 -2.48 -1.25 -0.68
beta[12,3] 0.34 0.01 0.62 -0.81 -0.06 0.32
beta[12,4] -0.19 0.00 0.24 -0.70 -0.33 -0.17
beta[12,5] -0.06 0.00 0.18 -0.46 -0.16 -0.06
beta[12,6] 0.01 0.00 0.19 -0.36 -0.11 0.00
beta[12,7] 0.02 0.00 0.18 -0.33 -0.09 0.01
beta[12,8] 0.05 0.00 0.18 -0.30 -0.06 0.04
beta[12,9] 0.04 0.00 0.33 -0.63 -0.14 0.02
beta[12,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[12,11] 0.05 0.00 0.26 -0.43 -0.09 0.04
beta[12,12] -0.14 0.00 0.30 -0.82 -0.30 -0.12
beta[13,1] 0.10 0.00 0.30 -0.41 -0.08 0.06
beta[13,2] 1.52 0.01 0.55 0.53 1.15 1.49
beta[13,3] -1.42 0.01 0.55 -2.60 -1.76 -1.37
beta[13,4] -0.09 0.00 0.24 -0.58 -0.24 -0.08
beta[13,5] -0.07 0.00 0.18 -0.46 -0.17 -0.06
beta[13,6] -0.03 0.00 0.18 -0.41 -0.14 -0.03
beta[13,7] 0.01 0.00 0.18 -0.36 -0.10 0.00
beta[13,8] 0.01 0.00 0.18 -0.35 -0.09 0.01
beta[13,9] -0.07 0.00 0.32 -0.80 -0.24 -0.05
beta[13,10] 0.00 0.00 0.22 -0.46 -0.11 0.00
beta[13,11] 0.12 0.00 0.26 -0.33 -0.04 0.08
beta[13,12] -0.21 0.01 0.31 -0.94 -0.38 -0.17
beta[14,1] -0.03 0.00 0.31 -0.67 -0.19 -0.03
beta[14,2] -0.29 0.01 0.91 -2.12 -0.87 -0.27
beta[14,3] -0.17 0.01 0.66 -1.56 -0.58 -0.15
beta[14,4] -0.19 0.00 0.30 -0.83 -0.36 -0.17
beta[14,5] -0.10 0.00 0.20 -0.55 -0.20 -0.08
beta[14,6] -0.09 0.00 0.21 -0.57 -0.20 -0.07
beta[14,7] -0.07 0.00 0.20 -0.53 -0.18 -0.05
beta[14,8] -0.05 0.00 0.19 -0.49 -0.14 -0.03
beta[14,9] -0.01 0.00 0.35 -0.74 -0.20 -0.01
beta[14,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[14,11] 0.01 0.00 0.26 -0.53 -0.13 0.01
beta[14,12] -0.03 0.00 0.31 -0.69 -0.21 -0.03
beta[15,1] -0.03 0.00 0.29 -0.62 -0.19 -0.03
beta[15,2] -0.01 0.01 0.95 -1.91 -0.60 -0.01
beta[15,3] -0.01 0.01 0.68 -1.38 -0.43 0.00
beta[15,4] -0.04 0.00 0.33 -0.70 -0.24 -0.04
beta[15,5] -0.04 0.00 0.21 -0.47 -0.15 -0.04
beta[15,6] -0.03 0.00 0.22 -0.49 -0.15 -0.03
beta[15,7] -0.01 0.00 0.21 -0.44 -0.12 -0.01
beta[15,8] 0.00 0.00 0.20 -0.42 -0.10 0.00
beta[15,9] -0.01 0.00 0.35 -0.74 -0.20 -0.01
beta[15,10] 0.00 0.00 0.22 -0.48 -0.11 0.00
beta[15,11] 0.00 0.00 0.26 -0.55 -0.13 0.00
beta[15,12] -0.03 0.00 0.32 -0.68 -0.20 -0.04
beta[16,1] -0.02 0.00 0.31 -0.65 -0.19 -0.02
beta[16,2] 0.00 0.01 0.95 -1.90 -0.59 0.00
beta[16,3] 0.00 0.01 0.69 -1.40 -0.42 0.00
beta[16,4] -0.04 0.00 0.33 -0.69 -0.24 -0.05
beta[16,5] -0.04 0.00 0.21 -0.48 -0.15 -0.04
beta[16,6] -0.03 0.00 0.21 -0.47 -0.15 -0.03
beta[16,7] -0.01 0.00 0.21 -0.44 -0.12 -0.01
beta[16,8] 0.00 0.00 0.20 -0.42 -0.10 0.00
beta[16,9] 0.00 0.00 0.36 -0.75 -0.19 -0.01
beta[16,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[16,11] 0.00 0.00 0.26 -0.55 -0.13 0.00
beta[16,12] -0.03 0.00 0.31 -0.68 -0.20 -0.03
beta[17,1] -0.03 0.00 0.31 -0.68 -0.20 -0.03
beta[17,2] -0.16 0.01 0.91 -1.99 -0.73 -0.14
beta[17,3] -0.10 0.01 0.70 -1.53 -0.52 -0.08
beta[17,4] -0.22 0.00 0.29 -0.86 -0.38 -0.20
beta[17,5] -0.11 0.00 0.20 -0.56 -0.21 -0.09
beta[17,6] -0.11 0.00 0.20 -0.57 -0.22 -0.09
beta[17,7] -0.09 0.00 0.20 -0.53 -0.19 -0.07
beta[17,8] -0.07 0.00 0.19 -0.51 -0.17 -0.05
beta[17,9] -0.01 0.00 0.35 -0.72 -0.19 -0.01
beta[17,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[17,11] 0.00 0.00 0.26 -0.55 -0.13 0.00
beta[17,12] -0.03 0.00 0.32 -0.68 -0.21 -0.04
beta[18,1] -0.03 0.00 0.31 -0.67 -0.19 -0.03
beta[18,2] -0.13 0.01 0.93 -2.01 -0.72 -0.12
beta[18,3] -0.06 0.01 0.69 -1.47 -0.47 -0.04
beta[18,4] -0.19 0.00 0.29 -0.82 -0.36 -0.17
beta[18,5] -0.09 0.00 0.20 -0.54 -0.20 -0.07
beta[18,6] -0.09 0.00 0.20 -0.54 -0.20 -0.07
beta[18,7] -0.06 0.00 0.20 -0.51 -0.17 -0.05
beta[18,8] -0.05 0.00 0.19 -0.48 -0.15 -0.03
beta[18,9] -0.01 0.00 0.35 -0.74 -0.19 -0.01
beta[18,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[18,11] 0.00 0.00 0.27 -0.56 -0.13 0.00
beta[18,12] -0.04 0.00 0.32 -0.70 -0.21 -0.04
beta[19,1] -0.02 0.00 0.29 -0.64 -0.19 -0.03
beta[19,2] 0.00 0.01 0.95 -1.88 -0.59 0.00
beta[19,3] 0.00 0.01 0.70 -1.39 -0.43 0.00
beta[19,4] -0.05 0.00 0.33 -0.70 -0.25 -0.05
beta[19,5] -0.03 0.00 0.21 -0.45 -0.14 -0.04
beta[19,6] -0.03 0.00 0.21 -0.47 -0.15 -0.03
beta[19,7] -0.01 0.00 0.21 -0.45 -0.13 -0.01
beta[19,8] 0.00 0.00 0.20 -0.41 -0.10 0.00
beta[19,9] 0.00 0.00 0.35 -0.74 -0.19 -0.01
beta[19,10] 0.00 0.00 0.22 -0.46 -0.12 0.00
beta[19,11] 0.00 0.00 0.26 -0.54 -0.13 0.00
beta[19,12] -0.03 0.00 0.31 -0.68 -0.20 -0.03
beta[20,1] -0.02 0.00 0.31 -0.64 -0.19 -0.02
beta[20,2] -0.02 0.01 0.91 -1.82 -0.61 -0.02
beta[20,3] 0.00 0.01 0.68 -1.36 -0.43 0.00
beta[20,4] -0.04 0.00 0.33 -0.70 -0.25 -0.05
beta[20,5] -0.04 0.00 0.21 -0.47 -0.15 -0.04
beta[20,6] -0.03 0.00 0.22 -0.49 -0.15 -0.03
beta[20,7] -0.01 0.00 0.21 -0.45 -0.13 -0.01
beta[20,8] 0.00 0.00 0.19 -0.40 -0.10 0.00
beta[20,9] 0.00 0.00 0.35 -0.73 -0.19 0.00
beta[20,10] -0.01 0.00 0.23 -0.49 -0.11 -0.01
beta[20,11] 0.00 0.00 0.26 -0.53 -0.13 0.00
beta[20,12] -0.03 0.00 0.31 -0.65 -0.20 -0.03
beta[21,1] -0.03 0.00 0.31 -0.65 -0.19 -0.03
beta[21,2] 0.00 0.01 0.92 -1.84 -0.58 -0.01
beta[21,3] 0.00 0.01 0.68 -1.39 -0.44 -0.01
beta[21,4] -0.04 0.00 0.32 -0.68 -0.24 -0.05
beta[21,5] -0.04 0.00 0.21 -0.47 -0.15 -0.04
beta[21,6] -0.03 0.00 0.22 -0.49 -0.14 -0.03
beta[21,7] -0.01 0.00 0.21 -0.43 -0.13 -0.01
beta[21,8] 0.00 0.00 0.20 -0.41 -0.11 0.00
beta[21,9] 0.00 0.00 0.35 -0.73 -0.19 -0.01
beta[21,10] 0.00 0.00 0.22 -0.47 -0.11 0.00
beta[21,11] 0.01 0.00 0.26 -0.53 -0.13 0.00
beta[21,12] -0.03 0.00 0.31 -0.67 -0.20 -0.03
beta[22,1] -0.02 0.00 0.31 -0.65 -0.19 -0.03
beta[22,2] -0.01 0.01 0.92 -1.86 -0.60 -0.01
beta[22,3] 0.00 0.01 0.69 -1.35 -0.43 0.00
beta[22,4] -0.04 0.00 0.33 -0.71 -0.24 -0.04
beta[22,5] -0.04 0.00 0.21 -0.47 -0.15 -0.04
beta[22,6] -0.03 0.00 0.22 -0.47 -0.15 -0.03
beta[22,7] -0.01 0.00 0.21 -0.43 -0.13 -0.02
beta[22,8] 0.01 0.00 0.20 -0.41 -0.10 0.01
beta[22,9] -0.01 0.00 0.35 -0.75 -0.20 -0.01
beta[22,10] 0.00 0.00 0.22 -0.46 -0.11 0.00
beta[22,11] 0.00 0.00 0.26 -0.53 -0.13 0.00
beta[22,12] -0.03 0.00 0.30 -0.64 -0.20 -0.04
mu_prior[1] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[2] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[3] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[4] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[5] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[6] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[7] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[8] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[9] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[10] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[11] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu_prior[12] 0.00 0.00 0.05 -0.10 -0.03 0.00
sigma_prior[1] 0.20 0.00 0.10 0.06 0.13 0.18
sigma_prior[2] 0.20 0.00 0.10 0.05 0.13 0.18
sigma_prior[3] 0.20 0.00 0.10 0.05 0.12 0.18
sigma_prior[4] 0.20 0.00 0.10 0.06 0.13 0.18
sigma_prior[5] 0.20 0.00 0.10 0.05 0.13 0.18
sigma_prior[6] 0.20 0.00 0.10 0.05 0.13 0.18
sigma_prior[7] 0.20 0.00 0.10 0.05 0.13 0.18
sigma_prior[8] 0.20 0.00 0.10 0.06 0.13 0.18
sigma_prior[9] 0.20 0.00 0.10 0.05 0.13 0.18
sigma_prior[10] 0.20 0.00 0.10 0.06 0.13 0.18
sigma_prior[11] 0.20 0.00 0.10 0.05 0.13 0.18
sigma_prior[12] 0.20 0.00 0.10 0.06 0.13 0.18
p_prior[1] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[2] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[3] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[4] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[5] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[6] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[7] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[8] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[9] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[10] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[11] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[12] 0.50 0.00 0.43 0.00 0.02 0.48
p_prior[13] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[14] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[15] 0.50 0.00 0.43 0.00 0.02 0.48
p_prior[16] 0.50 0.00 0.43 0.00 0.02 0.48
p_prior[17] 0.50 0.00 0.43 0.00 0.02 0.48
p_prior[18] 0.50 0.00 0.43 0.00 0.02 0.48
p_prior[19] 0.50 0.00 0.43 0.00 0.02 0.48
p_prior[20] 0.50 0.00 0.43 0.00 0.02 0.48
p_prior[21] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[22] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[23] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[24] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[25] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[26] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[27] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[28] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[29] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[30] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[31] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[32] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[33] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[34] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[35] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[36] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[37] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[38] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[39] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[40] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[41] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[42] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[43] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[44] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[45] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[46] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[47] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[48] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[49] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[50] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[51] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[52] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[53] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[54] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[55] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[56] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[57] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[58] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[59] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[60] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[61] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[62] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[63] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[64] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[65] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[66] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[67] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[68] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[69] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[70] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[71] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[72] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[73] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[74] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[75] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[76] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[77] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[78] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[79] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[80] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[81] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[82] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[83] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[84] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[85] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[86] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[87] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[88] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[89] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[90] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[91] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[92] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[93] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[94] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[95] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[96] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[97] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[98] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[99] 0.50 0.00 0.43 0.00 0.01 0.51
p_prior[100] 0.50 0.00 0.43 0.00 0.01 0.51
p_prior[101] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[102] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[103] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[104] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[105] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[106] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[107] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[108] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[109] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[110] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[111] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[112] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[113] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[114] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[115] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[116] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[117] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[118] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[119] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[120] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[121] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[122] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[123] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[124] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[125] 0.50 0.00 0.43 0.00 0.01 0.48
p_prior[126] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[127] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[128] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[129] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[130] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[131] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[132] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[133] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[134] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[135] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[136] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[137] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[138] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[139] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[140] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[141] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[142] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[143] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[144] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[145] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[146] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[147] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[148] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[149] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[150] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[151] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[152] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[153] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[154] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[155] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[156] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[157] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[158] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[159] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[160] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[161] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[162] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[163] 0.50 0.00 0.17 0.17 0.38 0.50
p_prior[164] 0.50 0.00 0.17 0.17 0.38 0.50
p_prior[165] 0.50 0.00 0.17 0.17 0.38 0.50
p_prior[166] 0.50 0.00 0.17 0.17 0.38 0.50
p_prior[167] 0.50 0.00 0.18 0.16 0.38 0.50
p_prior[168] 0.50 0.00 0.18 0.16 0.38 0.50
p_prior[169] 0.50 0.00 0.18 0.16 0.37 0.50
p_prior[170] 0.50 0.00 0.18 0.16 0.37 0.50
p_prior[171] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[172] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[173] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[174] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[175] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[176] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[177] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[178] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[179] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[180] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[181] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[182] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[183] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[184] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[185] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[186] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[187] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[188] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[189] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[190] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[191] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[192] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[193] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[194] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[195] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[196] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[197] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[198] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[199] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[200] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[201] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[202] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[203] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[204] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[205] 0.50 0.00 0.45 0.00 0.01 0.47
p_prior[206] 0.50 0.00 0.45 0.00 0.01 0.47
p_prior[207] 0.50 0.00 0.45 0.00 0.01 0.47
p_prior[208] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[209] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[210] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[211] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[212] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[213] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[214] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[215] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[216] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[217] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[218] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[219] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[220] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[221] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[222] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[223] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[224] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[225] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[226] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[227] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[228] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[229] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[230] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[231] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[232] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[233] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[234] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[235] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[236] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[237] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[238] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[239] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[240] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[241] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[242] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[243] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[244] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[245] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[246] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[247] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[248] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[249] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[250] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[251] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[252] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[253] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[254] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[255] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[256] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[257] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[258] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[259] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[260] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[261] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[262] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[263] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[264] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[265] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[266] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[267] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[268] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[269] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[270] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[271] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[272] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[273] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[274] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[275] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[276] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[277] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[278] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[279] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[280] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[281] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[282] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[283] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[284] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[285] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[286] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[287] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[288] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[289] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[290] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[291] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[292] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[293] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[294] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[295] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[296] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[297] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[298] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[299] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[300] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[301] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[302] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[303] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[304] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[305] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[306] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[307] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[308] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[309] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[310] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[311] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[312] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[313] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[314] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[315] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[316] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[317] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[318] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[319] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[320] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[321] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[322] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[323] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[324] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[325] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[326] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[327] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[328] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[329] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[330] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[331] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[332] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[333] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[334] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[335] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[336] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[337] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[338] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[339] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[340] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[341] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[342] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[343] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[344] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[345] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[346] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[347] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[348] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[349] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[350] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[351] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[352] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[353] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[354] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[355] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[356] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[357] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[358] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[359] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[360] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[361] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[362] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[363] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[364] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[365] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[366] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[367] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[368] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[369] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[370] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[371] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[372] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[373] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[374] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[375] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[376] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[377] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[378] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[379] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[380] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[381] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[382] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[383] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[384] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[385] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[386] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[387] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[388] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[389] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[390] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[391] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[392] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[393] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[394] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[395] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[396] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[397] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[398] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[399] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[400] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[401] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[402] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[403] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[404] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[405] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[406] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[407] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[408] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[409] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[410] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[411] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[412] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[413] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[414] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[415] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[416] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[417] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[418] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[419] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[420] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[421] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[422] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[423] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[424] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[425] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[426] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[427] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[428] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[429] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[430] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[431] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[432] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[433] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[434] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[435] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[436] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[437] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[438] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[439] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[440] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[441] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[442] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[443] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[444] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[445] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[446] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[447] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[448] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[449] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[450] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[451] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[452] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[453] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[454] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[455] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[456] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[457] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[458] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[459] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[460] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[461] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[462] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[463] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[464] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[465] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[466] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[467] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[468] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[469] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[470] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[471] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[472] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[473] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[474] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[475] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[476] 0.50 0.00 0.15 0.22 0.40 0.50
p_prior[477] 0.50 0.00 0.16 0.19 0.39 0.50
p_prior[478] 0.50 0.00 0.17 0.18 0.38 0.50
p_prior[479] 0.50 0.00 0.18 0.15 0.37 0.50
p_prior[480] 0.50 0.00 0.19 0.14 0.37 0.50
p_prior[481] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[482] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[483] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[484] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[485] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[486] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[487] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[488] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[489] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[490] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[491] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[492] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[493] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[494] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[495] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[496] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[497] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[498] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[499] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[500] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[501] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[502] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[503] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[504] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[505] 0.50 0.00 0.15 0.22 0.40 0.50
p_prior[506] 0.50 0.00 0.15 0.22 0.40 0.50
p_prior[507] 0.50 0.00 0.15 0.22 0.40 0.50
p_prior[508] 0.50 0.00 0.16 0.19 0.39 0.50
p_prior[509] 0.50 0.00 0.17 0.18 0.39 0.50
p_prior[510] 0.50 0.00 0.18 0.16 0.38 0.50
p_prior[511] 0.50 0.00 0.18 0.15 0.37 0.50
p_prior[512] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[513] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[514] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[515] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[516] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[517] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[518] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[519] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[520] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[521] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[522] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[523] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[524] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[525] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[526] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[527] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[528] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[529] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[530] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[531] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[532] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[533] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[534] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[535] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[536] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[537] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[538] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[539] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[540] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[541] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[542] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[543] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[544] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[545] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[546] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[547] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[548] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[549] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[550] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[551] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[552] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[553] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[554] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[555] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[556] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[557] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[558] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[559] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[560] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[561] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[562] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[563] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[564] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[565] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[566] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[567] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[568] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[569] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[570] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[571] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[572] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[573] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[574] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[575] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[576] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[577] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[578] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[579] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[580] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[581] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[582] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[583] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[584] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[585] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[586] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[587] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[588] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[589] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[590] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[591] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[592] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[593] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[594] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[595] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[596] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[597] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[598] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[599] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[600] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[601] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[602] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[603] 0.50 0.00 0.13 0.25 0.41 0.50
p_prior[604] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[605] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[606] 0.50 0.00 0.16 0.18 0.39 0.50
p_prior[607] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[608] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[609] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[610] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[611] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[612] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[613] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[614] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[615] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[616] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[617] 0.50 0.00 0.10 0.30 0.44 0.50
p_prior[618] 0.50 0.00 0.10 0.30 0.44 0.50
p_prior[619] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[620] 0.51 0.00 0.44 0.00 0.01 0.53
p_prior[621] 0.51 0.00 0.44 0.00 0.01 0.53
p_prior[622] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[623] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[624] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[625] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[626] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[627] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[628] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[629] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[630] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[631] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[632] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[633] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[634] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[635] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[636] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[637] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[638] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[639] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[640] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[641] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[642] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[643] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[644] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[645] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[646] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[647] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[648] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[649] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[650] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[651] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[652] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[653] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[654] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[655] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[656] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[657] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[658] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[659] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[660] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[661] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[662] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[663] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[664] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[665] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[666] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[667] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[668] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[669] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[670] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[671] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[672] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[673] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[674] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[675] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[676] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[677] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[678] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[679] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[680] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[681] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[682] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[683] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[684] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[685] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[686] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[687] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[688] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[689] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[690] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[691] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[692] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[693] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[694] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[695] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[696] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[697] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[698] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[699] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[700] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[701] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[702] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[703] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[704] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[705] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[706] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[707] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[708] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[709] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[710] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[711] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[712] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[713] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[714] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[715] 0.50 0.00 0.13 0.23 0.41 0.50
p_prior[716] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[717] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[718] 0.50 0.00 0.14 0.23 0.41 0.50
p_prior[719] 0.50 0.00 0.15 0.22 0.40 0.50
p_prior[720] 0.50 0.00 0.16 0.20 0.39 0.50
p_prior[721] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[722] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[723] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[724] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[725] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[726] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[727] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[728] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[729] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[730] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[731] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[732] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[733] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[734] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[735] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[736] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[737] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[738] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[739] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[740] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[741] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[742] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[743] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[744] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[745] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[746] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[747] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[748] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[749] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[750] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[751] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[752] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[753] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[754] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[755] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[756] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[757] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[758] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[759] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[760] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[761] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[762] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[763] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[764] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[765] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[766] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[767] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[768] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[769] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[770] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[771] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[772] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[773] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[774] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[775] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[776] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[777] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[778] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[779] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[780] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[781] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[782] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[783] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[784] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[785] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[786] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[787] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[788] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[789] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[790] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[791] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[792] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[793] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[794] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[795] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[796] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[797] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[798] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[799] 0.50 0.00 0.45 0.00 0.00 0.52
p_prior[800] 0.50 0.00 0.45 0.00 0.00 0.52
p_prior[801] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[802] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[803] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[804] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[805] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[806] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[807] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[808] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[809] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[810] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[811] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[812] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[813] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[814] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[815] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[816] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[817] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[818] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[819] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[820] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[821] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[822] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[823] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[824] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[825] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[826] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[827] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[828] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[829] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[830] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[831] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[832] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[833] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[834] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[835] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[836] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[837] 0.50 0.00 0.18 0.16 0.38 0.50
p_prior[838] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[839] 0.50 0.00 0.18 0.16 0.38 0.50
p_prior[840] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[841] 0.50 0.00 0.18 0.16 0.38 0.50
p_prior[842] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[843] 0.50 0.00 0.18 0.16 0.38 0.50
p_prior[844] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[845] 0.50 0.00 0.19 0.14 0.37 0.50
p_prior[846] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[847] 0.50 0.00 0.19 0.14 0.37 0.50
p_prior[848] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[849] 0.50 0.00 0.19 0.14 0.37 0.50
p_prior[850] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[851] 0.50 0.00 0.19 0.14 0.37 0.50
p_prior[852] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[853] 0.50 0.00 0.20 0.13 0.36 0.50
p_prior[854] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[855] 0.50 0.00 0.20 0.13 0.36 0.50
p_prior[856] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[857] 0.50 0.00 0.21 0.11 0.34 0.50
p_prior[858] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[859] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[860] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[861] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[862] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[863] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[864] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[865] 0.50 0.00 0.12 0.27 0.42 0.50
p_prior[866] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[867] 0.50 0.00 0.12 0.27 0.42 0.50
p_prior[868] 0.50 0.00 0.12 0.27 0.42 0.50
p_prior[869] 0.50 0.00 0.12 0.27 0.43 0.50
p_prior[870] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[871] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[872] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[873] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[874] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[875] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[876] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[877] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[878] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[879] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[880] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[881] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[882] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[883] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[884] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[885] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[886] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[887] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[888] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[889] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[890] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[891] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[892] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[893] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[894] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[895] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[896] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[897] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[898] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[899] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[900] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[901] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[902] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[903] 0.50 0.00 0.15 0.22 0.40 0.50
p_prior[904] 0.50 0.00 0.15 0.21 0.40 0.50
p_prior[905] 0.50 0.00 0.15 0.21 0.40 0.50
p_prior[906] 0.50 0.00 0.16 0.19 0.39 0.50
p_prior[907] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[908] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[909] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[910] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[911] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[912] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[913] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[914] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[915] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[916] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[917] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[918] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[919] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[920] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[921] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[922] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[923] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[924] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[925] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[926] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[927] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[928] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[929] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[930] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[931] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[932] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[933] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[934] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[935] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[936] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[937] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[938] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[939] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[940] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[941] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[942] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[943] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[944] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[945] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[946] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[947] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[948] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[949] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[950] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[951] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[952] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[953] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[954] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[955] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[956] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[957] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[958] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[959] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[960] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[961] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[962] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[963] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[964] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[965] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[966] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[967] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[968] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[969] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[970] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[971] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[972] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[973] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[974] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[975] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[976] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[977] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[978] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[979] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[980] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[981] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[982] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[983] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[984] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[985] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[986] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[987] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[988] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[989] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[990] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[991] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[992] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[993] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[994] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[995] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[996] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[997] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[998] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[999] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[1000] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[1001] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[1002] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[1003] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[1004] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[1005] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1006] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1007] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[1008] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1009] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1010] 0.50 0.00 0.44 0.00 0.01 0.47
p_prior[1011] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1012] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1013] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1014] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1015] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1016] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1017] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1018] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1019] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1020] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1021] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1022] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1023] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1024] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1025] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1026] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1027] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1028] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1029] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1030] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1031] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1032] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1033] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1034] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1035] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1036] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1037] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1038] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[1039] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1040] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1041] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1042] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1043] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1044] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1045] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1046] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1047] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1048] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1049] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1050] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1051] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1052] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1053] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1054] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1055] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1056] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1057] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1058] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1059] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1060] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1061] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1062] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1063] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1064] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1065] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1066] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1067] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1068] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1069] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1070] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1071] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1072] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1073] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1074] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1075] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1076] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1077] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1078] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1079] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1080] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1081] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1082] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1083] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1084] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[1085] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[1086] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[1087] 0.50 0.00 0.15 0.22 0.40 0.50
p_prior[1088] 0.50 0.00 0.15 0.21 0.40 0.50
p_prior[1089] 0.50 0.00 0.16 0.20 0.39 0.50
p_prior[1090] 0.50 0.00 0.16 0.20 0.39 0.50
p_prior[1091] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1092] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1093] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1094] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1095] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1096] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1097] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1098] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1099] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1100] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1101] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1102] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1103] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1104] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1105] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1106] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[1107] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[1108] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1109] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1110] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[1111] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1112] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1113] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1114] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[1115] 0.50 0.00 0.43 0.00 0.02 0.51
p_prior[1116] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[1117] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[1118] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[1119] 0.50 0.00 0.13 0.26 0.42 0.50
p_prior[1120] 0.50 0.00 0.13 0.26 0.42 0.50
p_prior[1121] 0.50 0.00 0.13 0.25 0.41 0.50
p_prior[1122] 0.50 0.00 0.13 0.25 0.41 0.50
p_prior[1123] 0.50 0.00 0.13 0.25 0.42 0.50
p_prior[1124] 0.50 0.00 0.13 0.25 0.41 0.50
p_prior[1125] 0.50 0.00 0.13 0.25 0.42 0.50
p_prior[1126] 0.50 0.00 0.13 0.25 0.41 0.50
p_prior[1127] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1128] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1129] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1130] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1131] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1132] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1133] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[1134] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[1135] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1136] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1137] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1138] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1139] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1140] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1141] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1142] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1143] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1144] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1145] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1146] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1147] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1148] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1149] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1150] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1151] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1152] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1153] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1154] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1155] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1156] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1157] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1158] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1159] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1160] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1161] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1162] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1163] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1164] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1165] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1166] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[1167] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1168] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1169] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[1170] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1171] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1172] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[1173] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1174] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[1175] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[1176] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[1177] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[1178] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[1179] 0.50 0.00 0.19 0.15 0.37 0.50
p_prior[1180] 0.50 0.00 0.19 0.14 0.37 0.50
p_prior[1181] 0.50 0.00 0.19 0.14 0.36 0.50
p_prior[1182] 0.50 0.00 0.19 0.13 0.36 0.50
p_prior[1183] 0.50 0.00 0.19 0.13 0.36 0.50
p_prior[1184] 0.50 0.00 0.20 0.12 0.35 0.50
p_prior[1185] 0.50 0.00 0.21 0.12 0.35 0.50
p_prior[1186] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1187] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1188] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1189] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1190] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1191] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1192] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1193] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1194] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1195] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1196] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1197] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1198] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1199] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1200] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1201] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1202] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1203] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1204] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1205] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1206] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1207] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1208] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1209] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[1210] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[1211] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1212] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1213] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1214] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1215] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1216] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1217] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1218] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1219] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1220] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1221] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1222] 0.50 0.00 0.45 0.00 0.00 0.51
p_prior[1223] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1224] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1225] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1226] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1227] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1228] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1229] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[1230] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[1231] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[1232] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[1233] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1234] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1235] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1236] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1237] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1238] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1239] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1240] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1241] 0.50 0.00 0.10 0.31 0.44 0.50
p_prior[1242] 0.50 0.00 0.10 0.31 0.44 0.50
p_prior[1243] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1244] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1245] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1246] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1247] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1248] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1249] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1250] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1251] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1252] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1253] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1254] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1255] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1256] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1257] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1258] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1259] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1260] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1261] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1262] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1263] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1264] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1265] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1266] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1267] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1268] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1269] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1270] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1271] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1272] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1273] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1274] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1275] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1276] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1277] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1278] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1279] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1280] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1281] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1282] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1283] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1284] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1285] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1286] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1287] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1288] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1289] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1290] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1291] 0.50 0.00 0.43 0.00 0.01 0.48
p_prior[1292] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1293] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1294] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1295] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1296] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1297] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1298] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1299] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1300] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1301] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1302] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1303] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1304] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1305] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1306] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1307] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1308] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1309] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1310] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1311] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1312] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1313] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1314] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1315] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1316] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1317] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1318] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1319] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1320] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1321] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1322] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1323] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1324] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1325] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1326] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1327] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1328] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1329] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1330] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1331] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1332] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1333] 0.50 0.00 0.42 0.00 0.02 0.50
p_prior[1334] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1335] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1336] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1337] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1338] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1339] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1340] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1341] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1342] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1343] 0.50 0.00 0.43 0.00 0.02 0.50
p_predicted[1] 0.22 0.00 0.07 0.10 0.17 0.21
p_predicted[2] 0.22 0.00 0.07 0.10 0.17 0.21
p_predicted[3] 0.22 0.00 0.07 0.11 0.17 0.21
p_predicted[4] 0.19 0.00 0.06 0.09 0.15 0.19
p_predicted[5] 0.19 0.00 0.06 0.09 0.15 0.19
p_predicted[6] 0.19 0.00 0.06 0.09 0.15 0.18
p_predicted[7] 0.18 0.00 0.07 0.07 0.13 0.17
p_predicted[8] 0.18 0.00 0.07 0.07 0.13 0.17
p_predicted[9] 0.38 0.00 0.08 0.23 0.32 0.37
p_predicted[10] 0.39 0.00 0.08 0.25 0.34 0.39
p_predicted[11] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[12] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[13] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[14] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[15] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[16] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted[17] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted[18] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted[19] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted[20] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted[21] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[22] 0.09 0.00 0.06 0.01 0.04 0.07
p_predicted[23] 0.38 0.00 0.04 0.30 0.35 0.38
p_predicted[24] 0.36 0.00 0.04 0.28 0.33 0.36
p_predicted[25] 0.33 0.00 0.05 0.24 0.29 0.33
p_predicted[26] 0.27 0.00 0.04 0.20 0.25 0.27
p_predicted[27] 0.26 0.00 0.04 0.19 0.23 0.26
p_predicted[28] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted[29] 0.23 0.00 0.04 0.15 0.20 0.23
p_predicted[30] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[31] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[32] 0.31 0.00 0.04 0.24 0.28 0.31
p_predicted[33] 0.31 0.00 0.04 0.24 0.28 0.31
p_predicted[34] 0.29 0.00 0.04 0.22 0.26 0.29
p_predicted[35] 0.29 0.00 0.04 0.22 0.26 0.29
p_predicted[36] 0.28 0.00 0.04 0.21 0.25 0.28
p_predicted[37] 0.28 0.00 0.04 0.21 0.25 0.28
p_predicted[38] 0.21 0.00 0.03 0.15 0.18 0.20
p_predicted[39] 0.21 0.00 0.03 0.15 0.18 0.20
p_predicted[40] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted[41] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted[42] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted[43] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted[44] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted[45] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted[46] 0.14 0.00 0.02 0.10 0.12 0.14
p_predicted[47] 0.14 0.00 0.02 0.10 0.12 0.14
p_predicted[48] 0.14 0.00 0.02 0.09 0.12 0.14
p_predicted[49] 0.14 0.00 0.02 0.09 0.12 0.14
p_predicted[50] 0.11 0.00 0.06 0.04 0.07 0.10
p_predicted[51] 0.08 0.00 0.04 0.03 0.06 0.08
p_predicted[52] 0.08 0.00 0.04 0.03 0.06 0.08
p_predicted[53] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted[54] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[55] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[56] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[57] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted[58] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted[59] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted[60] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted[61] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[62] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[63] 0.04 0.00 0.02 0.01 0.03 0.03
p_predicted[64] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[65] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted[66] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[67] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[68] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[69] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[70] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[71] 0.20 0.00 0.05 0.11 0.16 0.20
p_predicted[72] 0.19 0.00 0.05 0.11 0.16 0.19
p_predicted[73] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[74] 0.18 0.00 0.05 0.10 0.14 0.17
p_predicted[75] 0.13 0.00 0.05 0.05 0.09 0.12
p_predicted[76] 0.64 0.00 0.11 0.42 0.57 0.65
p_predicted[77] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[78] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[79] 0.64 0.00 0.11 0.42 0.57 0.65
p_predicted[80] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[81] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[82] 0.64 0.00 0.11 0.42 0.57 0.65
p_predicted[83] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[84] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[85] 0.56 0.00 0.08 0.39 0.51 0.57
p_predicted[86] 0.38 0.00 0.06 0.26 0.34 0.38
p_predicted[87] 0.32 0.00 0.06 0.20 0.27 0.31
p_predicted[88] 0.16 0.00 0.03 0.10 0.13 0.15
p_predicted[89] 0.15 0.00 0.03 0.10 0.13 0.14
p_predicted[90] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[91] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[92] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[93] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[94] 0.62 0.00 0.13 0.36 0.54 0.63
p_predicted[95] 0.62 0.00 0.12 0.37 0.54 0.63
p_predicted[96] 0.58 0.00 0.12 0.35 0.50 0.59
p_predicted[97] 0.36 0.00 0.13 0.14 0.26 0.35
p_predicted[98] 0.35 0.00 0.13 0.13 0.26 0.35
p_predicted[99] 0.20 0.00 0.07 0.09 0.16 0.20
p_predicted[100] 0.20 0.00 0.07 0.09 0.16 0.20
p_predicted[101] 0.26 0.00 0.05 0.17 0.22 0.25
p_predicted[102] 0.22 0.00 0.04 0.14 0.19 0.21
p_predicted[103] 0.18 0.00 0.03 0.11 0.15 0.17
p_predicted[104] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted[105] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[106] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[107] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[108] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[109] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[110] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[111] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[112] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[113] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[114] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[115] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[116] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[117] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[118] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[119] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[120] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[121] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[122] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[123] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[124] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[125] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[126] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[127] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[128] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted[129] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted[130] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted[131] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[132] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[133] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[134] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[135] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[136] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted[137] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted[138] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted[139] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted[140] 0.27 0.00 0.05 0.18 0.24 0.27
p_predicted[141] 0.25 0.00 0.05 0.16 0.21 0.25
p_predicted[142] 0.21 0.00 0.04 0.13 0.18 0.21
p_predicted[143] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[144] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted[145] 0.20 0.00 0.04 0.13 0.17 0.19
p_predicted[146] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[147] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[148] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted[149] 0.14 0.00 0.04 0.07 0.11 0.14
p_predicted[150] 0.09 0.00 0.04 0.03 0.06 0.08
p_predicted[151] 0.12 0.00 0.04 0.05 0.09 0.11
p_predicted[152] 0.12 0.00 0.04 0.05 0.09 0.11
p_predicted[153] 0.08 0.00 0.03 0.04 0.06 0.07
p_predicted[154] 0.08 0.00 0.03 0.04 0.06 0.08
p_predicted[155] 0.09 0.00 0.03 0.05 0.07 0.09
p_predicted[156] 0.09 0.00 0.03 0.04 0.07 0.09
p_predicted[157] 0.08 0.00 0.03 0.04 0.06 0.07
p_predicted[158] 0.09 0.00 0.03 0.04 0.07 0.08
p_predicted[159] 0.07 0.00 0.02 0.03 0.05 0.07
p_predicted[160] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[161] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted[162] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted[163] 0.13 0.00 0.04 0.07 0.10 0.13
p_predicted[164] 0.13 0.00 0.04 0.07 0.10 0.13
p_predicted[165] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted[166] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted[167] 0.07 0.00 0.02 0.04 0.06 0.07
p_predicted[168] 0.07 0.00 0.02 0.04 0.06 0.07
p_predicted[169] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted[170] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted[171] 0.38 0.00 0.04 0.30 0.35 0.38
p_predicted[172] 0.35 0.00 0.04 0.27 0.32 0.35
p_predicted[173] 0.26 0.00 0.04 0.19 0.23 0.26
p_predicted[174] 0.12 0.00 0.05 0.05 0.09 0.12
p_predicted[175] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted[176] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted[177] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted[178] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[179] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted[180] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[181] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[182] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[183] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[184] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[185] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[186] 0.03 0.00 0.02 0.01 0.02 0.02
p_predicted[187] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[188] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[189] 0.10 0.00 0.04 0.04 0.08 0.10
p_predicted[190] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted[191] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted[192] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted[193] 0.08 0.00 0.03 0.03 0.06 0.07
p_predicted[194] 0.07 0.00 0.03 0.03 0.06 0.07
p_predicted[195] 0.07 0.00 0.03 0.03 0.06 0.07
p_predicted[196] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[197] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[198] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[199] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[200] 0.10 0.00 0.04 0.04 0.08 0.10
p_predicted[201] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted[202] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[203] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[204] 0.07 0.00 0.03 0.03 0.06 0.07
p_predicted[205] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted[206] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted[207] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted[208] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted[209] 0.13 0.00 0.03 0.07 0.10 0.12
p_predicted[210] 0.14 0.00 0.03 0.08 0.11 0.13
p_predicted[211] 0.14 0.00 0.03 0.08 0.11 0.13
p_predicted[212] 0.11 0.00 0.03 0.06 0.09 0.10
p_predicted[213] 0.09 0.00 0.02 0.05 0.07 0.08
p_predicted[214] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted[215] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted[216] 0.58 0.00 0.12 0.34 0.50 0.59
p_predicted[217] 0.60 0.00 0.10 0.40 0.53 0.60
p_predicted[218] 0.59 0.00 0.10 0.39 0.52 0.60
p_predicted[219] 0.35 0.00 0.13 0.13 0.25 0.34
p_predicted[220] 0.34 0.00 0.13 0.12 0.24 0.33
p_predicted[221] 0.60 0.00 0.10 0.40 0.53 0.60
p_predicted[222] 0.59 0.00 0.10 0.39 0.52 0.59
p_predicted[223] 0.34 0.00 0.13 0.13 0.25 0.34
p_predicted[224] 0.34 0.00 0.13 0.12 0.24 0.33
p_predicted[225] 0.34 0.00 0.13 0.12 0.24 0.33
p_predicted[226] 0.28 0.00 0.11 0.10 0.19 0.26
p_predicted[227] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted[228] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted[229] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted[230] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted[231] 0.09 0.00 0.04 0.03 0.05 0.08
p_predicted[232] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[233] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[234] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted[235] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted[236] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted[237] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted[238] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted[239] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted[240] 0.21 0.00 0.14 0.03 0.10 0.18
p_predicted[241] 0.20 0.00 0.13 0.03 0.10 0.16
p_predicted[242] 0.20 0.00 0.13 0.03 0.10 0.16
p_predicted[243] 0.18 0.00 0.12 0.02 0.08 0.14
p_predicted[244] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted[245] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted[246] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted[247] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted[248] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted[249] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted[250] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted[251] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted[252] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted[253] 0.46 0.00 0.06 0.36 0.42 0.46
p_predicted[254] 0.46 0.00 0.06 0.36 0.42 0.46
p_predicted[255] 0.46 0.00 0.06 0.36 0.42 0.46
p_predicted[256] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted[257] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted[258] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted[259] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted[260] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted[261] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted[262] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted[263] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted[264] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted[265] 0.35 0.00 0.06 0.24 0.31 0.35
p_predicted[266] 0.35 0.00 0.06 0.24 0.31 0.35
p_predicted[267] 0.35 0.00 0.06 0.24 0.31 0.35
p_predicted[268] 0.34 0.00 0.06 0.24 0.30 0.34
p_predicted[269] 0.34 0.00 0.06 0.24 0.30 0.34
p_predicted[270] 0.34 0.00 0.06 0.24 0.30 0.34
p_predicted[271] 0.20 0.00 0.03 0.15 0.18 0.20
p_predicted[272] 0.20 0.00 0.03 0.15 0.18 0.20
p_predicted[273] 0.20 0.00 0.03 0.15 0.18 0.20
p_predicted[274] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted[275] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted[276] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted[277] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[278] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[279] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[280] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[281] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[282] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[283] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[284] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[285] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[286] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[287] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[288] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[289] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[290] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[291] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[292] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[293] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[294] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[295] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[296] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[297] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[298] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[299] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[300] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[301] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[302] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[303] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[304] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[305] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[306] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[307] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[308] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[309] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[310] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[311] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[312] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[313] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[314] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[315] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[316] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[317] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[318] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[319] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[320] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[321] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[322] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[323] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[324] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[325] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted[326] 0.13 0.00 0.04 0.07 0.10 0.13
p_predicted[327] 0.15 0.00 0.05 0.07 0.11 0.14
p_predicted[328] 0.09 0.00 0.03 0.04 0.06 0.08
p_predicted[329] 0.09 0.00 0.03 0.04 0.07 0.09
p_predicted[330] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted[331] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[332] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[333] 0.06 0.00 0.02 0.02 0.04 0.06
p_predicted[334] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[335] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted[336] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[337] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[338] 0.28 0.00 0.15 0.05 0.16 0.26
p_predicted[339] 0.26 0.00 0.14 0.05 0.15 0.24
p_predicted[340] 0.25 0.00 0.13 0.05 0.15 0.23
p_predicted[341] 0.25 0.00 0.13 0.05 0.15 0.23
p_predicted[342] 0.24 0.00 0.13 0.05 0.15 0.22
p_predicted[343] 0.24 0.00 0.13 0.05 0.14 0.22
p_predicted[344] 0.24 0.00 0.13 0.05 0.14 0.22
p_predicted[345] 0.27 0.00 0.14 0.05 0.16 0.24
p_predicted[346] 0.42 0.00 0.07 0.29 0.37 0.42
p_predicted[347] 0.42 0.00 0.07 0.29 0.37 0.42
p_predicted[348] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted[349] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted[350] 0.36 0.00 0.05 0.27 0.33 0.36
p_predicted[351] 0.36 0.00 0.05 0.27 0.33 0.36
p_predicted[352] 0.27 0.00 0.05 0.19 0.24 0.27
p_predicted[353] 0.27 0.00 0.05 0.19 0.24 0.27
p_predicted[354] 0.21 0.00 0.09 0.07 0.14 0.20
p_predicted[355] 0.21 0.00 0.11 0.06 0.13 0.19
p_predicted[356] 0.26 0.00 0.09 0.11 0.19 0.25
p_predicted[357] 0.26 0.00 0.11 0.09 0.18 0.25
p_predicted[358] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[359] 0.21 0.00 0.07 0.10 0.16 0.21
p_predicted[360] 0.19 0.00 0.06 0.09 0.14 0.18
p_predicted[361] 0.19 0.00 0.07 0.08 0.14 0.18
p_predicted[362] 0.14 0.00 0.05 0.05 0.10 0.13
p_predicted[363] 0.13 0.00 0.06 0.05 0.09 0.13
p_predicted[364] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted[365] 0.13 0.00 0.06 0.05 0.09 0.13
p_predicted[366] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted[367] 0.13 0.00 0.06 0.05 0.09 0.13
p_predicted[368] 0.37 0.00 0.05 0.27 0.33 0.37
p_predicted[369] 0.37 0.00 0.05 0.27 0.33 0.37
p_predicted[370] 0.37 0.00 0.05 0.27 0.33 0.37
p_predicted[371] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[372] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[373] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[374] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[375] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[376] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[377] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[378] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[379] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[380] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[381] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[382] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[383] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[384] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[385] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[386] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[387] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[388] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[389] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[390] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[391] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[392] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[393] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[394] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[395] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[396] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[397] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[398] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[399] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[400] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[401] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[402] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[403] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[404] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[405] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[406] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[407] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[408] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[409] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[410] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[411] 0.03 0.00 0.02 0.00 0.01 0.03
p_predicted[412] 0.03 0.00 0.02 0.00 0.01 0.03
p_predicted[413] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[414] 0.32 0.00 0.09 0.15 0.26 0.32
p_predicted[415] 0.28 0.00 0.05 0.19 0.24 0.27
p_predicted[416] 0.27 0.00 0.05 0.18 0.23 0.26
p_predicted[417] 0.26 0.00 0.04 0.18 0.23 0.26
p_predicted[418] 0.27 0.00 0.05 0.18 0.23 0.26
p_predicted[419] 0.27 0.00 0.05 0.19 0.24 0.27
p_predicted[420] 0.23 0.00 0.05 0.14 0.19 0.23
p_predicted[421] 0.25 0.00 0.04 0.18 0.22 0.25
p_predicted[422] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[423] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted[424] 0.20 0.00 0.04 0.13 0.18 0.20
p_predicted[425] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[426] 0.15 0.00 0.03 0.09 0.12 0.14
p_predicted[427] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[428] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[429] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted[430] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted[431] 0.07 0.00 0.02 0.04 0.06 0.07
p_predicted[432] 0.05 0.00 0.02 0.03 0.04 0.05
p_predicted[433] 0.37 0.00 0.07 0.24 0.32 0.36
p_predicted[434] 0.32 0.00 0.07 0.21 0.28 0.32
p_predicted[435] 0.26 0.00 0.06 0.15 0.22 0.25
p_predicted[436] 0.26 0.00 0.06 0.15 0.21 0.25
p_predicted[437] 0.29 0.00 0.07 0.17 0.24 0.29
p_predicted[438] 0.17 0.00 0.06 0.06 0.12 0.16
p_predicted[439] 0.15 0.00 0.07 0.04 0.10 0.14
p_predicted[440] 0.15 0.00 0.07 0.04 0.09 0.14
p_predicted[441] 0.13 0.00 0.07 0.03 0.08 0.12
p_predicted[442] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted[443] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted[444] 0.29 0.00 0.05 0.20 0.25 0.29
p_predicted[445] 0.30 0.00 0.05 0.20 0.26 0.29
p_predicted[446] 0.29 0.00 0.05 0.19 0.25 0.29
p_predicted[447] 0.26 0.00 0.05 0.17 0.23 0.26
p_predicted[448] 0.28 0.00 0.05 0.18 0.24 0.27
p_predicted[449] 0.23 0.00 0.05 0.14 0.20 0.23
p_predicted[450] 0.22 0.00 0.04 0.14 0.19 0.22
p_predicted[451] 0.25 0.00 0.05 0.16 0.22 0.25
p_predicted[452] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[453] 0.21 0.00 0.04 0.13 0.18 0.21
p_predicted[454] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[455] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted[456] 0.16 0.00 0.07 0.05 0.11 0.16
p_predicted[457] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted[458] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted[459] 0.12 0.00 0.05 0.04 0.08 0.12
p_predicted[460] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted[461] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted[462] 0.28 0.00 0.05 0.18 0.24 0.28
p_predicted[463] 0.26 0.00 0.05 0.17 0.22 0.25
p_predicted[464] 0.25 0.00 0.05 0.16 0.21 0.25
p_predicted[465] 0.24 0.00 0.05 0.15 0.20 0.24
p_predicted[466] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[467] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[468] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted[469] 0.22 0.00 0.07 0.10 0.17 0.22
p_predicted[470] 0.28 0.00 0.05 0.18 0.24 0.27
p_predicted[471] 0.24 0.00 0.05 0.16 0.21 0.24
p_predicted[472] 0.25 0.00 0.05 0.17 0.22 0.25
p_predicted[473] 0.20 0.00 0.04 0.12 0.17 0.19
p_predicted[474] 0.21 0.00 0.04 0.13 0.18 0.20
p_predicted[475] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[476] 0.20 0.00 0.04 0.12 0.17 0.19
p_predicted[477] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted[478] 0.10 0.00 0.03 0.05 0.07 0.09
p_predicted[479] 0.08 0.00 0.03 0.03 0.05 0.07
p_predicted[480] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted[481] 0.21 0.00 0.06 0.09 0.16 0.21
p_predicted[482] 0.26 0.00 0.04 0.18 0.23 0.25
p_predicted[483] 0.26 0.00 0.04 0.19 0.23 0.26
p_predicted[484] 0.17 0.00 0.03 0.12 0.16 0.17
p_predicted[485] 0.17 0.00 0.03 0.12 0.15 0.17
p_predicted[486] 0.16 0.00 0.03 0.12 0.14 0.16
p_predicted[487] 0.16 0.00 0.03 0.12 0.15 0.16
p_predicted[488] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted[489] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted[490] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted[491] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted[492] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted[493] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted[494] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted[495] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted[496] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted[497] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[498] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[499] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[500] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[501] 0.22 0.00 0.07 0.10 0.17 0.22
p_predicted[502] 0.28 0.00 0.05 0.19 0.24 0.28
p_predicted[503] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[504] 0.20 0.00 0.04 0.12 0.17 0.20
p_predicted[505] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted[506] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[507] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[508] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted[509] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted[510] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted[511] 0.08 0.00 0.03 0.02 0.05 0.07
p_predicted[512] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[513] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[514] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[515] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[516] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[517] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[518] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[519] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[520] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[521] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[522] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[523] 0.18 0.00 0.08 0.05 0.12 0.17
p_predicted[524] 0.17 0.00 0.09 0.04 0.10 0.15
p_predicted[525] 0.49 0.00 0.06 0.37 0.45 0.49
p_predicted[526] 0.49 0.00 0.06 0.37 0.45 0.49
p_predicted[527] 0.49 0.00 0.06 0.37 0.45 0.49
p_predicted[528] 0.46 0.00 0.06 0.34 0.42 0.46
p_predicted[529] 0.46 0.00 0.06 0.34 0.42 0.46
p_predicted[530] 0.46 0.00 0.06 0.34 0.42 0.46
p_predicted[531] 0.30 0.00 0.04 0.23 0.28 0.30
p_predicted[532] 0.30 0.00 0.04 0.23 0.28 0.30
p_predicted[533] 0.30 0.00 0.04 0.23 0.28 0.30
p_predicted[534] 0.30 0.00 0.04 0.22 0.27 0.29
p_predicted[535] 0.30 0.00 0.04 0.22 0.27 0.29
p_predicted[536] 0.30 0.00 0.04 0.22 0.27 0.29
p_predicted[537] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted[538] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted[539] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted[540] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted[541] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted[542] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted[543] 0.36 0.00 0.05 0.26 0.32 0.36
p_predicted[544] 0.36 0.00 0.05 0.26 0.32 0.36
p_predicted[545] 0.36 0.00 0.05 0.26 0.32 0.36
p_predicted[546] 0.21 0.00 0.04 0.14 0.18 0.20
p_predicted[547] 0.21 0.00 0.04 0.14 0.18 0.20
p_predicted[548] 0.21 0.00 0.04 0.14 0.18 0.20
p_predicted[549] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[550] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[551] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted[552] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[553] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[554] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[555] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[556] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[557] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[558] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[559] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[560] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[561] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[562] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[563] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[564] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[565] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[566] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[567] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[568] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[569] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[570] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[571] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[572] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[573] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[574] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[575] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[576] 0.15 0.00 0.07 0.05 0.11 0.15
p_predicted[577] 0.08 0.00 0.04 0.02 0.05 0.07
p_predicted[578] 0.07 0.00 0.04 0.02 0.04 0.07
p_predicted[579] 0.08 0.00 0.05 0.02 0.04 0.07
p_predicted[580] 0.08 0.00 0.05 0.02 0.05 0.07
p_predicted[581] 0.06 0.00 0.04 0.01 0.04 0.05
p_predicted[582] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted[583] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted[584] 0.25 0.00 0.05 0.15 0.21 0.24
p_predicted[585] 0.21 0.00 0.04 0.13 0.18 0.20
p_predicted[586] 0.19 0.00 0.04 0.12 0.16 0.18
p_predicted[587] 0.12 0.00 0.06 0.03 0.07 0.11
p_predicted[588] 0.12 0.00 0.06 0.03 0.07 0.11
p_predicted[589] 0.15 0.00 0.07 0.05 0.10 0.15
p_predicted[590] 0.15 0.00 0.07 0.05 0.10 0.14
p_predicted[591] 0.16 0.00 0.07 0.05 0.11 0.15
p_predicted[592] 0.15 0.00 0.07 0.05 0.11 0.14
p_predicted[593] 0.16 0.00 0.07 0.05 0.11 0.15
p_predicted[594] 0.15 0.00 0.07 0.05 0.11 0.14
p_predicted[595] 0.11 0.00 0.06 0.03 0.07 0.10
p_predicted[596] 0.11 0.00 0.06 0.03 0.07 0.10
p_predicted[597] 0.30 0.00 0.08 0.14 0.24 0.30
p_predicted[598] 0.36 0.00 0.05 0.27 0.33 0.36
p_predicted[599] 0.35 0.00 0.05 0.25 0.31 0.35
p_predicted[600] 0.34 0.00 0.05 0.24 0.30 0.34
p_predicted[601] 0.27 0.00 0.05 0.18 0.24 0.27
p_predicted[602] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted[603] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted[604] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted[605] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted[606] 0.09 0.00 0.04 0.03 0.06 0.08
p_predicted[607] 0.57 0.00 0.16 0.26 0.45 0.57
p_predicted[608] 0.57 0.00 0.16 0.26 0.45 0.57
p_predicted[609] 0.54 0.00 0.15 0.25 0.43 0.54
p_predicted[610] 0.54 0.00 0.15 0.25 0.43 0.54
p_predicted[611] 0.53 0.00 0.15 0.25 0.43 0.53
p_predicted[612] 0.53 0.00 0.15 0.25 0.43 0.53
p_predicted[613] 0.56 0.00 0.15 0.27 0.46 0.56
p_predicted[614] 0.56 0.00 0.15 0.27 0.46 0.56
p_predicted[615] 0.66 0.00 0.15 0.33 0.56 0.67
p_predicted[616] 0.66 0.00 0.15 0.33 0.56 0.67
p_predicted[617] 0.20 0.00 0.03 0.13 0.17 0.20
p_predicted[618] 0.20 0.00 0.03 0.13 0.17 0.20
p_predicted[619] 0.18 0.00 0.04 0.11 0.16 0.18
p_predicted[620] 0.05 0.00 0.11 0.00 0.00 0.00
p_predicted[621] 0.05 0.00 0.11 0.00 0.00 0.00
p_predicted[622] 0.09 0.00 0.06 0.02 0.05 0.07
p_predicted[623] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted[624] 0.08 0.00 0.05 0.02 0.05 0.07
p_predicted[625] 0.06 0.00 0.04 0.01 0.04 0.05
p_predicted[626] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[627] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[628] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[629] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[630] 0.31 0.00 0.09 0.15 0.25 0.30
p_predicted[631] 0.38 0.00 0.06 0.27 0.34 0.37
p_predicted[632] 0.31 0.00 0.06 0.20 0.26 0.30
p_predicted[633] 0.30 0.00 0.06 0.20 0.26 0.30
p_predicted[634] 0.30 0.00 0.06 0.19 0.26 0.30
p_predicted[635] 0.14 0.00 0.04 0.08 0.12 0.14
p_predicted[636] 0.21 0.00 0.08 0.08 0.15 0.20
p_predicted[637] 0.21 0.00 0.10 0.06 0.14 0.20
p_predicted[638] 0.26 0.00 0.08 0.13 0.21 0.26
p_predicted[639] 0.26 0.00 0.10 0.11 0.19 0.25
p_predicted[640] 0.26 0.00 0.07 0.13 0.21 0.25
p_predicted[641] 0.26 0.00 0.09 0.11 0.19 0.25
p_predicted[642] 0.26 0.00 0.07 0.13 0.21 0.25
p_predicted[643] 0.26 0.00 0.09 0.11 0.19 0.25
p_predicted[644] 0.19 0.00 0.07 0.08 0.14 0.18
p_predicted[645] 0.19 0.00 0.08 0.06 0.13 0.18
p_predicted[646] 0.19 0.00 0.07 0.07 0.14 0.18
p_predicted[647] 0.19 0.00 0.09 0.06 0.13 0.18
p_predicted[648] 0.19 0.00 0.07 0.08 0.14 0.18
p_predicted[649] 0.19 0.00 0.08 0.06 0.13 0.18
p_predicted[650] 0.27 0.00 0.11 0.11 0.20 0.26
p_predicted[651] 0.27 0.00 0.11 0.11 0.20 0.26
p_predicted[652] 0.27 0.00 0.10 0.10 0.19 0.26
p_predicted[653] 0.27 0.00 0.10 0.10 0.19 0.26
p_predicted[654] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[655] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[656] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[657] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[658] 0.06 0.00 0.04 0.01 0.04 0.06
p_predicted[659] 0.06 0.00 0.04 0.01 0.04 0.06
p_predicted[660] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[661] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[662] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[663] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[664] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[665] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[666] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[667] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[668] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[669] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[670] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[671] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[672] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[673] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[674] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[675] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[676] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[677] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[678] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[679] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[680] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[681] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[682] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[683] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[684] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[685] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[686] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[687] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[688] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[689] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[690] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[691] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[692] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[693] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted[694] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[695] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[696] 0.05 0.00 0.03 0.02 0.03 0.05
p_predicted[697] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[698] 0.08 0.00 0.03 0.03 0.05 0.07
p_predicted[699] 0.05 0.00 0.03 0.02 0.03 0.05
p_predicted[700] 0.08 0.00 0.03 0.03 0.05 0.07
p_predicted[701] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[702] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted[703] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[704] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[705] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted[706] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[707] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[708] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted[709] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[710] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted[711] 0.39 0.00 0.06 0.27 0.35 0.39
p_predicted[712] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted[713] 0.25 0.00 0.05 0.17 0.22 0.25
p_predicted[714] 0.25 0.00 0.05 0.17 0.22 0.25
p_predicted[715] 0.25 0.00 0.04 0.16 0.21 0.24
p_predicted[716] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted[717] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[718] 0.18 0.00 0.04 0.12 0.16 0.18
p_predicted[719] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted[720] 0.09 0.00 0.03 0.05 0.07 0.09
p_predicted[721] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[722] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[723] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[724] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[725] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[726] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[727] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[728] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[729] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[730] 0.33 0.00 0.11 0.14 0.24 0.32
p_predicted[731] 0.33 0.00 0.11 0.14 0.24 0.32
p_predicted[732] 0.24 0.00 0.10 0.08 0.17 0.23
p_predicted[733] 0.52 0.00 0.13 0.27 0.43 0.52
p_predicted[734] 0.52 0.00 0.13 0.27 0.43 0.52
p_predicted[735] 0.50 0.00 0.12 0.27 0.42 0.50
p_predicted[736] 0.43 0.00 0.12 0.20 0.34 0.42
p_predicted[737] 0.43 0.00 0.12 0.20 0.34 0.42
p_predicted[738] 0.42 0.00 0.12 0.19 0.33 0.42
p_predicted[739] 0.41 0.00 0.13 0.18 0.32 0.41
p_predicted[740] 0.41 0.00 0.13 0.17 0.31 0.40
p_predicted[741] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted[742] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted[743] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[744] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[745] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[746] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[747] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[748] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[749] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[750] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[751] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[752] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[753] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[754] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[755] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[756] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[757] 0.02 0.00 0.06 0.00 0.00 0.00
p_predicted[758] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[759] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[760] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[761] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[762] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[763] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[764] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[765] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[766] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[767] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[768] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[769] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[770] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[771] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[772] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[773] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[774] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[775] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[776] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[777] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[778] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[779] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[780] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[781] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[782] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[783] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[784] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[785] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[786] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[787] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[788] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[789] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[790] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[791] 0.35 0.00 0.05 0.26 0.32 0.35
p_predicted[792] 0.34 0.00 0.05 0.25 0.31 0.34
p_predicted[793] 0.34 0.00 0.05 0.24 0.30 0.34
p_predicted[794] 0.35 0.00 0.05 0.25 0.31 0.35
p_predicted[795] 0.29 0.00 0.05 0.20 0.26 0.29
p_predicted[796] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted[797] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted[798] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted[799] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted[800] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted[801] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted[802] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted[803] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted[804] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted[805] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[806] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[807] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[808] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[809] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[810] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[811] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[812] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[813] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[814] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[815] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[816] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[817] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[818] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted[819] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[820] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[821] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[822] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[823] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[824] 0.05 0.00 0.03 0.01 0.03 0.05
p_predicted[825] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[826] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[827] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[828] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[829] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[830] 0.06 0.00 0.05 0.01 0.03 0.04
p_predicted[831] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted[832] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[833] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[834] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[835] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[836] 0.15 0.00 0.05 0.06 0.11 0.15
p_predicted[837] 0.15 0.00 0.06 0.06 0.11 0.15
p_predicted[838] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[839] 0.19 0.00 0.05 0.11 0.16 0.19
p_predicted[840] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[841] 0.20 0.00 0.05 0.11 0.16 0.19
p_predicted[842] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[843] 0.19 0.00 0.05 0.11 0.16 0.19
p_predicted[844] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted[845] 0.11 0.00 0.04 0.06 0.09 0.11
p_predicted[846] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted[847] 0.11 0.00 0.04 0.06 0.09 0.11
p_predicted[848] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted[849] 0.11 0.00 0.04 0.05 0.08 0.10
p_predicted[850] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted[851] 0.08 0.00 0.03 0.04 0.06 0.08
p_predicted[852] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted[853] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted[854] 0.06 0.00 0.02 0.03 0.04 0.06
p_predicted[855] 0.06 0.00 0.02 0.02 0.04 0.06
p_predicted[856] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted[857] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted[858] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[859] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted[860] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[861] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[862] 0.15 0.00 0.04 0.09 0.13 0.15
p_predicted[863] 0.15 0.00 0.04 0.09 0.12 0.14
p_predicted[864] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted[865] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[866] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted[867] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted[868] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[869] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[870] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[871] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[872] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[873] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[874] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[875] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[876] 0.29 0.00 0.08 0.16 0.24 0.28
p_predicted[877] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[878] 0.16 0.00 0.05 0.07 0.12 0.15
p_predicted[879] 0.15 0.00 0.05 0.07 0.11 0.14
p_predicted[880] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[881] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[882] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[883] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[884] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[885] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[886] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[887] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[888] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[889] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[890] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[891] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[892] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[893] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[894] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[895] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[896] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[897] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[898] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[899] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[900] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[901] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted[902] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[903] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted[904] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[905] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted[906] 0.09 0.00 0.03 0.04 0.07 0.08
p_predicted[907] 0.27 0.00 0.05 0.18 0.24 0.27
p_predicted[908] 0.25 0.00 0.05 0.16 0.21 0.24
p_predicted[909] 0.20 0.00 0.04 0.13 0.18 0.20
p_predicted[910] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[911] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[912] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[913] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[914] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[915] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[916] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[917] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[918] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[919] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[920] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[921] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[922] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[923] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[924] 0.34 0.00 0.09 0.16 0.28 0.34
p_predicted[925] 0.39 0.00 0.06 0.27 0.34 0.39
p_predicted[926] 0.39 0.00 0.06 0.27 0.34 0.38
p_predicted[927] 0.39 0.00 0.06 0.27 0.35 0.39
p_predicted[928] 0.11 0.00 0.04 0.04 0.07 0.10
p_predicted[929] 0.13 0.00 0.04 0.07 0.11 0.13
p_predicted[930] 0.08 0.00 0.02 0.04 0.06 0.07
p_predicted[931] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[932] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[933] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[934] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[935] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[936] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[937] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[938] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[939] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[940] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[941] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[942] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[943] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[944] 0.51 0.00 0.07 0.38 0.46 0.51
p_predicted[945] 0.49 0.00 0.06 0.36 0.44 0.49
p_predicted[946] 0.42 0.00 0.07 0.28 0.37 0.42
p_predicted[947] 0.22 0.00 0.04 0.15 0.20 0.22
p_predicted[948] 0.22 0.00 0.04 0.16 0.20 0.22
p_predicted[949] 0.22 0.00 0.04 0.15 0.19 0.21
p_predicted[950] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted[951] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted[952] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted[953] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[954] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[955] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[956] 0.18 0.00 0.05 0.09 0.15 0.18
p_predicted[957] 0.18 0.00 0.05 0.09 0.15 0.18
p_predicted[958] 0.18 0.00 0.05 0.09 0.15 0.18
p_predicted[959] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[960] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[961] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[962] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted[963] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted[964] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted[965] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted[966] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted[967] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted[968] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted[969] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted[970] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted[971] 0.17 0.00 0.05 0.08 0.13 0.17
p_predicted[972] 0.17 0.00 0.05 0.08 0.13 0.17
p_predicted[973] 0.17 0.00 0.05 0.08 0.13 0.17
p_predicted[974] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[975] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[976] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[977] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[978] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[979] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[980] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[981] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[982] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[983] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[984] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[985] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[986] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[987] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[988] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[989] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[990] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[991] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[992] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[993] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[994] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[995] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[996] 0.93 0.00 0.10 0.62 0.91 0.97
p_predicted[997] 0.93 0.00 0.09 0.66 0.91 0.97
p_predicted[998] 0.94 0.00 0.09 0.67 0.93 0.98
p_predicted[999] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1000] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1001] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1002] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1003] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1004] 0.14 0.00 0.08 0.03 0.09 0.13
p_predicted[1005] 0.17 0.00 0.09 0.04 0.10 0.15
p_predicted[1006] 0.17 0.00 0.09 0.04 0.10 0.15
p_predicted[1007] 0.13 0.00 0.07 0.04 0.08 0.12
p_predicted[1008] 0.14 0.00 0.08 0.04 0.08 0.12
p_predicted[1009] 0.29 0.00 0.13 0.10 0.20 0.28
p_predicted[1010] 0.33 0.00 0.12 0.13 0.24 0.31
p_predicted[1011] 0.36 0.00 0.15 0.12 0.25 0.35
p_predicted[1012] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[1013] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1014] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1015] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted[1016] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[1017] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1018] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1019] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1020] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1021] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1022] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1023] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1024] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1025] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1026] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1027] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted[1028] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[1029] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1030] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1031] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1032] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1033] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1034] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1035] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1036] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1037] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1038] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1039] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1040] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1041] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1042] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1043] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1044] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1045] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1046] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1047] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1048] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1049] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1050] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1051] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[1052] 0.10 0.00 0.06 0.02 0.05 0.09
p_predicted[1053] 0.10 0.00 0.06 0.02 0.05 0.09
p_predicted[1054] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1055] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1056] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1057] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1058] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted[1059] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted[1060] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted[1061] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted[1062] 0.10 0.00 0.06 0.02 0.05 0.09
p_predicted[1063] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1064] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1065] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1066] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted[1067] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1068] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted[1069] 0.09 0.00 0.06 0.01 0.04 0.07
p_predicted[1070] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted[1071] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1072] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1073] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1074] 0.14 0.00 0.04 0.06 0.11 0.13
p_predicted[1075] 0.14 0.00 0.04 0.06 0.11 0.13
p_predicted[1076] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[1077] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[1078] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1079] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1080] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1081] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1082] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1083] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1084] 0.14 0.00 0.05 0.05 0.10 0.14
p_predicted[1085] 0.18 0.00 0.04 0.10 0.15 0.17
p_predicted[1086] 0.13 0.00 0.03 0.07 0.10 0.12
p_predicted[1087] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted[1088] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[1089] 0.10 0.00 0.03 0.05 0.08 0.09
p_predicted[1090] 0.09 0.00 0.03 0.05 0.08 0.09
p_predicted[1091] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted[1092] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted[1093] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted[1094] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted[1095] 0.18 0.00 0.06 0.08 0.13 0.17
p_predicted[1096] 0.18 0.00 0.06 0.08 0.14 0.17
p_predicted[1097] 0.18 0.00 0.06 0.08 0.13 0.17
p_predicted[1098] 0.18 0.00 0.06 0.08 0.14 0.17
p_predicted[1099] 0.17 0.00 0.06 0.08 0.13 0.17
p_predicted[1100] 0.17 0.00 0.06 0.08 0.13 0.17
p_predicted[1101] 0.17 0.00 0.06 0.08 0.13 0.17
p_predicted[1102] 0.16 0.00 0.06 0.07 0.12 0.15
p_predicted[1103] 0.13 0.00 0.04 0.06 0.10 0.12
p_predicted[1104] 0.12 0.00 0.04 0.05 0.09 0.12
p_predicted[1105] 0.12 0.00 0.04 0.05 0.09 0.11
p_predicted[1106] 0.43 0.00 0.07 0.30 0.38 0.43
p_predicted[1107] 0.43 0.00 0.07 0.30 0.38 0.43
p_predicted[1108] 0.42 0.00 0.07 0.29 0.37 0.42
p_predicted[1109] 0.36 0.00 0.08 0.23 0.31 0.36
p_predicted[1110] 0.22 0.00 0.06 0.12 0.18 0.21
p_predicted[1111] 0.25 0.00 0.06 0.16 0.21 0.24
p_predicted[1112] 0.24 0.00 0.05 0.15 0.20 0.23
p_predicted[1113] 0.23 0.00 0.05 0.15 0.20 0.23
p_predicted[1114] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[1115] 0.20 0.00 0.05 0.12 0.17 0.20
p_predicted[1116] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[1117] 0.22 0.00 0.04 0.14 0.19 0.21
p_predicted[1118] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[1119] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted[1120] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted[1121] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[1122] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[1123] 0.15 0.00 0.03 0.09 0.12 0.14
p_predicted[1124] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted[1125] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted[1126] 0.14 0.00 0.03 0.09 0.11 0.13
p_predicted[1127] 0.29 0.00 0.10 0.13 0.22 0.28
p_predicted[1128] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted[1129] 0.23 0.00 0.07 0.12 0.18 0.22
p_predicted[1130] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted[1131] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[1132] 0.07 0.00 0.07 0.00 0.02 0.04
p_predicted[1133] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1134] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1135] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted[1136] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1137] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted[1138] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted[1139] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1140] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted[1141] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted[1142] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[1143] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted[1144] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted[1145] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1146] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted[1147] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1148] 0.05 0.00 0.02 0.01 0.03 0.05
p_predicted[1149] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1150] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[1151] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted[1152] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[1153] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted[1154] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted[1155] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted[1156] 0.10 0.00 0.04 0.04 0.07 0.09
p_predicted[1157] 0.07 0.00 0.03 0.02 0.05 0.06
p_predicted[1158] 0.10 0.00 0.04 0.04 0.07 0.09
p_predicted[1159] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1160] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1161] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1162] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1163] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1164] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1165] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted[1166] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1167] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted[1168] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted[1169] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[1170] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted[1171] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1172] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[1173] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1174] 0.79 0.00 0.14 0.45 0.71 0.82
p_predicted[1175] 0.78 0.00 0.14 0.45 0.70 0.81
p_predicted[1176] 0.77 0.00 0.14 0.43 0.69 0.80
p_predicted[1177] 0.76 0.00 0.15 0.41 0.67 0.79
p_predicted[1178] 0.76 0.00 0.16 0.39 0.67 0.79
p_predicted[1179] 0.08 0.00 0.04 0.03 0.05 0.08
p_predicted[1180] 0.11 0.00 0.04 0.05 0.08 0.10
p_predicted[1181] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted[1182] 0.07 0.00 0.03 0.03 0.05 0.06
p_predicted[1183] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1184] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted[1185] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[1186] 0.52 0.00 0.19 0.17 0.38 0.52
p_predicted[1187] 0.49 0.00 0.20 0.14 0.34 0.48
p_predicted[1188] 0.49 0.00 0.20 0.14 0.34 0.49
p_predicted[1189] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[1190] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1191] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1192] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1193] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1194] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[1195] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1196] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1197] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1198] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1199] 0.58 0.00 0.12 0.33 0.49 0.58
p_predicted[1200] 0.58 0.00 0.12 0.33 0.49 0.58
p_predicted[1201] 0.64 0.00 0.14 0.35 0.54 0.65
p_predicted[1202] 0.55 0.00 0.12 0.32 0.47 0.56
p_predicted[1203] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1204] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1205] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1206] 0.55 0.00 0.12 0.31 0.47 0.56
p_predicted[1207] 0.50 0.00 0.12 0.25 0.41 0.50
p_predicted[1208] 0.48 0.00 0.12 0.25 0.40 0.48
p_predicted[1209] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1210] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1211] 0.06 0.00 0.05 0.01 0.03 0.05
p_predicted[1212] 0.06 0.00 0.05 0.01 0.03 0.05
p_predicted[1213] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1214] 0.05 0.00 0.03 0.01 0.02 0.04
p_predicted[1215] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1216] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1217] 0.27 0.00 0.08 0.13 0.21 0.26
p_predicted[1218] 0.26 0.00 0.08 0.13 0.21 0.26
p_predicted[1219] 0.22 0.00 0.06 0.11 0.17 0.21
p_predicted[1220] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[1221] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[1222] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted[1223] 0.08 0.00 0.06 0.01 0.03 0.06
p_predicted[1224] 0.08 0.00 0.06 0.01 0.03 0.06
p_predicted[1225] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1226] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1227] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1228] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1229] 0.63 0.00 0.14 0.34 0.54 0.64
p_predicted[1230] 0.64 0.00 0.12 0.38 0.56 0.65
p_predicted[1231] 0.57 0.00 0.14 0.29 0.48 0.58
p_predicted[1232] 0.57 0.00 0.14 0.28 0.47 0.57
p_predicted[1233] 0.31 0.00 0.04 0.22 0.28 0.31
p_predicted[1234] 0.31 0.00 0.04 0.22 0.28 0.31
p_predicted[1235] 0.29 0.00 0.04 0.21 0.26 0.29
p_predicted[1236] 0.29 0.00 0.04 0.21 0.26 0.29
p_predicted[1237] 0.27 0.00 0.04 0.19 0.25 0.27
p_predicted[1238] 0.27 0.00 0.04 0.19 0.25 0.27
p_predicted[1239] 0.23 0.00 0.04 0.16 0.20 0.23
p_predicted[1240] 0.23 0.00 0.04 0.16 0.20 0.23
p_predicted[1241] 0.21 0.00 0.03 0.14 0.18 0.21
p_predicted[1242] 0.21 0.00 0.03 0.14 0.18 0.21
p_predicted[1243] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted[1244] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted[1245] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[1246] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[1247] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted[1248] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted[1249] 0.14 0.00 0.08 0.03 0.08 0.12
p_predicted[1250] 0.10 0.00 0.05 0.02 0.06 0.09
p_predicted[1251] 0.14 0.00 0.08 0.03 0.08 0.12
p_predicted[1252] 0.16 0.00 0.07 0.04 0.10 0.15
p_predicted[1253] 0.11 0.00 0.05 0.03 0.07 0.10
p_predicted[1254] 0.16 0.00 0.07 0.04 0.10 0.15
p_predicted[1255] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted[1256] 0.11 0.00 0.05 0.04 0.07 0.10
p_predicted[1257] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted[1258] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted[1259] 0.11 0.00 0.05 0.04 0.07 0.10
p_predicted[1260] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted[1261] 0.18 0.00 0.09 0.05 0.12 0.17
p_predicted[1262] 0.13 0.00 0.06 0.04 0.09 0.12
p_predicted[1263] 0.18 0.00 0.09 0.05 0.12 0.17
p_predicted[1264] 0.15 0.00 0.08 0.03 0.09 0.13
p_predicted[1265] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted[1266] 0.15 0.00 0.08 0.03 0.09 0.13
p_predicted[1267] 0.04 0.00 0.03 0.00 0.01 0.03
p_predicted[1268] 0.04 0.00 0.03 0.00 0.01 0.03
p_predicted[1269] 0.03 0.00 0.03 0.00 0.01 0.03
p_predicted[1270] 0.03 0.00 0.03 0.00 0.01 0.03
p_predicted[1271] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1272] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted[1273] 0.06 0.00 0.05 0.01 0.02 0.04
p_predicted[1274] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1275] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1276] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1277] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1278] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1279] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1280] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1281] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1282] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1283] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1284] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1285] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1286] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1287] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1288] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1289] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1290] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1291] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1292] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1293] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1294] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1295] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1296] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1297] 0.11 0.00 0.05 0.03 0.07 0.11
p_predicted[1298] 0.13 0.00 0.05 0.05 0.09 0.12
p_predicted[1299] 0.20 0.00 0.08 0.08 0.14 0.19
p_predicted[1300] 0.20 0.00 0.08 0.08 0.14 0.19
p_predicted[1301] 0.16 0.00 0.07 0.05 0.11 0.15
p_predicted[1302] 0.36 0.00 0.11 0.18 0.28 0.35
p_predicted[1303] 0.36 0.00 0.11 0.18 0.28 0.35
p_predicted[1304] 0.28 0.00 0.08 0.14 0.22 0.27
p_predicted[1305] 0.28 0.00 0.08 0.14 0.22 0.27
p_predicted[1306] 0.29 0.00 0.08 0.16 0.24 0.28
p_predicted[1307] 0.29 0.00 0.08 0.16 0.24 0.28
p_predicted[1308] 0.23 0.00 0.06 0.12 0.19 0.23
p_predicted[1309] 0.23 0.00 0.06 0.12 0.19 0.23
p_predicted[1310] 0.23 0.00 0.06 0.12 0.18 0.22
p_predicted[1311] 0.23 0.00 0.06 0.12 0.18 0.22
p_predicted[1312] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted[1313] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted[1314] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted[1315] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted[1316] 0.08 0.00 0.05 0.01 0.04 0.06
p_predicted[1317] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted[1318] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted[1319] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1320] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1321] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1322] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1323] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1324] 0.08 0.00 0.05 0.01 0.04 0.06
p_predicted[1325] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted[1326] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted[1327] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1328] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1329] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1330] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1331] 0.27 0.00 0.07 0.15 0.22 0.27
p_predicted[1332] 0.22 0.00 0.06 0.12 0.18 0.22
p_predicted[1333] 0.21 0.00 0.06 0.10 0.16 0.20
p_predicted[1334] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1335] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1336] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1337] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1338] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1339] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1340] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1341] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1342] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[1343] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1] 0.22 0.00 0.07 0.10 0.17 0.21
p_predicted_default[2] 0.22 0.00 0.07 0.10 0.17 0.21
p_predicted_default[3] 0.22 0.00 0.07 0.11 0.17 0.21
p_predicted_default[4] 0.19 0.00 0.06 0.09 0.15 0.19
p_predicted_default[5] 0.19 0.00 0.06 0.09 0.15 0.19
p_predicted_default[6] 0.19 0.00 0.06 0.09 0.15 0.18
p_predicted_default[7] 0.18 0.00 0.07 0.07 0.13 0.17
p_predicted_default[8] 0.18 0.00 0.07 0.07 0.13 0.17
p_predicted_default[9] 0.38 0.00 0.08 0.23 0.32 0.37
p_predicted_default[10] 0.39 0.00 0.08 0.25 0.34 0.39
p_predicted_default[11] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[12] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[13] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[14] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[15] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[16] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted_default[17] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted_default[18] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted_default[19] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted_default[20] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted_default[21] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[22] 0.09 0.00 0.06 0.01 0.04 0.07
p_predicted_default[23] 0.38 0.00 0.04 0.30 0.35 0.38
p_predicted_default[24] 0.36 0.00 0.04 0.28 0.33 0.36
p_predicted_default[25] 0.33 0.00 0.05 0.24 0.29 0.33
p_predicted_default[26] 0.27 0.00 0.04 0.20 0.25 0.27
p_predicted_default[27] 0.26 0.00 0.04 0.19 0.23 0.26
p_predicted_default[28] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted_default[29] 0.23 0.00 0.04 0.15 0.20 0.23
p_predicted_default[30] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted_default[31] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted_default[32] 0.31 0.00 0.04 0.24 0.28 0.31
p_predicted_default[33] 0.31 0.00 0.04 0.24 0.28 0.31
p_predicted_default[34] 0.29 0.00 0.04 0.22 0.26 0.29
p_predicted_default[35] 0.29 0.00 0.04 0.22 0.26 0.29
p_predicted_default[36] 0.28 0.00 0.04 0.21 0.25 0.28
p_predicted_default[37] 0.28 0.00 0.04 0.21 0.25 0.28
p_predicted_default[38] 0.21 0.00 0.03 0.15 0.18 0.20
p_predicted_default[39] 0.21 0.00 0.03 0.15 0.18 0.20
p_predicted_default[40] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted_default[41] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted_default[42] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted_default[43] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted_default[44] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted_default[45] 0.14 0.00 0.02 0.10 0.13 0.14
p_predicted_default[46] 0.14 0.00 0.02 0.10 0.12 0.14
p_predicted_default[47] 0.14 0.00 0.02 0.10 0.12 0.14
p_predicted_default[48] 0.14 0.00 0.02 0.09 0.12 0.14
p_predicted_default[49] 0.14 0.00 0.02 0.09 0.12 0.14
p_predicted_default[50] 0.11 0.00 0.06 0.04 0.07 0.10
p_predicted_default[51] 0.08 0.00 0.04 0.03 0.06 0.08
p_predicted_default[52] 0.08 0.00 0.04 0.03 0.06 0.08
p_predicted_default[53] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted_default[54] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[55] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[56] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[57] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted_default[58] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted_default[59] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted_default[60] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted_default[61] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted_default[62] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted_default[63] 0.04 0.00 0.02 0.01 0.03 0.03
p_predicted_default[64] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[65] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted_default[66] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[67] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[68] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[69] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted_default[70] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[71] 0.20 0.00 0.05 0.11 0.16 0.20
p_predicted_default[72] 0.19 0.00 0.05 0.11 0.16 0.19
p_predicted_default[73] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[74] 0.18 0.00 0.05 0.10 0.14 0.17
p_predicted_default[75] 0.13 0.00 0.05 0.05 0.09 0.12
p_predicted_default[76] 0.64 0.00 0.11 0.42 0.57 0.65
p_predicted_default[77] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted_default[78] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted_default[79] 0.64 0.00 0.11 0.42 0.57 0.65
p_predicted_default[80] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted_default[81] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted_default[82] 0.64 0.00 0.11 0.42 0.57 0.65
p_predicted_default[83] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted_default[84] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted_default[85] 0.56 0.00 0.08 0.39 0.51 0.57
p_predicted_default[86] 0.38 0.00 0.06 0.26 0.34 0.38
p_predicted_default[87] 0.32 0.00 0.06 0.20 0.27 0.31
p_predicted_default[88] 0.16 0.00 0.03 0.10 0.13 0.15
p_predicted_default[89] 0.15 0.00 0.03 0.10 0.13 0.14
p_predicted_default[90] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[91] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[92] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[93] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[94] 0.62 0.00 0.13 0.36 0.54 0.63
p_predicted_default[95] 0.62 0.00 0.12 0.37 0.54 0.63
p_predicted_default[96] 0.58 0.00 0.12 0.35 0.50 0.59
p_predicted_default[97] 0.36 0.00 0.13 0.14 0.26 0.35
p_predicted_default[98] 0.35 0.00 0.13 0.13 0.26 0.35
p_predicted_default[99] 0.20 0.00 0.07 0.09 0.16 0.20
p_predicted_default[100] 0.20 0.00 0.07 0.09 0.16 0.20
p_predicted_default[101] 0.26 0.00 0.05 0.17 0.22 0.25
p_predicted_default[102] 0.22 0.00 0.04 0.14 0.19 0.21
p_predicted_default[103] 0.18 0.00 0.03 0.11 0.15 0.17
p_predicted_default[104] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted_default[105] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[106] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[107] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[108] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[109] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[110] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[111] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[112] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[113] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[114] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[115] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[116] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[117] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[118] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[119] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[120] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[121] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[122] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[123] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[124] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[125] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[126] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[127] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[128] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted_default[129] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted_default[130] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted_default[131] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[132] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[133] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[134] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[135] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[136] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted_default[137] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted_default[138] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted_default[139] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted_default[140] 0.27 0.00 0.05 0.18 0.24 0.27
p_predicted_default[141] 0.25 0.00 0.05 0.16 0.21 0.25
p_predicted_default[142] 0.21 0.00 0.04 0.13 0.18 0.21
p_predicted_default[143] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[144] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted_default[145] 0.20 0.00 0.04 0.13 0.17 0.19
p_predicted_default[146] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[147] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[148] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted_default[149] 0.14 0.00 0.04 0.07 0.11 0.14
p_predicted_default[150] 0.09 0.00 0.04 0.03 0.06 0.08
p_predicted_default[151] 0.12 0.00 0.04 0.05 0.09 0.11
p_predicted_default[152] 0.12 0.00 0.04 0.05 0.09 0.11
p_predicted_default[153] 0.08 0.00 0.03 0.04 0.06 0.07
p_predicted_default[154] 0.08 0.00 0.03 0.04 0.06 0.08
p_predicted_default[155] 0.09 0.00 0.03 0.05 0.07 0.09
p_predicted_default[156] 0.09 0.00 0.03 0.04 0.07 0.09
p_predicted_default[157] 0.08 0.00 0.03 0.04 0.06 0.07
p_predicted_default[158] 0.09 0.00 0.03 0.04 0.07 0.08
p_predicted_default[159] 0.07 0.00 0.02 0.03 0.05 0.07
p_predicted_default[160] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[161] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted_default[162] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted_default[163] 0.13 0.00 0.04 0.07 0.10 0.13
p_predicted_default[164] 0.13 0.00 0.04 0.07 0.10 0.13
p_predicted_default[165] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted_default[166] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted_default[167] 0.07 0.00 0.02 0.04 0.06 0.07
p_predicted_default[168] 0.07 0.00 0.02 0.04 0.06 0.07
p_predicted_default[169] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted_default[170] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted_default[171] 0.38 0.00 0.04 0.30 0.35 0.38
p_predicted_default[172] 0.35 0.00 0.04 0.27 0.32 0.35
p_predicted_default[173] 0.26 0.00 0.04 0.19 0.23 0.26
p_predicted_default[174] 0.12 0.00 0.05 0.05 0.09 0.12
p_predicted_default[175] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted_default[176] 0.07 0.00 0.04 0.02 0.04 0.06
p_predicted_default[177] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted_default[178] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted_default[179] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted_default[180] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted_default[181] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted_default[182] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[183] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[184] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[185] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[186] 0.03 0.00 0.02 0.01 0.02 0.02
p_predicted_default[187] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted_default[188] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted_default[189] 0.10 0.00 0.04 0.04 0.08 0.10
p_predicted_default[190] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted_default[191] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted_default[192] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted_default[193] 0.08 0.00 0.03 0.03 0.06 0.07
p_predicted_default[194] 0.07 0.00 0.03 0.03 0.06 0.07
p_predicted_default[195] 0.07 0.00 0.03 0.03 0.06 0.07
p_predicted_default[196] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[197] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted_default[198] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted_default[199] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted_default[200] 0.10 0.00 0.04 0.04 0.08 0.10
p_predicted_default[201] 0.10 0.00 0.03 0.04 0.07 0.09
p_predicted_default[202] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[203] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[204] 0.07 0.00 0.03 0.03 0.06 0.07
p_predicted_default[205] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[206] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[207] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[208] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted_default[209] 0.13 0.00 0.03 0.07 0.10 0.12
p_predicted_default[210] 0.14 0.00 0.03 0.08 0.11 0.13
p_predicted_default[211] 0.14 0.00 0.03 0.08 0.11 0.13
p_predicted_default[212] 0.11 0.00 0.03 0.06 0.09 0.10
p_predicted_default[213] 0.09 0.00 0.02 0.05 0.07 0.08
p_predicted_default[214] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted_default[215] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted_default[216] 0.58 0.00 0.12 0.34 0.50 0.59
p_predicted_default[217] 0.60 0.00 0.10 0.40 0.53 0.60
p_predicted_default[218] 0.59 0.00 0.10 0.39 0.52 0.60
p_predicted_default[219] 0.35 0.00 0.13 0.13 0.25 0.34
p_predicted_default[220] 0.34 0.00 0.13 0.12 0.24 0.33
p_predicted_default[221] 0.60 0.00 0.10 0.40 0.53 0.60
p_predicted_default[222] 0.59 0.00 0.10 0.39 0.52 0.59
p_predicted_default[223] 0.34 0.00 0.13 0.13 0.25 0.34
p_predicted_default[224] 0.34 0.00 0.13 0.12 0.24 0.33
p_predicted_default[225] 0.34 0.00 0.13 0.12 0.24 0.33
p_predicted_default[226] 0.28 0.00 0.11 0.10 0.19 0.26
p_predicted_default[227] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted_default[228] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted_default[229] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted_default[230] 0.29 0.00 0.11 0.11 0.21 0.28
p_predicted_default[231] 0.09 0.00 0.04 0.03 0.05 0.08
p_predicted_default[232] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[233] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted_default[234] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted_default[235] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted_default[236] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[237] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[238] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[239] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[240] 0.21 0.00 0.14 0.03 0.10 0.18
p_predicted_default[241] 0.20 0.00 0.13 0.03 0.10 0.16
p_predicted_default[242] 0.20 0.00 0.13 0.03 0.10 0.16
p_predicted_default[243] 0.18 0.00 0.12 0.02 0.08 0.14
p_predicted_default[244] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted_default[245] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted_default[246] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted_default[247] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted_default[248] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted_default[249] 0.40 0.00 0.09 0.21 0.33 0.40
p_predicted_default[250] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted_default[251] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted_default[252] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted_default[253] 0.46 0.00 0.06 0.36 0.42 0.46
p_predicted_default[254] 0.46 0.00 0.06 0.36 0.42 0.46
p_predicted_default[255] 0.46 0.00 0.06 0.36 0.42 0.46
p_predicted_default[256] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted_default[257] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted_default[258] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted_default[259] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted_default[260] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted_default[261] 0.45 0.00 0.06 0.34 0.41 0.45
p_predicted_default[262] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted_default[263] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted_default[264] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted_default[265] 0.35 0.00 0.06 0.24 0.31 0.35
p_predicted_default[266] 0.35 0.00 0.06 0.24 0.31 0.35
p_predicted_default[267] 0.35 0.00 0.06 0.24 0.31 0.35
p_predicted_default[268] 0.34 0.00 0.06 0.24 0.30 0.34
p_predicted_default[269] 0.34 0.00 0.06 0.24 0.30 0.34
p_predicted_default[270] 0.34 0.00 0.06 0.24 0.30 0.34
p_predicted_default[271] 0.20 0.00 0.03 0.15 0.18 0.20
p_predicted_default[272] 0.20 0.00 0.03 0.15 0.18 0.20
p_predicted_default[273] 0.20 0.00 0.03 0.15 0.18 0.20
p_predicted_default[274] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted_default[275] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted_default[276] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted_default[277] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[278] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[279] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[280] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[281] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[282] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[283] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[284] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[285] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[286] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[287] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[288] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[289] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[290] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[291] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[292] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[293] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[294] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[295] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[296] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[297] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[298] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[299] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[300] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[301] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[302] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[303] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[304] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[305] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[306] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[307] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[308] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[309] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[310] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[311] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[312] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[313] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[314] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[315] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[316] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[317] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[318] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[319] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[320] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[321] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[322] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[323] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[324] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[325] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted_default[326] 0.13 0.00 0.04 0.07 0.10 0.13
p_predicted_default[327] 0.15 0.00 0.05 0.07 0.11 0.14
p_predicted_default[328] 0.09 0.00 0.03 0.04 0.06 0.08
p_predicted_default[329] 0.09 0.00 0.03 0.04 0.07 0.09
p_predicted_default[330] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted_default[331] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[332] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[333] 0.06 0.00 0.02 0.02 0.04 0.06
p_predicted_default[334] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[335] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted_default[336] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted_default[337] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[338] 0.28 0.00 0.15 0.05 0.16 0.26
p_predicted_default[339] 0.26 0.00 0.14 0.05 0.15 0.24
p_predicted_default[340] 0.25 0.00 0.13 0.05 0.15 0.23
p_predicted_default[341] 0.25 0.00 0.13 0.05 0.15 0.23
p_predicted_default[342] 0.24 0.00 0.13 0.05 0.15 0.22
p_predicted_default[343] 0.24 0.00 0.13 0.05 0.14 0.22
p_predicted_default[344] 0.24 0.00 0.13 0.05 0.14 0.22
p_predicted_default[345] 0.27 0.00 0.14 0.05 0.16 0.24
p_predicted_default[346] 0.42 0.00 0.07 0.29 0.37 0.42
p_predicted_default[347] 0.42 0.00 0.07 0.29 0.37 0.42
p_predicted_default[348] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted_default[349] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted_default[350] 0.36 0.00 0.05 0.27 0.33 0.36
p_predicted_default[351] 0.36 0.00 0.05 0.27 0.33 0.36
p_predicted_default[352] 0.27 0.00 0.05 0.19 0.24 0.27
p_predicted_default[353] 0.27 0.00 0.05 0.19 0.24 0.27
p_predicted_default[354] 0.21 0.00 0.09 0.07 0.14 0.20
p_predicted_default[355] 0.21 0.00 0.11 0.06 0.13 0.19
p_predicted_default[356] 0.26 0.00 0.09 0.11 0.19 0.25
p_predicted_default[357] 0.26 0.00 0.11 0.09 0.18 0.25
p_predicted_default[358] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted_default[359] 0.21 0.00 0.07 0.10 0.16 0.21
p_predicted_default[360] 0.19 0.00 0.06 0.09 0.14 0.18
p_predicted_default[361] 0.19 0.00 0.07 0.08 0.14 0.18
p_predicted_default[362] 0.14 0.00 0.05 0.05 0.10 0.13
p_predicted_default[363] 0.13 0.00 0.06 0.05 0.09 0.13
p_predicted_default[364] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted_default[365] 0.13 0.00 0.06 0.05 0.09 0.13
p_predicted_default[366] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted_default[367] 0.13 0.00 0.06 0.05 0.09 0.13
p_predicted_default[368] 0.37 0.00 0.05 0.27 0.33 0.37
p_predicted_default[369] 0.37 0.00 0.05 0.27 0.33 0.37
p_predicted_default[370] 0.37 0.00 0.05 0.27 0.33 0.37
p_predicted_default[371] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[372] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[373] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[374] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[375] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[376] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[377] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[378] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[379] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[380] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[381] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[382] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[383] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[384] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[385] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[386] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[387] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[388] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[389] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[390] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[391] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[392] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[393] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[394] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[395] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[396] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[397] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[398] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[399] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[400] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[401] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[402] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[403] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[404] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[405] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[406] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[407] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[408] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[409] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[410] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[411] 0.03 0.00 0.02 0.00 0.01 0.03
p_predicted_default[412] 0.03 0.00 0.02 0.00 0.01 0.03
p_predicted_default[413] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[414] 0.32 0.00 0.09 0.15 0.26 0.32
p_predicted_default[415] 0.28 0.00 0.05 0.19 0.24 0.27
p_predicted_default[416] 0.27 0.00 0.05 0.18 0.23 0.26
p_predicted_default[417] 0.26 0.00 0.04 0.18 0.23 0.26
p_predicted_default[418] 0.27 0.00 0.05 0.18 0.23 0.26
p_predicted_default[419] 0.27 0.00 0.05 0.19 0.24 0.27
p_predicted_default[420] 0.23 0.00 0.05 0.14 0.19 0.23
p_predicted_default[421] 0.25 0.00 0.04 0.18 0.22 0.25
p_predicted_default[422] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[423] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted_default[424] 0.20 0.00 0.04 0.13 0.18 0.20
p_predicted_default[425] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[426] 0.15 0.00 0.03 0.09 0.12 0.14
p_predicted_default[427] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted_default[428] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[429] 0.20 0.00 0.03 0.14 0.17 0.19
p_predicted_default[430] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted_default[431] 0.07 0.00 0.02 0.04 0.06 0.07
p_predicted_default[432] 0.05 0.00 0.02 0.03 0.04 0.05
p_predicted_default[433] 0.37 0.00 0.07 0.24 0.32 0.36
p_predicted_default[434] 0.32 0.00 0.07 0.21 0.28 0.32
p_predicted_default[435] 0.26 0.00 0.06 0.15 0.22 0.25
p_predicted_default[436] 0.26 0.00 0.06 0.15 0.21 0.25
p_predicted_default[437] 0.29 0.00 0.07 0.17 0.24 0.29
p_predicted_default[438] 0.17 0.00 0.06 0.06 0.12 0.16
p_predicted_default[439] 0.15 0.00 0.07 0.04 0.10 0.14
p_predicted_default[440] 0.15 0.00 0.07 0.04 0.09 0.14
p_predicted_default[441] 0.13 0.00 0.07 0.03 0.08 0.12
p_predicted_default[442] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted_default[443] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted_default[444] 0.29 0.00 0.05 0.20 0.25 0.29
p_predicted_default[445] 0.30 0.00 0.05 0.20 0.26 0.29
p_predicted_default[446] 0.29 0.00 0.05 0.19 0.25 0.29
p_predicted_default[447] 0.26 0.00 0.05 0.17 0.23 0.26
p_predicted_default[448] 0.28 0.00 0.05 0.18 0.24 0.27
p_predicted_default[449] 0.23 0.00 0.05 0.14 0.20 0.23
p_predicted_default[450] 0.22 0.00 0.04 0.14 0.19 0.22
p_predicted_default[451] 0.25 0.00 0.05 0.16 0.22 0.25
p_predicted_default[452] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[453] 0.21 0.00 0.04 0.13 0.18 0.21
p_predicted_default[454] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[455] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted_default[456] 0.16 0.00 0.07 0.05 0.11 0.16
p_predicted_default[457] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted_default[458] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted_default[459] 0.12 0.00 0.05 0.04 0.08 0.12
p_predicted_default[460] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted_default[461] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted_default[462] 0.28 0.00 0.05 0.18 0.24 0.28
p_predicted_default[463] 0.26 0.00 0.05 0.17 0.22 0.25
p_predicted_default[464] 0.25 0.00 0.05 0.16 0.21 0.25
p_predicted_default[465] 0.24 0.00 0.05 0.15 0.20 0.24
p_predicted_default[466] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[467] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[468] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted_default[469] 0.22 0.00 0.07 0.10 0.17 0.22
p_predicted_default[470] 0.28 0.00 0.05 0.18 0.24 0.27
p_predicted_default[471] 0.24 0.00 0.05 0.16 0.21 0.24
p_predicted_default[472] 0.25 0.00 0.05 0.17 0.22 0.25
p_predicted_default[473] 0.20 0.00 0.04 0.12 0.17 0.19
p_predicted_default[474] 0.21 0.00 0.04 0.13 0.18 0.20
p_predicted_default[475] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[476] 0.20 0.00 0.04 0.12 0.17 0.19
p_predicted_default[477] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted_default[478] 0.10 0.00 0.03 0.05 0.07 0.09
p_predicted_default[479] 0.08 0.00 0.03 0.03 0.05 0.07
p_predicted_default[480] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted_default[481] 0.21 0.00 0.06 0.09 0.16 0.21
p_predicted_default[482] 0.26 0.00 0.04 0.18 0.23 0.25
p_predicted_default[483] 0.26 0.00 0.04 0.19 0.23 0.26
p_predicted_default[484] 0.17 0.00 0.03 0.12 0.16 0.17
p_predicted_default[485] 0.17 0.00 0.03 0.12 0.15 0.17
p_predicted_default[486] 0.16 0.00 0.03 0.12 0.14 0.16
p_predicted_default[487] 0.16 0.00 0.03 0.12 0.15 0.16
p_predicted_default[488] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted_default[489] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted_default[490] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted_default[491] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted_default[492] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted_default[493] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted_default[494] 0.07 0.00 0.03 0.02 0.05 0.07
p_predicted_default[495] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted_default[496] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted_default[497] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[498] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[499] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[500] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[501] 0.22 0.00 0.07 0.10 0.17 0.22
p_predicted_default[502] 0.28 0.00 0.05 0.19 0.24 0.28
p_predicted_default[503] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[504] 0.20 0.00 0.04 0.12 0.17 0.20
p_predicted_default[505] 0.19 0.00 0.04 0.12 0.17 0.19
p_predicted_default[506] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[507] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[508] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted_default[509] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted_default[510] 0.08 0.00 0.03 0.03 0.06 0.08
p_predicted_default[511] 0.08 0.00 0.03 0.02 0.05 0.07
p_predicted_default[512] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[513] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[514] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[515] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[516] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[517] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[518] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[519] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[520] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[521] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[522] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[523] 0.18 0.00 0.08 0.05 0.12 0.17
p_predicted_default[524] 0.17 0.00 0.09 0.04 0.10 0.15
p_predicted_default[525] 0.49 0.00 0.06 0.37 0.45 0.49
p_predicted_default[526] 0.49 0.00 0.06 0.37 0.45 0.49
p_predicted_default[527] 0.49 0.00 0.06 0.37 0.45 0.49
p_predicted_default[528] 0.46 0.00 0.06 0.34 0.42 0.46
p_predicted_default[529] 0.46 0.00 0.06 0.34 0.42 0.46
p_predicted_default[530] 0.46 0.00 0.06 0.34 0.42 0.46
p_predicted_default[531] 0.30 0.00 0.04 0.23 0.28 0.30
p_predicted_default[532] 0.30 0.00 0.04 0.23 0.28 0.30
p_predicted_default[533] 0.30 0.00 0.04 0.23 0.28 0.30
p_predicted_default[534] 0.30 0.00 0.04 0.22 0.27 0.29
p_predicted_default[535] 0.30 0.00 0.04 0.22 0.27 0.29
p_predicted_default[536] 0.30 0.00 0.04 0.22 0.27 0.29
p_predicted_default[537] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted_default[538] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted_default[539] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted_default[540] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted_default[541] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted_default[542] 0.38 0.00 0.06 0.27 0.34 0.38
p_predicted_default[543] 0.36 0.00 0.05 0.26 0.32 0.36
p_predicted_default[544] 0.36 0.00 0.05 0.26 0.32 0.36
p_predicted_default[545] 0.36 0.00 0.05 0.26 0.32 0.36
p_predicted_default[546] 0.21 0.00 0.04 0.14 0.18 0.20
p_predicted_default[547] 0.21 0.00 0.04 0.14 0.18 0.20
p_predicted_default[548] 0.21 0.00 0.04 0.14 0.18 0.20
p_predicted_default[549] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[550] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[551] 0.21 0.00 0.04 0.14 0.18 0.21
p_predicted_default[552] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[553] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[554] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[555] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[556] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[557] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[558] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[559] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[560] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[561] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[562] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[563] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[564] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[565] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[566] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[567] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[568] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[569] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[570] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[571] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[572] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[573] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[574] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[575] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[576] 0.15 0.00 0.07 0.05 0.11 0.15
p_predicted_default[577] 0.08 0.00 0.04 0.02 0.05 0.07
p_predicted_default[578] 0.07 0.00 0.04 0.02 0.04 0.07
p_predicted_default[579] 0.08 0.00 0.05 0.02 0.04 0.07
p_predicted_default[580] 0.08 0.00 0.05 0.02 0.05 0.07
p_predicted_default[581] 0.06 0.00 0.04 0.01 0.04 0.05
p_predicted_default[582] 0.25 0.00 0.08 0.11 0.20 0.25
p_predicted_default[583] 0.31 0.00 0.06 0.21 0.27 0.31
p_predicted_default[584] 0.25 0.00 0.05 0.15 0.21 0.24
p_predicted_default[585] 0.21 0.00 0.04 0.13 0.18 0.20
p_predicted_default[586] 0.19 0.00 0.04 0.12 0.16 0.18
p_predicted_default[587] 0.12 0.00 0.06 0.03 0.07 0.11
p_predicted_default[588] 0.12 0.00 0.06 0.03 0.07 0.11
p_predicted_default[589] 0.15 0.00 0.07 0.05 0.10 0.15
p_predicted_default[590] 0.15 0.00 0.07 0.05 0.10 0.14
p_predicted_default[591] 0.16 0.00 0.07 0.05 0.11 0.15
p_predicted_default[592] 0.15 0.00 0.07 0.05 0.11 0.14
p_predicted_default[593] 0.16 0.00 0.07 0.05 0.11 0.15
p_predicted_default[594] 0.15 0.00 0.07 0.05 0.11 0.14
p_predicted_default[595] 0.11 0.00 0.06 0.03 0.07 0.10
p_predicted_default[596] 0.11 0.00 0.06 0.03 0.07 0.10
p_predicted_default[597] 0.30 0.00 0.08 0.14 0.24 0.30
p_predicted_default[598] 0.36 0.00 0.05 0.27 0.33 0.36
p_predicted_default[599] 0.35 0.00 0.05 0.25 0.31 0.35
p_predicted_default[600] 0.34 0.00 0.05 0.24 0.30 0.34
p_predicted_default[601] 0.27 0.00 0.05 0.18 0.24 0.27
p_predicted_default[602] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted_default[603] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted_default[604] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted_default[605] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted_default[606] 0.09 0.00 0.04 0.03 0.06 0.08
p_predicted_default[607] 0.57 0.00 0.16 0.26 0.45 0.57
p_predicted_default[608] 0.57 0.00 0.16 0.26 0.45 0.57
p_predicted_default[609] 0.54 0.00 0.15 0.25 0.43 0.54
p_predicted_default[610] 0.54 0.00 0.15 0.25 0.43 0.54
p_predicted_default[611] 0.53 0.00 0.15 0.25 0.43 0.53
p_predicted_default[612] 0.53 0.00 0.15 0.25 0.43 0.53
p_predicted_default[613] 0.56 0.00 0.15 0.27 0.46 0.56
p_predicted_default[614] 0.56 0.00 0.15 0.27 0.46 0.56
p_predicted_default[615] 0.66 0.00 0.15 0.33 0.56 0.67
p_predicted_default[616] 0.66 0.00 0.15 0.33 0.56 0.67
p_predicted_default[617] 0.20 0.00 0.03 0.13 0.17 0.20
p_predicted_default[618] 0.20 0.00 0.03 0.13 0.17 0.20
p_predicted_default[619] 0.18 0.00 0.04 0.11 0.16 0.18
p_predicted_default[620] 0.05 0.00 0.11 0.00 0.00 0.00
p_predicted_default[621] 0.05 0.00 0.11 0.00 0.00 0.00
p_predicted_default[622] 0.09 0.00 0.06 0.02 0.05 0.07
p_predicted_default[623] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted_default[624] 0.08 0.00 0.05 0.02 0.05 0.07
p_predicted_default[625] 0.06 0.00 0.04 0.01 0.04 0.05
p_predicted_default[626] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[627] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[628] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[629] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[630] 0.31 0.00 0.09 0.15 0.25 0.30
p_predicted_default[631] 0.38 0.00 0.06 0.27 0.34 0.37
p_predicted_default[632] 0.31 0.00 0.06 0.20 0.26 0.30
p_predicted_default[633] 0.30 0.00 0.06 0.20 0.26 0.30
p_predicted_default[634] 0.30 0.00 0.06 0.19 0.26 0.30
p_predicted_default[635] 0.14 0.00 0.04 0.08 0.12 0.14
p_predicted_default[636] 0.21 0.00 0.08 0.08 0.15 0.20
p_predicted_default[637] 0.21 0.00 0.10 0.06 0.14 0.20
p_predicted_default[638] 0.26 0.00 0.08 0.13 0.21 0.26
p_predicted_default[639] 0.26 0.00 0.10 0.11 0.19 0.25
p_predicted_default[640] 0.26 0.00 0.07 0.13 0.21 0.25
p_predicted_default[641] 0.26 0.00 0.09 0.11 0.19 0.25
p_predicted_default[642] 0.26 0.00 0.07 0.13 0.21 0.25
p_predicted_default[643] 0.26 0.00 0.09 0.11 0.19 0.25
p_predicted_default[644] 0.19 0.00 0.07 0.08 0.14 0.18
p_predicted_default[645] 0.19 0.00 0.08 0.06 0.13 0.18
p_predicted_default[646] 0.19 0.00 0.07 0.07 0.14 0.18
p_predicted_default[647] 0.19 0.00 0.09 0.06 0.13 0.18
p_predicted_default[648] 0.19 0.00 0.07 0.08 0.14 0.18
p_predicted_default[649] 0.19 0.00 0.08 0.06 0.13 0.18
p_predicted_default[650] 0.27 0.00 0.11 0.11 0.20 0.26
p_predicted_default[651] 0.27 0.00 0.11 0.11 0.20 0.26
p_predicted_default[652] 0.27 0.00 0.10 0.10 0.19 0.26
p_predicted_default[653] 0.27 0.00 0.10 0.10 0.19 0.26
p_predicted_default[654] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted_default[655] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted_default[656] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[657] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[658] 0.06 0.00 0.04 0.01 0.04 0.06
p_predicted_default[659] 0.06 0.00 0.04 0.01 0.04 0.06
p_predicted_default[660] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[661] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[662] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[663] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[664] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[665] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[666] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[667] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[668] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[669] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[670] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[671] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[672] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[673] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[674] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[675] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[676] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[677] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[678] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[679] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[680] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[681] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[682] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[683] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[684] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[685] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[686] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[687] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[688] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[689] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[690] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[691] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[692] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[693] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted_default[694] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[695] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[696] 0.05 0.00 0.03 0.02 0.03 0.05
p_predicted_default[697] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[698] 0.08 0.00 0.03 0.03 0.05 0.07
p_predicted_default[699] 0.05 0.00 0.03 0.02 0.03 0.05
p_predicted_default[700] 0.08 0.00 0.03 0.03 0.05 0.07
p_predicted_default[701] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[702] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted_default[703] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[704] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[705] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted_default[706] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[707] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[708] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted_default[709] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[710] 0.47 0.00 0.06 0.37 0.44 0.47
p_predicted_default[711] 0.39 0.00 0.06 0.27 0.35 0.39
p_predicted_default[712] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted_default[713] 0.25 0.00 0.05 0.17 0.22 0.25
p_predicted_default[714] 0.25 0.00 0.05 0.17 0.22 0.25
p_predicted_default[715] 0.25 0.00 0.04 0.16 0.21 0.24
p_predicted_default[716] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted_default[717] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[718] 0.18 0.00 0.04 0.12 0.16 0.18
p_predicted_default[719] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted_default[720] 0.09 0.00 0.03 0.05 0.07 0.09
p_predicted_default[721] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[722] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[723] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[724] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[725] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[726] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[727] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[728] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[729] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[730] 0.33 0.00 0.11 0.14 0.24 0.32
p_predicted_default[731] 0.33 0.00 0.11 0.14 0.24 0.32
p_predicted_default[732] 0.24 0.00 0.10 0.08 0.17 0.23
p_predicted_default[733] 0.52 0.00 0.13 0.27 0.43 0.52
p_predicted_default[734] 0.52 0.00 0.13 0.27 0.43 0.52
p_predicted_default[735] 0.50 0.00 0.12 0.27 0.42 0.50
p_predicted_default[736] 0.43 0.00 0.12 0.20 0.34 0.42
p_predicted_default[737] 0.43 0.00 0.12 0.20 0.34 0.42
p_predicted_default[738] 0.42 0.00 0.12 0.19 0.33 0.42
p_predicted_default[739] 0.41 0.00 0.13 0.18 0.32 0.41
p_predicted_default[740] 0.41 0.00 0.13 0.17 0.31 0.40
p_predicted_default[741] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted_default[742] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted_default[743] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[744] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[745] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[746] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[747] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[748] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[749] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[750] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[751] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[752] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted_default[753] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted_default[754] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted_default[755] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted_default[756] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted_default[757] 0.02 0.00 0.06 0.00 0.00 0.00
p_predicted_default[758] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[759] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[760] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[761] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[762] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[763] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[764] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[765] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[766] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[767] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[768] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[769] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[770] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[771] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[772] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[773] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[774] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[775] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[776] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[777] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[778] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[779] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[780] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[781] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[782] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[783] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[784] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[785] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[786] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[787] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[788] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[789] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[790] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[791] 0.35 0.00 0.05 0.26 0.32 0.35
p_predicted_default[792] 0.34 0.00 0.05 0.25 0.31 0.34
p_predicted_default[793] 0.34 0.00 0.05 0.24 0.30 0.34
p_predicted_default[794] 0.35 0.00 0.05 0.25 0.31 0.35
p_predicted_default[795] 0.29 0.00 0.05 0.20 0.26 0.29
p_predicted_default[796] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted_default[797] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted_default[798] 0.28 0.00 0.05 0.19 0.25 0.28
p_predicted_default[799] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_default[800] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_default[801] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_default[802] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_default[803] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_default[804] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_default[805] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[806] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[807] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[808] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[809] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[810] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted_default[811] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[812] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[813] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[814] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[815] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[816] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[817] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[818] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted_default[819] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[820] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[821] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[822] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[823] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[824] 0.05 0.00 0.03 0.01 0.03 0.05
p_predicted_default[825] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[826] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[827] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[828] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[829] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[830] 0.06 0.00 0.05 0.01 0.03 0.04
p_predicted_default[831] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted_default[832] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[833] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[834] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[835] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[836] 0.15 0.00 0.05 0.06 0.11 0.15
p_predicted_default[837] 0.15 0.00 0.06 0.06 0.11 0.15
p_predicted_default[838] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[839] 0.19 0.00 0.05 0.11 0.16 0.19
p_predicted_default[840] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[841] 0.20 0.00 0.05 0.11 0.16 0.19
p_predicted_default[842] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[843] 0.19 0.00 0.05 0.11 0.16 0.19
p_predicted_default[844] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted_default[845] 0.11 0.00 0.04 0.06 0.09 0.11
p_predicted_default[846] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted_default[847] 0.11 0.00 0.04 0.06 0.09 0.11
p_predicted_default[848] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted_default[849] 0.11 0.00 0.04 0.05 0.08 0.10
p_predicted_default[850] 0.08 0.00 0.02 0.05 0.07 0.08
p_predicted_default[851] 0.08 0.00 0.03 0.04 0.06 0.08
p_predicted_default[852] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted_default[853] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted_default[854] 0.06 0.00 0.02 0.03 0.04 0.06
p_predicted_default[855] 0.06 0.00 0.02 0.02 0.04 0.06
p_predicted_default[856] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted_default[857] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted_default[858] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted_default[859] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted_default[860] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted_default[861] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted_default[862] 0.15 0.00 0.04 0.09 0.13 0.15
p_predicted_default[863] 0.15 0.00 0.04 0.09 0.12 0.14
p_predicted_default[864] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted_default[865] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted_default[866] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted_default[867] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted_default[868] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted_default[869] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted_default[870] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[871] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[872] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[873] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[874] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[875] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[876] 0.29 0.00 0.08 0.16 0.24 0.28
p_predicted_default[877] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted_default[878] 0.16 0.00 0.05 0.07 0.12 0.15
p_predicted_default[879] 0.15 0.00 0.05 0.07 0.11 0.14
p_predicted_default[880] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[881] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[882] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[883] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[884] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[885] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[886] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[887] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[888] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[889] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[890] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[891] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[892] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[893] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[894] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[895] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[896] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[897] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[898] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[899] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[900] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[901] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted_default[902] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted_default[903] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted_default[904] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted_default[905] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted_default[906] 0.09 0.00 0.03 0.04 0.07 0.08
p_predicted_default[907] 0.27 0.00 0.05 0.18 0.24 0.27
p_predicted_default[908] 0.25 0.00 0.05 0.16 0.21 0.24
p_predicted_default[909] 0.20 0.00 0.04 0.13 0.18 0.20
p_predicted_default[910] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted_default[911] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted_default[912] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted_default[913] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[914] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted_default[915] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[916] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted_default[917] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[918] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted_default[919] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[920] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted_default[921] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted_default[922] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[923] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted_default[924] 0.34 0.00 0.09 0.16 0.28 0.34
p_predicted_default[925] 0.39 0.00 0.06 0.27 0.34 0.39
p_predicted_default[926] 0.39 0.00 0.06 0.27 0.34 0.38
p_predicted_default[927] 0.39 0.00 0.06 0.27 0.35 0.39
p_predicted_default[928] 0.11 0.00 0.04 0.04 0.07 0.10
p_predicted_default[929] 0.13 0.00 0.04 0.07 0.11 0.13
p_predicted_default[930] 0.08 0.00 0.02 0.04 0.06 0.07
p_predicted_default[931] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[932] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[933] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[934] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[935] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[936] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[937] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[938] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[939] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[940] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[941] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[942] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[943] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[944] 0.51 0.00 0.07 0.38 0.46 0.51
p_predicted_default[945] 0.49 0.00 0.06 0.36 0.44 0.49
p_predicted_default[946] 0.42 0.00 0.07 0.28 0.37 0.42
p_predicted_default[947] 0.22 0.00 0.04 0.15 0.20 0.22
p_predicted_default[948] 0.22 0.00 0.04 0.16 0.20 0.22
p_predicted_default[949] 0.22 0.00 0.04 0.15 0.19 0.21
p_predicted_default[950] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted_default[951] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted_default[952] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted_default[953] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[954] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[955] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[956] 0.18 0.00 0.05 0.09 0.15 0.18
p_predicted_default[957] 0.18 0.00 0.05 0.09 0.15 0.18
p_predicted_default[958] 0.18 0.00 0.05 0.09 0.15 0.18
p_predicted_default[959] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted_default[960] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted_default[961] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted_default[962] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted_default[963] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted_default[964] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted_default[965] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted_default[966] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted_default[967] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted_default[968] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted_default[969] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted_default[970] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted_default[971] 0.17 0.00 0.05 0.08 0.13 0.17
p_predicted_default[972] 0.17 0.00 0.05 0.08 0.13 0.17
p_predicted_default[973] 0.17 0.00 0.05 0.08 0.13 0.17
p_predicted_default[974] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted_default[975] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted_default[976] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted_default[977] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[978] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[979] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[980] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[981] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[982] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[983] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[984] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[985] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[986] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[987] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[988] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[989] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[990] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[991] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[992] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[993] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[994] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[995] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[996] 0.93 0.00 0.10 0.62 0.91 0.97
p_predicted_default[997] 0.93 0.00 0.09 0.66 0.91 0.97
p_predicted_default[998] 0.94 0.00 0.09 0.67 0.93 0.98
p_predicted_default[999] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[1000] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[1001] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[1002] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[1003] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[1004] 0.14 0.00 0.08 0.03 0.09 0.13
p_predicted_default[1005] 0.17 0.00 0.09 0.04 0.10 0.15
p_predicted_default[1006] 0.17 0.00 0.09 0.04 0.10 0.15
p_predicted_default[1007] 0.13 0.00 0.07 0.04 0.08 0.12
p_predicted_default[1008] 0.14 0.00 0.08 0.04 0.08 0.12
p_predicted_default[1009] 0.29 0.00 0.13 0.10 0.20 0.28
p_predicted_default[1010] 0.33 0.00 0.12 0.13 0.24 0.31
p_predicted_default[1011] 0.36 0.00 0.15 0.12 0.25 0.35
p_predicted_default[1012] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[1013] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1014] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1015] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted_default[1016] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[1017] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1018] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1019] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1020] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[1021] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1022] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1023] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1024] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[1025] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[1026] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[1027] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted_default[1028] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[1029] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1030] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1031] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1032] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[1033] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1034] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1035] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1036] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[1037] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[1038] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1039] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[1040] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1041] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[1042] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1043] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[1044] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1045] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[1046] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1047] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1048] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1049] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1050] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1051] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[1052] 0.10 0.00 0.06 0.02 0.05 0.09
p_predicted_default[1053] 0.10 0.00 0.06 0.02 0.05 0.09
p_predicted_default[1054] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1055] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1056] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1057] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1058] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted_default[1059] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted_default[1060] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted_default[1061] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted_default[1062] 0.10 0.00 0.06 0.02 0.05 0.09
p_predicted_default[1063] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1064] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1065] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1066] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted_default[1067] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted_default[1068] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted_default[1069] 0.09 0.00 0.06 0.01 0.04 0.07
p_predicted_default[1070] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted_default[1071] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[1072] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[1073] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[1074] 0.14 0.00 0.04 0.06 0.11 0.13
p_predicted_default[1075] 0.14 0.00 0.04 0.06 0.11 0.13
p_predicted_default[1076] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted_default[1077] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted_default[1078] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[1079] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[1080] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[1081] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[1082] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[1083] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[1084] 0.14 0.00 0.05 0.05 0.10 0.14
p_predicted_default[1085] 0.18 0.00 0.04 0.10 0.15 0.17
p_predicted_default[1086] 0.13 0.00 0.03 0.07 0.10 0.12
p_predicted_default[1087] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted_default[1088] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted_default[1089] 0.10 0.00 0.03 0.05 0.08 0.09
p_predicted_default[1090] 0.09 0.00 0.03 0.05 0.08 0.09
p_predicted_default[1091] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted_default[1092] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted_default[1093] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted_default[1094] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted_default[1095] 0.18 0.00 0.06 0.08 0.13 0.17
p_predicted_default[1096] 0.18 0.00 0.06 0.08 0.14 0.17
p_predicted_default[1097] 0.18 0.00 0.06 0.08 0.13 0.17
p_predicted_default[1098] 0.18 0.00 0.06 0.08 0.14 0.17
p_predicted_default[1099] 0.17 0.00 0.06 0.08 0.13 0.17
p_predicted_default[1100] 0.17 0.00 0.06 0.08 0.13 0.17
p_predicted_default[1101] 0.17 0.00 0.06 0.08 0.13 0.17
p_predicted_default[1102] 0.16 0.00 0.06 0.07 0.12 0.15
p_predicted_default[1103] 0.13 0.00 0.04 0.06 0.10 0.12
p_predicted_default[1104] 0.12 0.00 0.04 0.05 0.09 0.12
p_predicted_default[1105] 0.12 0.00 0.04 0.05 0.09 0.11
p_predicted_default[1106] 0.43 0.00 0.07 0.30 0.38 0.43
p_predicted_default[1107] 0.43 0.00 0.07 0.30 0.38 0.43
p_predicted_default[1108] 0.42 0.00 0.07 0.29 0.37 0.42
p_predicted_default[1109] 0.36 0.00 0.08 0.23 0.31 0.36
p_predicted_default[1110] 0.22 0.00 0.06 0.12 0.18 0.21
p_predicted_default[1111] 0.25 0.00 0.06 0.16 0.21 0.24
p_predicted_default[1112] 0.24 0.00 0.05 0.15 0.20 0.23
p_predicted_default[1113] 0.23 0.00 0.05 0.15 0.20 0.23
p_predicted_default[1114] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted_default[1115] 0.20 0.00 0.05 0.12 0.17 0.20
p_predicted_default[1116] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted_default[1117] 0.22 0.00 0.04 0.14 0.19 0.21
p_predicted_default[1118] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[1119] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted_default[1120] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted_default[1121] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[1122] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[1123] 0.15 0.00 0.03 0.09 0.12 0.14
p_predicted_default[1124] 0.14 0.00 0.03 0.09 0.12 0.14
p_predicted_default[1125] 0.15 0.00 0.03 0.10 0.13 0.15
p_predicted_default[1126] 0.14 0.00 0.03 0.09 0.11 0.13
p_predicted_default[1127] 0.29 0.00 0.10 0.13 0.22 0.28
p_predicted_default[1128] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted_default[1129] 0.23 0.00 0.07 0.12 0.18 0.22
p_predicted_default[1130] 0.18 0.00 0.05 0.09 0.14 0.18
p_predicted_default[1131] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted_default[1132] 0.07 0.00 0.07 0.00 0.02 0.04
p_predicted_default[1133] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[1134] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[1135] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1136] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1137] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1138] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1139] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1140] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1141] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1142] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[1143] 0.02 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1144] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1145] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1146] 0.01 0.00 0.02 0.00 0.00 0.01
p_predicted_default[1147] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[1148] 0.05 0.00 0.02 0.01 0.03 0.05
p_predicted_default[1149] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[1150] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[1151] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted_default[1152] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[1153] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted_default[1154] 0.04 0.00 0.02 0.01 0.02 0.04
p_predicted_default[1155] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted_default[1156] 0.10 0.00 0.04 0.04 0.07 0.09
p_predicted_default[1157] 0.07 0.00 0.03 0.02 0.05 0.06
p_predicted_default[1158] 0.10 0.00 0.04 0.04 0.07 0.09
p_predicted_default[1159] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted_default[1160] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1161] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1162] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1163] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1164] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1165] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted_default[1166] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1167] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted_default[1168] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted_default[1169] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[1170] 0.04 0.00 0.02 0.01 0.03 0.04
p_predicted_default[1171] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1172] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[1173] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1174] 0.79 0.00 0.14 0.45 0.71 0.82
p_predicted_default[1175] 0.78 0.00 0.14 0.45 0.70 0.81
p_predicted_default[1176] 0.77 0.00 0.14 0.43 0.69 0.80
p_predicted_default[1177] 0.76 0.00 0.15 0.41 0.67 0.79
p_predicted_default[1178] 0.76 0.00 0.16 0.39 0.67 0.79
p_predicted_default[1179] 0.08 0.00 0.04 0.03 0.05 0.08
p_predicted_default[1180] 0.11 0.00 0.04 0.05 0.08 0.10
p_predicted_default[1181] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted_default[1182] 0.07 0.00 0.03 0.03 0.05 0.06
p_predicted_default[1183] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[1184] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted_default[1185] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[1186] 0.52 0.00 0.19 0.17 0.38 0.52
p_predicted_default[1187] 0.49 0.00 0.20 0.14 0.34 0.48
p_predicted_default[1188] 0.49 0.00 0.20 0.14 0.34 0.49
p_predicted_default[1189] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[1190] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1191] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1192] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1193] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1194] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted_default[1195] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1196] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1197] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[1198] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted_default[1199] 0.58 0.00 0.12 0.33 0.49 0.58
p_predicted_default[1200] 0.58 0.00 0.12 0.33 0.49 0.58
p_predicted_default[1201] 0.64 0.00 0.14 0.35 0.54 0.65
p_predicted_default[1202] 0.55 0.00 0.12 0.32 0.47 0.56
p_predicted_default[1203] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted_default[1204] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted_default[1205] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted_default[1206] 0.55 0.00 0.12 0.31 0.47 0.56
p_predicted_default[1207] 0.50 0.00 0.12 0.25 0.41 0.50
p_predicted_default[1208] 0.48 0.00 0.12 0.25 0.40 0.48
p_predicted_default[1209] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1210] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1211] 0.06 0.00 0.05 0.01 0.03 0.05
p_predicted_default[1212] 0.06 0.00 0.05 0.01 0.03 0.05
p_predicted_default[1213] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1214] 0.05 0.00 0.03 0.01 0.02 0.04
p_predicted_default[1215] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted_default[1216] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted_default[1217] 0.27 0.00 0.08 0.13 0.21 0.26
p_predicted_default[1218] 0.26 0.00 0.08 0.13 0.21 0.26
p_predicted_default[1219] 0.22 0.00 0.06 0.11 0.17 0.21
p_predicted_default[1220] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted_default[1221] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted_default[1222] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted_default[1223] 0.08 0.00 0.06 0.01 0.03 0.06
p_predicted_default[1224] 0.08 0.00 0.06 0.01 0.03 0.06
p_predicted_default[1225] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[1226] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[1227] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[1228] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[1229] 0.63 0.00 0.14 0.34 0.54 0.64
p_predicted_default[1230] 0.64 0.00 0.12 0.38 0.56 0.65
p_predicted_default[1231] 0.57 0.00 0.14 0.29 0.48 0.58
p_predicted_default[1232] 0.57 0.00 0.14 0.28 0.47 0.57
p_predicted_default[1233] 0.31 0.00 0.04 0.22 0.28 0.31
p_predicted_default[1234] 0.31 0.00 0.04 0.22 0.28 0.31
p_predicted_default[1235] 0.29 0.00 0.04 0.21 0.26 0.29
p_predicted_default[1236] 0.29 0.00 0.04 0.21 0.26 0.29
p_predicted_default[1237] 0.27 0.00 0.04 0.19 0.25 0.27
p_predicted_default[1238] 0.27 0.00 0.04 0.19 0.25 0.27
p_predicted_default[1239] 0.23 0.00 0.04 0.16 0.20 0.23
p_predicted_default[1240] 0.23 0.00 0.04 0.16 0.20 0.23
p_predicted_default[1241] 0.21 0.00 0.03 0.14 0.18 0.21
p_predicted_default[1242] 0.21 0.00 0.03 0.14 0.18 0.21
p_predicted_default[1243] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted_default[1244] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted_default[1245] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[1246] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[1247] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted_default[1248] 0.19 0.00 0.04 0.13 0.17 0.19
p_predicted_default[1249] 0.14 0.00 0.08 0.03 0.08 0.12
p_predicted_default[1250] 0.10 0.00 0.05 0.02 0.06 0.09
p_predicted_default[1251] 0.14 0.00 0.08 0.03 0.08 0.12
p_predicted_default[1252] 0.16 0.00 0.07 0.04 0.10 0.15
p_predicted_default[1253] 0.11 0.00 0.05 0.03 0.07 0.10
p_predicted_default[1254] 0.16 0.00 0.07 0.04 0.10 0.15
p_predicted_default[1255] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted_default[1256] 0.11 0.00 0.05 0.04 0.07 0.10
p_predicted_default[1257] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted_default[1258] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted_default[1259] 0.11 0.00 0.05 0.04 0.07 0.10
p_predicted_default[1260] 0.16 0.00 0.07 0.05 0.10 0.15
p_predicted_default[1261] 0.18 0.00 0.09 0.05 0.12 0.17
p_predicted_default[1262] 0.13 0.00 0.06 0.04 0.09 0.12
p_predicted_default[1263] 0.18 0.00 0.09 0.05 0.12 0.17
p_predicted_default[1264] 0.15 0.00 0.08 0.03 0.09 0.13
p_predicted_default[1265] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted_default[1266] 0.15 0.00 0.08 0.03 0.09 0.13
p_predicted_default[1267] 0.04 0.00 0.03 0.00 0.01 0.03
p_predicted_default[1268] 0.04 0.00 0.03 0.00 0.01 0.03
p_predicted_default[1269] 0.03 0.00 0.03 0.00 0.01 0.03
p_predicted_default[1270] 0.03 0.00 0.03 0.00 0.01 0.03
p_predicted_default[1271] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted_default[1272] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted_default[1273] 0.06 0.00 0.05 0.01 0.02 0.04
p_predicted_default[1274] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1275] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1276] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1277] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1278] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[1279] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[1280] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[1281] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[1282] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1283] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1284] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1285] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1286] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1287] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1288] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1289] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1290] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1291] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1292] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1293] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1294] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1295] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1296] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1297] 0.11 0.00 0.05 0.03 0.07 0.11
p_predicted_default[1298] 0.13 0.00 0.05 0.05 0.09 0.12
p_predicted_default[1299] 0.20 0.00 0.08 0.08 0.14 0.19
p_predicted_default[1300] 0.20 0.00 0.08 0.08 0.14 0.19
p_predicted_default[1301] 0.16 0.00 0.07 0.05 0.11 0.15
p_predicted_default[1302] 0.36 0.00 0.11 0.18 0.28 0.35
p_predicted_default[1303] 0.36 0.00 0.11 0.18 0.28 0.35
p_predicted_default[1304] 0.28 0.00 0.08 0.14 0.22 0.27
p_predicted_default[1305] 0.28 0.00 0.08 0.14 0.22 0.27
p_predicted_default[1306] 0.29 0.00 0.08 0.16 0.24 0.28
p_predicted_default[1307] 0.29 0.00 0.08 0.16 0.24 0.28
p_predicted_default[1308] 0.23 0.00 0.06 0.12 0.19 0.23
p_predicted_default[1309] 0.23 0.00 0.06 0.12 0.19 0.23
p_predicted_default[1310] 0.23 0.00 0.06 0.12 0.18 0.22
p_predicted_default[1311] 0.23 0.00 0.06 0.12 0.18 0.22
p_predicted_default[1312] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted_default[1313] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted_default[1314] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted_default[1315] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted_default[1316] 0.08 0.00 0.05 0.01 0.04 0.06
p_predicted_default[1317] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted_default[1318] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted_default[1319] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1320] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1321] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1322] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1323] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1324] 0.08 0.00 0.05 0.01 0.04 0.06
p_predicted_default[1325] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted_default[1326] 0.07 0.00 0.04 0.01 0.03 0.06
p_predicted_default[1327] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1328] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1329] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1330] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[1331] 0.27 0.00 0.07 0.15 0.22 0.27
p_predicted_default[1332] 0.22 0.00 0.06 0.12 0.18 0.22
p_predicted_default[1333] 0.21 0.00 0.06 0.10 0.16 0.20
p_predicted_default[1334] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted_default[1335] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted_default[1336] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1337] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1338] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1339] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1340] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1341] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1342] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[1343] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_intervention[1] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[2] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[3] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[4] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[5] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[6] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[7] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[8] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[9] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[10] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[11] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[12] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[13] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[14] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[15] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[16] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[17] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[18] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[19] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[20] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[21] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[22] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[23] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[24] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[25] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[26] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[27] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[28] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[29] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[30] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[31] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[32] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[33] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[34] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[35] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[36] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[37] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[38] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[39] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[40] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[41] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[42] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[43] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[44] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[45] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[46] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[47] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[48] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[49] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[50] 0.31 0.00 0.35 0.00 0.01 0.12
p_predicted_intervention[51] 0.33 0.00 0.36 0.00 0.02 0.15
p_predicted_intervention[52] 0.33 0.00 0.36 0.00 0.02 0.15
p_predicted_intervention[53] 0.33 0.00 0.36 0.00 0.01 0.14
p_predicted_intervention[54] 0.29 0.00 0.35 0.00 0.01 0.10
p_predicted_intervention[55] 0.29 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[56] 0.28 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[57] 0.27 0.00 0.34 0.00 0.01 0.08
p_predicted_intervention[58] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[59] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[60] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[61] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[62] 0.29 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[63] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[64] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[65] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[66] 0.26 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[67] 0.26 0.00 0.35 0.00 0.00 0.04
p_predicted_intervention[68] 0.26 0.00 0.35 0.00 0.00 0.04
p_predicted_intervention[69] 0.25 0.00 0.35 0.00 0.00 0.03
p_predicted_intervention[70] 0.25 0.00 0.35 0.00 0.00 0.03
p_predicted_intervention[71] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[72] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[73] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[74] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[75] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[76] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[77] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[78] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[79] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[80] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[81] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[82] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[83] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[84] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[85] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[86] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[87] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[88] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[89] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[90] 0.46 0.00 0.45 0.00 0.00 0.30
p_predicted_intervention[91] 0.46 0.00 0.45 0.00 0.00 0.29
p_predicted_intervention[92] 0.46 0.00 0.45 0.00 0.00 0.30
p_predicted_intervention[93] 0.46 0.00 0.45 0.00 0.00 0.31
p_predicted_intervention[94] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[95] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[96] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[97] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[98] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[99] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[100] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[101] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[102] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[103] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[104] 0.93 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[105] 0.93 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[106] 0.93 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[107] 0.93 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[108] 0.93 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[109] 0.93 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[110] 0.93 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[111] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[112] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[113] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[114] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[115] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[116] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[117] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[118] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[119] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[120] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[121] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[122] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[123] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[124] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[125] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[126] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[127] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[128] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[129] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[130] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[131] 0.27 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[132] 0.26 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[133] 0.26 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[134] 0.27 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[135] 0.27 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[136] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[137] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[138] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[139] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[140] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[141] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[142] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[143] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[144] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[145] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[146] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[147] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[148] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[149] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[150] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[151] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[152] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[153] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[154] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[155] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[156] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[157] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[158] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[159] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[160] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[161] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[162] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[163] 0.20 0.00 0.14 0.01 0.08 0.17
p_predicted_intervention[164] 0.20 0.00 0.14 0.01 0.08 0.17
p_predicted_intervention[165] 0.21 0.00 0.13 0.02 0.10 0.19
p_predicted_intervention[166] 0.21 0.00 0.13 0.02 0.10 0.19
p_predicted_intervention[167] 0.20 0.00 0.13 0.02 0.09 0.17
p_predicted_intervention[168] 0.20 0.00 0.13 0.02 0.09 0.17
p_predicted_intervention[169] 0.20 0.00 0.13 0.02 0.09 0.17
p_predicted_intervention[170] 0.20 0.00 0.13 0.02 0.09 0.17
p_predicted_intervention[171] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[172] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[173] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[174] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[175] 0.28 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[176] 0.28 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[177] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[178] 0.29 0.00 0.37 0.00 0.00 0.06
p_predicted_intervention[179] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[180] 0.29 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[181] 0.29 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[182] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[183] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[184] 0.26 0.00 0.35 0.00 0.00 0.04
p_predicted_intervention[185] 0.25 0.00 0.35 0.00 0.00 0.04
p_predicted_intervention[186] 0.25 0.00 0.35 0.00 0.00 0.04
p_predicted_intervention[187] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[188] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[189] 0.33 0.00 0.36 0.00 0.01 0.14
p_predicted_intervention[190] 0.32 0.00 0.36 0.00 0.01 0.14
p_predicted_intervention[191] 0.32 0.00 0.36 0.00 0.01 0.13
p_predicted_intervention[192] 0.32 0.00 0.36 0.00 0.01 0.14
p_predicted_intervention[193] 0.29 0.00 0.34 0.00 0.01 0.10
p_predicted_intervention[194] 0.28 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[195] 0.28 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[196] 0.28 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[197] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[198] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[199] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[200] 0.33 0.00 0.36 0.00 0.01 0.14
p_predicted_intervention[201] 0.32 0.00 0.36 0.00 0.01 0.13
p_predicted_intervention[202] 0.28 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[203] 0.28 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[204] 0.28 0.00 0.34 0.00 0.01 0.09
p_predicted_intervention[205] 0.20 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[206] 0.20 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[207] 0.20 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[208] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[209] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[210] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[211] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[212] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[213] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[214] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[215] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[216] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[217] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[218] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[219] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[220] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[221] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[222] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[223] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[224] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[225] 0.13 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[226] 0.43 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[227] 0.43 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[228] 0.43 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[229] 0.44 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[230] 0.43 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[231] 0.28 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[232] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[233] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[234] 0.26 0.00 0.35 0.00 0.00 0.04
p_predicted_intervention[235] 0.26 0.00 0.35 0.00 0.00 0.04
p_predicted_intervention[236] 0.20 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[237] 0.20 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[238] 0.20 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[239] 0.20 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[240] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[241] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[242] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[243] 0.92 0.00 0.24 0.02 1.00 1.00
p_predicted_intervention[244] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[245] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[246] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[247] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[248] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[249] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[250] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[251] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[252] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[253] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[254] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[255] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[256] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[257] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[258] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[259] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[260] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[261] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[262] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[263] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[264] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[265] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[266] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[267] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[268] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[269] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[270] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[271] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[272] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[273] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[274] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[275] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[276] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[277] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[278] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[279] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[280] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[281] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[282] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[283] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[284] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[285] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[286] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[287] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[288] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[289] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[290] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[291] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[292] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[293] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[294] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[295] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[296] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[297] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[298] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[299] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[300] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[301] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[302] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[303] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[304] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[305] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[306] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[307] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[308] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[309] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[310] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[311] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[312] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[313] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[314] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[315] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[316] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[317] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[318] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[319] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[320] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[321] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[322] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[323] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[324] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[325] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[326] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[327] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[328] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[329] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[330] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[331] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[332] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[333] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[334] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[335] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[336] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[337] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[338] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[339] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[340] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[341] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[342] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[343] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[344] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[345] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[346] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[347] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[348] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[349] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[350] 0.26 0.00 0.13 0.05 0.16 0.25
p_predicted_intervention[351] 0.26 0.00 0.13 0.05 0.16 0.25
p_predicted_intervention[352] 0.30 0.00 0.11 0.09 0.21 0.29
p_predicted_intervention[353] 0.30 0.00 0.11 0.09 0.21 0.29
p_predicted_intervention[354] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[355] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[356] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[357] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[358] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[359] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[360] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[361] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[362] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[363] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[364] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[365] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[366] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[367] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[368] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[369] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[370] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[371] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[372] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[373] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[374] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[375] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[376] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[377] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[378] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[379] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[380] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[381] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[382] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[383] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[384] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[385] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[386] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[387] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[388] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[389] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[390] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[391] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[392] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[393] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[394] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[395] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[396] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[397] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[398] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[399] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[400] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[401] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[402] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[403] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[404] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[405] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[406] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[407] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[408] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[409] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[410] 0.45 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[411] 0.45 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[412] 0.45 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[413] 0.44 0.00 0.45 0.00 0.00 0.23
p_predicted_intervention[414] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[415] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[416] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[417] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[418] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[419] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[420] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[421] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[422] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[423] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[424] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[425] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[426] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[427] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[428] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[429] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[430] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[431] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[432] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[433] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[434] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[435] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[436] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[437] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[438] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[439] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[440] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[441] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[442] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[443] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[444] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[445] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[446] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[447] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[448] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[449] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[450] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[451] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[452] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[453] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[454] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[455] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[456] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[457] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[458] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[459] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[460] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[461] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[462] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[463] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[464] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[465] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[466] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[467] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[468] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[469] 0.29 0.00 0.14 0.05 0.18 0.28
p_predicted_intervention[470] 0.23 0.00 0.14 0.03 0.12 0.21
p_predicted_intervention[471] 0.20 0.00 0.12 0.02 0.10 0.18
p_predicted_intervention[472] 0.21 0.00 0.13 0.02 0.11 0.19
p_predicted_intervention[473] 0.26 0.00 0.13 0.05 0.16 0.25
p_predicted_intervention[474] 0.27 0.00 0.13 0.05 0.16 0.26
p_predicted_intervention[475] 0.26 0.00 0.13 0.05 0.16 0.25
p_predicted_intervention[476] 0.25 0.00 0.13 0.05 0.15 0.24
p_predicted_intervention[477] 0.20 0.00 0.12 0.03 0.11 0.18
p_predicted_intervention[478] 0.18 0.00 0.11 0.03 0.10 0.16
p_predicted_intervention[479] 0.15 0.00 0.11 0.02 0.07 0.13
p_predicted_intervention[480] 0.14 0.00 0.10 0.02 0.06 0.12
p_predicted_intervention[481] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[482] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[483] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[484] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[485] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[486] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[487] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[488] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[489] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[490] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[491] 0.44 0.00 0.44 0.00 0.00 0.25
p_predicted_intervention[492] 0.44 0.00 0.44 0.00 0.00 0.25
p_predicted_intervention[493] 0.44 0.00 0.44 0.00 0.00 0.24
p_predicted_intervention[494] 0.44 0.00 0.44 0.00 0.00 0.24
p_predicted_intervention[495] 0.44 0.00 0.45 0.00 0.00 0.23
p_predicted_intervention[496] 0.44 0.00 0.45 0.00 0.00 0.23
p_predicted_intervention[497] 0.44 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[498] 0.44 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[499] 0.45 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[500] 0.45 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[501] 0.29 0.00 0.14 0.05 0.18 0.28
p_predicted_intervention[502] 0.23 0.00 0.14 0.03 0.12 0.21
p_predicted_intervention[503] 0.26 0.00 0.13 0.05 0.16 0.25
p_predicted_intervention[504] 0.26 0.00 0.13 0.05 0.16 0.25
p_predicted_intervention[505] 0.25 0.00 0.13 0.05 0.15 0.24
p_predicted_intervention[506] 0.25 0.00 0.13 0.05 0.15 0.24
p_predicted_intervention[507] 0.25 0.00 0.13 0.05 0.15 0.24
p_predicted_intervention[508] 0.21 0.00 0.12 0.03 0.11 0.19
p_predicted_intervention[509] 0.19 0.00 0.12 0.03 0.10 0.18
p_predicted_intervention[510] 0.16 0.00 0.11 0.02 0.08 0.14
p_predicted_intervention[511] 0.15 0.00 0.11 0.02 0.07 0.13
p_predicted_intervention[512] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[513] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[514] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[515] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[516] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[517] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[518] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[519] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[520] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[521] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[522] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[523] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[524] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_intervention[525] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[526] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[527] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[528] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[529] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[530] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[531] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[532] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[533] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[534] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[535] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[536] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[537] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[538] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[539] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[540] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[541] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[542] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[543] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[544] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[545] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[546] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[547] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[548] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[549] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[550] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[551] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[552] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[553] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[554] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[555] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[556] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[557] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[558] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[559] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[560] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[561] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[562] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[563] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[564] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[565] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[566] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[567] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[568] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[569] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[570] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[571] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[572] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[573] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[574] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[575] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[576] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[577] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[578] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[579] 0.44 0.00 0.45 0.00 0.00 0.23
p_predicted_intervention[580] 0.44 0.00 0.45 0.00 0.00 0.23
p_predicted_intervention[581] 0.45 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[582] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[583] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[584] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[585] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[586] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[587] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[588] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[589] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[590] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[591] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[592] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[593] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[594] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[595] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[596] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[597] 0.33 0.00 0.12 0.10 0.24 0.33
p_predicted_intervention[598] 0.25 0.00 0.12 0.05 0.16 0.24
p_predicted_intervention[599] 0.24 0.00 0.12 0.05 0.15 0.23
p_predicted_intervention[600] 0.24 0.00 0.12 0.04 0.14 0.23
p_predicted_intervention[601] 0.29 0.00 0.11 0.09 0.21 0.29
p_predicted_intervention[602] 0.31 0.00 0.12 0.10 0.22 0.30
p_predicted_intervention[603] 0.24 0.00 0.11 0.06 0.15 0.23
p_predicted_intervention[604] 0.23 0.00 0.11 0.06 0.15 0.22
p_predicted_intervention[605] 0.21 0.00 0.11 0.05 0.13 0.20
p_predicted_intervention[606] 0.17 0.00 0.10 0.03 0.09 0.15
p_predicted_intervention[607] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[608] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[609] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[610] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[611] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[612] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[613] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[614] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[615] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[616] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[617] 0.28 0.00 0.10 0.10 0.21 0.27
p_predicted_intervention[618] 0.28 0.00 0.10 0.10 0.21 0.27
p_predicted_intervention[619] 0.26 0.00 0.10 0.09 0.19 0.25
p_predicted_intervention[620] 0.31 0.00 0.44 0.00 0.00 0.00
p_predicted_intervention[621] 0.31 0.00 0.44 0.00 0.00 0.00
p_predicted_intervention[622] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[623] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[624] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[625] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[626] 0.44 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[627] 0.45 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[628] 0.45 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[629] 0.45 0.00 0.45 0.00 0.00 0.27
p_predicted_intervention[630] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[631] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[632] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[633] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[634] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[635] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[636] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[637] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[638] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[639] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[640] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[641] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[642] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[643] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[644] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[645] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[646] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[647] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[648] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[649] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[650] 0.29 0.00 0.33 0.00 0.02 0.13
p_predicted_intervention[651] 0.29 0.00 0.33 0.00 0.02 0.13
p_predicted_intervention[652] 0.29 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[653] 0.29 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[654] 0.28 0.00 0.33 0.00 0.02 0.11
p_predicted_intervention[655] 0.28 0.00 0.33 0.00 0.02 0.11
p_predicted_intervention[656] 0.31 0.00 0.38 0.00 0.00 0.09
p_predicted_intervention[657] 0.31 0.00 0.38 0.00 0.00 0.09
p_predicted_intervention[658] 0.31 0.00 0.38 0.00 0.00 0.09
p_predicted_intervention[659] 0.31 0.00 0.38 0.00 0.00 0.09
p_predicted_intervention[660] 0.28 0.00 0.36 0.00 0.00 0.06
p_predicted_intervention[661] 0.28 0.00 0.36 0.00 0.00 0.06
p_predicted_intervention[662] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[663] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[664] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[665] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[666] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[667] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[668] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[669] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[670] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[671] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[672] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[673] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[674] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[675] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[676] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[677] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[678] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[679] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[680] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[681] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[682] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[683] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[684] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[685] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[686] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[687] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[688] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[689] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[690] 0.45 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[691] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[692] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[693] 0.44 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[694] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[695] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[696] 0.44 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[697] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[698] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[699] 0.44 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[700] 0.44 0.00 0.44 0.00 0.00 0.26
p_predicted_intervention[701] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[702] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[703] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[704] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[705] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[706] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[707] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[708] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[709] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[710] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[711] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[712] 0.30 0.00 0.14 0.06 0.19 0.29
p_predicted_intervention[713] 0.23 0.00 0.13 0.03 0.13 0.22
p_predicted_intervention[714] 0.23 0.00 0.13 0.03 0.13 0.22
p_predicted_intervention[715] 0.23 0.00 0.13 0.03 0.12 0.21
p_predicted_intervention[716] 0.22 0.00 0.13 0.03 0.12 0.20
p_predicted_intervention[717] 0.28 0.00 0.13 0.06 0.18 0.27
p_predicted_intervention[718] 0.27 0.00 0.13 0.06 0.17 0.26
p_predicted_intervention[719] 0.25 0.00 0.13 0.04 0.15 0.23
p_predicted_intervention[720] 0.20 0.00 0.11 0.03 0.11 0.18
p_predicted_intervention[721] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[722] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[723] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[724] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[725] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[726] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[727] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[728] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[729] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[730] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[731] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[732] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[733] 0.51 0.01 0.41 0.00 0.04 0.52
p_predicted_intervention[734] 0.50 0.01 0.41 0.00 0.04 0.52
p_predicted_intervention[735] 0.51 0.01 0.41 0.00 0.04 0.53
p_predicted_intervention[736] 0.47 0.00 0.41 0.00 0.03 0.40
p_predicted_intervention[737] 0.47 0.00 0.41 0.00 0.03 0.40
p_predicted_intervention[738] 0.47 0.00 0.41 0.00 0.03 0.39
p_predicted_intervention[739] 0.47 0.00 0.41 0.00 0.03 0.39
p_predicted_intervention[740] 0.46 0.00 0.41 0.00 0.03 0.39
p_predicted_intervention[741] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[742] 0.92 0.00 0.23 0.02 1.00 1.00
p_predicted_intervention[743] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[744] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[745] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[746] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[747] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[748] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[749] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[750] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[751] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[752] 0.28 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[753] 0.28 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[754] 0.28 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[755] 0.28 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[756] 0.28 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[757] 0.28 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[758] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[759] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[760] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[761] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[762] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[763] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[764] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[765] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[766] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[767] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[768] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[769] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[770] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[771] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[772] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[773] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[774] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[775] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[776] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[777] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[778] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[779] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[780] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[781] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[782] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[783] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[784] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[785] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[786] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[787] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[788] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[789] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[790] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[791] 0.25 0.00 0.12 0.05 0.15 0.24
p_predicted_intervention[792] 0.24 0.00 0.12 0.04 0.15 0.23
p_predicted_intervention[793] 0.24 0.00 0.12 0.04 0.14 0.23
p_predicted_intervention[794] 0.24 0.00 0.12 0.05 0.15 0.23
p_predicted_intervention[795] 0.32 0.00 0.12 0.10 0.23 0.31
p_predicted_intervention[796] 0.31 0.00 0.12 0.10 0.22 0.31
p_predicted_intervention[797] 0.31 0.00 0.12 0.10 0.22 0.31
p_predicted_intervention[798] 0.31 0.00 0.12 0.10 0.22 0.30
p_predicted_intervention[799] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[800] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[801] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[802] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[803] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[804] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[805] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[806] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[807] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[808] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[809] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[810] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[811] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[812] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[813] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[814] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[815] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[816] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[817] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[818] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[819] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[820] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[821] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[822] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[823] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[824] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[825] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[826] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[827] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[828] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[829] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[830] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[831] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[832] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[833] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[834] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[835] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[836] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[837] 0.26 0.00 0.16 0.02 0.13 0.24
p_predicted_intervention[838] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[839] 0.20 0.00 0.15 0.01 0.08 0.17
p_predicted_intervention[840] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[841] 0.21 0.00 0.15 0.01 0.08 0.18
p_predicted_intervention[842] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[843] 0.20 0.00 0.15 0.01 0.08 0.17
p_predicted_intervention[844] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[845] 0.17 0.00 0.14 0.01 0.06 0.14
p_predicted_intervention[846] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[847] 0.17 0.00 0.14 0.01 0.06 0.14
p_predicted_intervention[848] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[849] 0.16 0.00 0.13 0.01 0.06 0.13
p_predicted_intervention[850] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[851] 0.21 0.00 0.14 0.02 0.09 0.18
p_predicted_intervention[852] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[853] 0.17 0.00 0.12 0.01 0.07 0.14
p_predicted_intervention[854] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[855] 0.16 0.00 0.12 0.01 0.06 0.13
p_predicted_intervention[856] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[857] 0.13 0.00 0.11 0.01 0.05 0.10
p_predicted_intervention[858] 0.25 0.00 0.12 0.05 0.15 0.24
p_predicted_intervention[859] 0.24 0.00 0.12 0.04 0.14 0.23
p_predicted_intervention[860] 0.23 0.00 0.11 0.04 0.14 0.22
p_predicted_intervention[861] 0.22 0.00 0.11 0.04 0.14 0.21
p_predicted_intervention[862] 0.21 0.00 0.11 0.04 0.13 0.20
p_predicted_intervention[863] 0.21 0.00 0.11 0.04 0.12 0.20
p_predicted_intervention[864] 0.27 0.00 0.11 0.08 0.19 0.27
p_predicted_intervention[865] 0.25 0.00 0.10 0.08 0.18 0.25
p_predicted_intervention[866] 0.24 0.00 0.10 0.07 0.17 0.23
p_predicted_intervention[867] 0.25 0.00 0.10 0.07 0.17 0.24
p_predicted_intervention[868] 0.25 0.00 0.10 0.08 0.18 0.25
p_predicted_intervention[869] 0.26 0.00 0.11 0.08 0.18 0.25
p_predicted_intervention[870] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[871] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[872] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[873] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[874] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[875] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[876] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[877] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[878] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[879] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[880] 0.28 0.00 0.36 0.00 0.00 0.06
p_predicted_intervention[881] 0.30 0.00 0.37 0.00 0.00 0.08
p_predicted_intervention[882] 0.28 0.00 0.36 0.00 0.00 0.06
p_predicted_intervention[883] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[884] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[885] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[886] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[887] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[888] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[889] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[890] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[891] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[892] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[893] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[894] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[895] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[896] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[897] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[898] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[899] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[900] 0.25 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[901] 0.30 0.00 0.14 0.06 0.19 0.29
p_predicted_intervention[902] 0.23 0.00 0.13 0.03 0.12 0.22
p_predicted_intervention[903] 0.25 0.00 0.13 0.04 0.15 0.23
p_predicted_intervention[904] 0.23 0.00 0.12 0.04 0.14 0.22
p_predicted_intervention[905] 0.21 0.00 0.12 0.04 0.12 0.20
p_predicted_intervention[906] 0.19 0.00 0.11 0.03 0.10 0.17
p_predicted_intervention[907] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[908] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[909] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[910] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[911] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[912] 0.45 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[913] 0.45 0.00 0.45 0.00 0.00 0.28
p_predicted_intervention[914] 0.45 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[915] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[916] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[917] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[918] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[919] 0.45 0.00 0.45 0.00 0.00 0.28
p_predicted_intervention[920] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[921] 0.45 0.00 0.44 0.00 0.00 0.31
p_predicted_intervention[922] 0.46 0.00 0.45 0.00 0.00 0.30
p_predicted_intervention[923] 0.45 0.00 0.44 0.00 0.00 0.31
p_predicted_intervention[924] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[925] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[926] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[927] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[928] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[929] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[930] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[931] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[932] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[933] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[934] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[935] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[936] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[937] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[938] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[939] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[940] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[941] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[942] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[943] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[944] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[945] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[946] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[947] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[948] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[949] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[950] 0.30 0.00 0.34 0.00 0.02 0.14
p_predicted_intervention[951] 0.30 0.00 0.34 0.00 0.02 0.14
p_predicted_intervention[952] 0.30 0.00 0.34 0.00 0.02 0.14
p_predicted_intervention[953] 0.27 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[954] 0.27 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[955] 0.27 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[956] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[957] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[958] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[959] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[960] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[961] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[962] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[963] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[964] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[965] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[966] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[967] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[968] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[969] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[970] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[971] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[972] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[973] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[974] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[975] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[976] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[977] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[978] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[979] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[980] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[981] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[982] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[983] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[984] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[985] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[986] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[987] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[988] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[989] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[990] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[991] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[992] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[993] 0.23 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[994] 0.23 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[995] 0.23 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[996] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[997] 0.07 0.00 0.22 0.00 0.00 0.00
p_predicted_intervention[998] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[999] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[1000] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[1001] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[1002] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[1003] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[1004] 0.44 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1005] 0.44 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[1006] 0.44 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[1007] 0.44 0.00 0.45 0.00 0.00 0.23
p_predicted_intervention[1008] 0.44 0.00 0.45 0.00 0.00 0.25
p_predicted_intervention[1009] 0.43 0.00 0.43 0.00 0.00 0.22
p_predicted_intervention[1010] 0.43 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[1011] 0.43 0.00 0.44 0.00 0.00 0.23
p_predicted_intervention[1012] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1013] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1014] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1015] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1016] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1017] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1018] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1019] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1020] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1021] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1022] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1023] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1024] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1025] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1026] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1027] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1028] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1029] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1030] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1031] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1032] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1033] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1034] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1035] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1036] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1037] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1038] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[1039] 0.30 0.00 0.36 0.00 0.01 0.09
p_predicted_intervention[1040] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[1041] 0.30 0.00 0.36 0.00 0.01 0.09
p_predicted_intervention[1042] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[1043] 0.30 0.00 0.36 0.00 0.01 0.09
p_predicted_intervention[1044] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[1045] 0.29 0.00 0.36 0.00 0.01 0.09
p_predicted_intervention[1046] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[1047] 0.29 0.00 0.36 0.00 0.01 0.08
p_predicted_intervention[1048] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[1049] 0.29 0.00 0.36 0.00 0.01 0.08
p_predicted_intervention[1050] 0.27 0.00 0.36 0.00 0.00 0.05
p_predicted_intervention[1051] 0.27 0.00 0.35 0.00 0.00 0.06
p_predicted_intervention[1052] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1053] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1054] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1055] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1056] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1057] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1058] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1059] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1060] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1061] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1062] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1063] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1064] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1065] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1066] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1067] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1068] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1069] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1070] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1071] 0.26 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[1072] 0.26 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[1073] 0.27 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[1074] 0.29 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[1075] 0.29 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[1076] 0.32 0.00 0.34 0.00 0.03 0.17
p_predicted_intervention[1077] 0.32 0.00 0.34 0.00 0.03 0.17
p_predicted_intervention[1078] 0.28 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[1079] 0.28 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[1080] 0.28 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[1081] 0.28 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[1082] 0.28 0.00 0.32 0.00 0.02 0.12
p_predicted_intervention[1083] 0.28 0.00 0.32 0.00 0.02 0.12
p_predicted_intervention[1084] 0.28 0.00 0.14 0.05 0.17 0.27
p_predicted_intervention[1085] 0.22 0.00 0.14 0.02 0.11 0.20
p_predicted_intervention[1086] 0.26 0.00 0.13 0.05 0.15 0.25
p_predicted_intervention[1087] 0.25 0.00 0.13 0.04 0.15 0.24
p_predicted_intervention[1088] 0.23 0.00 0.12 0.04 0.14 0.22
p_predicted_intervention[1089] 0.21 0.00 0.12 0.04 0.12 0.19
p_predicted_intervention[1090] 0.20 0.00 0.11 0.03 0.11 0.19
p_predicted_intervention[1091] 0.31 0.00 0.36 0.00 0.01 0.12
p_predicted_intervention[1092] 0.31 0.00 0.36 0.00 0.01 0.11
p_predicted_intervention[1093] 0.31 0.00 0.36 0.00 0.01 0.12
p_predicted_intervention[1094] 0.31 0.00 0.36 0.00 0.01 0.11
p_predicted_intervention[1095] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[1096] 0.31 0.00 0.36 0.00 0.01 0.11
p_predicted_intervention[1097] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[1098] 0.31 0.00 0.36 0.00 0.01 0.11
p_predicted_intervention[1099] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[1100] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[1101] 0.30 0.00 0.35 0.00 0.01 0.11
p_predicted_intervention[1102] 0.30 0.00 0.35 0.00 0.01 0.10
p_predicted_intervention[1103] 0.27 0.00 0.34 0.00 0.01 0.07
p_predicted_intervention[1104] 0.26 0.00 0.34 0.00 0.01 0.07
p_predicted_intervention[1105] 0.26 0.00 0.34 0.00 0.01 0.07
p_predicted_intervention[1106] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1107] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1108] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1109] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1110] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1111] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1112] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1113] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1114] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1115] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1116] 0.24 0.00 0.13 0.04 0.14 0.23
p_predicted_intervention[1117] 0.23 0.00 0.13 0.03 0.13 0.22
p_predicted_intervention[1118] 0.21 0.00 0.12 0.03 0.12 0.20
p_predicted_intervention[1119] 0.27 0.00 0.12 0.07 0.18 0.26
p_predicted_intervention[1120] 0.27 0.00 0.12 0.06 0.18 0.26
p_predicted_intervention[1121] 0.25 0.00 0.11 0.06 0.16 0.24
p_predicted_intervention[1122] 0.25 0.00 0.11 0.06 0.16 0.24
p_predicted_intervention[1123] 0.26 0.00 0.12 0.06 0.17 0.25
p_predicted_intervention[1124] 0.25 0.00 0.11 0.06 0.16 0.24
p_predicted_intervention[1125] 0.26 0.00 0.12 0.06 0.17 0.25
p_predicted_intervention[1126] 0.24 0.00 0.11 0.06 0.16 0.23
p_predicted_intervention[1127] 0.28 0.00 0.32 0.00 0.02 0.11
p_predicted_intervention[1128] 0.30 0.00 0.34 0.00 0.02 0.14
p_predicted_intervention[1129] 0.30 0.00 0.34 0.00 0.02 0.14
p_predicted_intervention[1130] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[1131] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[1132] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1133] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[1134] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[1135] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1136] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[1137] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1138] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[1139] 0.45 0.00 0.45 0.00 0.00 0.28
p_predicted_intervention[1140] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[1141] 0.45 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[1142] 0.45 0.00 0.45 0.00 0.00 0.27
p_predicted_intervention[1143] 0.45 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[1144] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[1145] 0.45 0.00 0.45 0.00 0.00 0.29
p_predicted_intervention[1146] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[1147] 0.44 0.00 0.44 0.00 0.00 0.25
p_predicted_intervention[1148] 0.44 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[1149] 0.44 0.00 0.44 0.00 0.00 0.25
p_predicted_intervention[1150] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[1151] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[1152] 0.44 0.00 0.44 0.00 0.00 0.28
p_predicted_intervention[1153] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1154] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[1155] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1156] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1157] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[1158] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1159] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1160] 0.93 0.00 0.21 0.05 1.00 1.00
p_predicted_intervention[1161] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1162] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1163] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1164] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1165] 0.44 0.00 0.44 0.00 0.00 0.25
p_predicted_intervention[1166] 0.44 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[1167] 0.44 0.00 0.44 0.00 0.00 0.25
p_predicted_intervention[1168] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1169] 0.45 0.00 0.45 0.00 0.00 0.26
p_predicted_intervention[1170] 0.44 0.00 0.44 0.00 0.00 0.27
p_predicted_intervention[1171] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[1172] 0.45 0.00 0.45 0.00 0.00 0.28
p_predicted_intervention[1173] 0.45 0.00 0.44 0.00 0.00 0.29
p_predicted_intervention[1174] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1175] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1176] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1177] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1178] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1179] 0.24 0.00 0.16 0.02 0.11 0.21
p_predicted_intervention[1180] 0.19 0.00 0.15 0.01 0.07 0.16
p_predicted_intervention[1181] 0.19 0.00 0.14 0.01 0.08 0.17
p_predicted_intervention[1182] 0.15 0.00 0.12 0.01 0.05 0.11
p_predicted_intervention[1183] 0.18 0.00 0.13 0.01 0.07 0.15
p_predicted_intervention[1184] 0.15 0.00 0.12 0.01 0.06 0.12
p_predicted_intervention[1185] 0.14 0.00 0.11 0.01 0.05 0.11
p_predicted_intervention[1186] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1187] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1188] 0.06 0.00 0.21 0.00 0.00 0.00
p_predicted_intervention[1189] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1190] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1191] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1192] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1193] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1194] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1195] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1196] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1197] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1198] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1199] 0.52 0.01 0.41 0.00 0.05 0.56
p_predicted_intervention[1200] 0.52 0.01 0.41 0.00 0.05 0.56
p_predicted_intervention[1201] 0.50 0.00 0.41 0.00 0.04 0.49
p_predicted_intervention[1202] 0.51 0.00 0.41 0.00 0.05 0.52
p_predicted_intervention[1203] 0.51 0.00 0.41 0.00 0.05 0.53
p_predicted_intervention[1204] 0.51 0.00 0.41 0.00 0.05 0.53
p_predicted_intervention[1205] 0.51 0.00 0.41 0.00 0.04 0.53
p_predicted_intervention[1206] 0.51 0.00 0.41 0.00 0.05 0.53
p_predicted_intervention[1207] 0.48 0.00 0.41 0.00 0.03 0.44
p_predicted_intervention[1208] 0.48 0.00 0.41 0.00 0.03 0.43
p_predicted_intervention[1209] 0.23 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1210] 0.23 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1211] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1212] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1213] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1214] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1215] 0.25 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1216] 0.24 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[1217] 0.29 0.00 0.33 0.00 0.02 0.13
p_predicted_intervention[1218] 0.29 0.00 0.33 0.00 0.02 0.13
p_predicted_intervention[1219] 0.28 0.00 0.33 0.00 0.02 0.11
p_predicted_intervention[1220] 0.28 0.00 0.33 0.00 0.02 0.11
p_predicted_intervention[1221] 0.28 0.00 0.32 0.00 0.02 0.11
p_predicted_intervention[1222] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1223] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1224] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1225] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1226] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1227] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1228] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[1229] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[1230] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[1231] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[1232] 0.13 0.00 0.32 0.00 0.00 0.00
p_predicted_intervention[1233] 0.27 0.00 0.12 0.06 0.18 0.26
p_predicted_intervention[1234] 0.27 0.00 0.12 0.06 0.18 0.26
p_predicted_intervention[1235] 0.25 0.00 0.11 0.06 0.16 0.24
p_predicted_intervention[1236] 0.25 0.00 0.11 0.06 0.16 0.24
p_predicted_intervention[1237] 0.24 0.00 0.11 0.05 0.15 0.23
p_predicted_intervention[1238] 0.24 0.00 0.11 0.05 0.15 0.23
p_predicted_intervention[1239] 0.31 0.00 0.10 0.12 0.24 0.31
p_predicted_intervention[1240] 0.31 0.00 0.10 0.12 0.24 0.31
p_predicted_intervention[1241] 0.29 0.00 0.10 0.11 0.22 0.28
p_predicted_intervention[1242] 0.29 0.00 0.10 0.11 0.22 0.28
p_predicted_intervention[1243] 0.27 0.00 0.10 0.10 0.20 0.27
p_predicted_intervention[1244] 0.27 0.00 0.10 0.10 0.20 0.27
p_predicted_intervention[1245] 0.28 0.00 0.10 0.10 0.20 0.27
p_predicted_intervention[1246] 0.28 0.00 0.10 0.10 0.20 0.27
p_predicted_intervention[1247] 0.27 0.00 0.10 0.10 0.20 0.26
p_predicted_intervention[1248] 0.27 0.00 0.10 0.10 0.20 0.26
p_predicted_intervention[1249] 0.43 0.00 0.43 0.00 0.00 0.24
p_predicted_intervention[1250] 0.44 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1251] 0.43 0.00 0.43 0.00 0.00 0.24
p_predicted_intervention[1252] 0.43 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1253] 0.44 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[1254] 0.43 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1255] 0.43 0.00 0.44 0.00 0.00 0.23
p_predicted_intervention[1256] 0.44 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[1257] 0.43 0.00 0.44 0.00 0.00 0.23
p_predicted_intervention[1258] 0.43 0.00 0.44 0.00 0.00 0.23
p_predicted_intervention[1259] 0.44 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1260] 0.43 0.00 0.44 0.00 0.00 0.23
p_predicted_intervention[1261] 0.43 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1262] 0.44 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1263] 0.43 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1264] 0.43 0.00 0.44 0.00 0.00 0.24
p_predicted_intervention[1265] 0.44 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1266] 0.43 0.00 0.44 0.00 0.00 0.24
p_predicted_intervention[1267] 0.93 0.00 0.22 0.03 1.00 1.00
p_predicted_intervention[1268] 0.93 0.00 0.22 0.03 1.00 1.00
p_predicted_intervention[1269] 0.93 0.00 0.22 0.03 1.00 1.00
p_predicted_intervention[1270] 0.93 0.00 0.22 0.03 1.00 1.00
p_predicted_intervention[1271] 0.93 0.00 0.22 0.03 1.00 1.00
p_predicted_intervention[1272] 0.43 0.00 0.44 0.00 0.00 0.19
p_predicted_intervention[1273] 0.43 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[1274] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1275] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1276] 0.93 0.00 0.22 0.03 1.00 1.00
p_predicted_intervention[1277] 0.92 0.00 0.23 0.03 1.00 1.00
p_predicted_intervention[1278] 0.31 0.00 0.37 0.00 0.00 0.09
p_predicted_intervention[1279] 0.31 0.00 0.37 0.00 0.00 0.09
p_predicted_intervention[1280] 0.28 0.00 0.36 0.00 0.00 0.06
p_predicted_intervention[1281] 0.28 0.00 0.36 0.00 0.00 0.06
p_predicted_intervention[1282] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1283] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1284] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1285] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1286] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1287] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1288] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1289] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[1290] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[1291] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[1292] 1.00 0.00 0.01 1.00 1.00 1.00
p_predicted_intervention[1293] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1294] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1295] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1296] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1297] 0.44 0.00 0.44 0.00 0.00 0.22
p_predicted_intervention[1298] 0.44 0.00 0.44 0.00 0.00 0.21
p_predicted_intervention[1299] 0.44 0.00 0.45 0.00 0.00 0.22
p_predicted_intervention[1300] 0.44 0.00 0.45 0.00 0.00 0.23
p_predicted_intervention[1301] 0.44 0.00 0.45 0.00 0.00 0.24
p_predicted_intervention[1302] 0.28 0.00 0.32 0.00 0.02 0.11
p_predicted_intervention[1303] 0.28 0.00 0.32 0.00 0.02 0.11
p_predicted_intervention[1304] 0.30 0.00 0.34 0.00 0.02 0.13
p_predicted_intervention[1305] 0.30 0.00 0.34 0.00 0.02 0.13
p_predicted_intervention[1306] 0.31 0.00 0.34 0.00 0.02 0.14
p_predicted_intervention[1307] 0.31 0.00 0.34 0.00 0.02 0.14
p_predicted_intervention[1308] 0.27 0.00 0.32 0.00 0.01 0.10
p_predicted_intervention[1309] 0.27 0.00 0.32 0.00 0.01 0.10
p_predicted_intervention[1310] 0.27 0.00 0.32 0.00 0.01 0.10
p_predicted_intervention[1311] 0.27 0.00 0.32 0.00 0.01 0.10
p_predicted_intervention[1312] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[1313] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[1314] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[1315] 0.26 0.00 0.32 0.00 0.01 0.09
p_predicted_intervention[1316] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1317] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1318] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1319] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1320] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1321] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1322] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1323] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1324] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1325] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1326] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1327] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1328] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1329] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1330] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1331] 0.32 0.00 0.34 0.00 0.02 0.17
p_predicted_intervention[1332] 0.28 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[1333] 0.28 0.00 0.32 0.00 0.02 0.11
p_predicted_intervention[1334] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1335] 0.93 0.00 0.22 0.04 1.00 1.00
p_predicted_intervention[1336] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1337] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1338] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1339] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1340] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1341] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1342] 0.93 0.00 0.22 0.05 1.00 1.00
p_predicted_intervention[1343] 0.93 0.00 0.22 0.04 1.00 1.00
predicted_difference[1] -0.19 0.00 0.16 -0.37 -0.26 -0.21
predicted_difference[2] -0.19 0.00 0.16 -0.36 -0.26 -0.21
predicted_difference[3] -0.19 0.00 0.16 -0.36 -0.26 -0.21
predicted_difference[4] -0.17 0.00 0.15 -0.33 -0.23 -0.18
predicted_difference[5] -0.17 0.00 0.15 -0.33 -0.23 -0.18
predicted_difference[6] -0.17 0.00 0.15 -0.33 -0.23 -0.18
predicted_difference[7] -0.15 0.00 0.15 -0.33 -0.21 -0.16
predicted_difference[8] -0.15 0.00 0.15 -0.33 -0.21 -0.16
predicted_difference[9] -0.37 0.00 0.08 -0.53 -0.43 -0.37
predicted_difference[10] -0.39 0.00 0.08 -0.55 -0.44 -0.39
predicted_difference[11] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[12] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[13] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[14] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[15] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[16] 0.98 0.00 0.02 0.94 0.97 0.99
predicted_difference[17] 0.98 0.00 0.02 0.94 0.98 0.99
predicted_difference[18] 0.98 0.00 0.02 0.94 0.98 0.99
predicted_difference[19] 0.98 0.00 0.02 0.94 0.98 0.99
predicted_difference[20] 0.98 0.00 0.02 0.94 0.98 0.99
predicted_difference[21] -0.06 0.00 0.13 -0.22 -0.11 -0.07
predicted_difference[22] -0.06 0.00 0.13 -0.21 -0.11 -0.07
predicted_difference[23] -0.38 0.00 0.05 -0.47 -0.41 -0.38
predicted_difference[24] -0.36 0.00 0.04 -0.45 -0.39 -0.36
predicted_difference[25] -0.32 0.00 0.05 -0.42 -0.36 -0.32
predicted_difference[26] -0.27 0.00 0.04 -0.35 -0.30 -0.27
predicted_difference[27] -0.26 0.00 0.04 -0.34 -0.29 -0.26
predicted_difference[28] -0.24 0.00 0.04 -0.33 -0.27 -0.24
predicted_difference[29] -0.23 0.00 0.05 -0.32 -0.26 -0.23
predicted_difference[30] -0.33 0.00 0.04 -0.42 -0.36 -0.33
predicted_difference[31] -0.33 0.00 0.04 -0.42 -0.36 -0.33
predicted_difference[32] -0.31 0.00 0.04 -0.39 -0.33 -0.31
predicted_difference[33] -0.31 0.00 0.04 -0.39 -0.33 -0.31
predicted_difference[34] -0.29 0.00 0.04 -0.37 -0.32 -0.29
predicted_difference[35] -0.29 0.00 0.04 -0.37 -0.32 -0.29
predicted_difference[36] -0.28 0.00 0.04 -0.37 -0.31 -0.28
predicted_difference[37] -0.28 0.00 0.04 -0.37 -0.31 -0.28
predicted_difference[38] -0.21 0.00 0.04 -0.28 -0.23 -0.20
predicted_difference[39] -0.21 0.00 0.04 -0.28 -0.23 -0.20
predicted_difference[40] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[41] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[42] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[43] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[44] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[45] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[46] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[47] -0.14 0.00 0.03 -0.19 -0.16 -0.14
predicted_difference[48] -0.14 0.00 0.03 -0.19 -0.15 -0.14
predicted_difference[49] -0.14 0.00 0.03 -0.19 -0.15 -0.14
predicted_difference[50] 0.19 0.00 0.37 -0.22 -0.09 0.02
predicted_difference[51] 0.25 0.00 0.37 -0.15 -0.06 0.07
predicted_difference[52] 0.25 0.00 0.37 -0.15 -0.06 0.07
predicted_difference[53] 0.25 0.00 0.37 -0.14 -0.06 0.07
predicted_difference[54] 0.23 0.00 0.35 -0.11 -0.05 0.04
predicted_difference[55] 0.23 0.00 0.35 -0.11 -0.05 0.04
predicted_difference[56] 0.22 0.00 0.35 -0.11 -0.04 0.04
predicted_difference[57] 0.22 0.00 0.34 -0.12 -0.04 0.03
predicted_difference[58] 0.21 0.00 0.36 -0.13 -0.05 0.00
predicted_difference[59] 0.21 0.00 0.36 -0.13 -0.05 0.00
predicted_difference[60] 0.21 0.00 0.36 -0.13 -0.05 0.00
predicted_difference[61] 0.25 0.00 0.37 -0.08 -0.04 0.02
predicted_difference[62] 0.25 0.00 0.37 -0.08 -0.04 0.02
predicted_difference[63] 0.23 0.00 0.36 -0.06 -0.03 0.01
predicted_difference[64] 0.23 0.00 0.36 -0.06 -0.03 0.01
predicted_difference[65] 0.23 0.00 0.36 -0.07 -0.03 0.01
predicted_difference[66] 0.23 0.00 0.36 -0.06 -0.03 0.01
predicted_difference[67] 0.23 0.00 0.36 -0.06 -0.02 0.01
predicted_difference[68] 0.23 0.00 0.35 -0.06 -0.02 0.01
predicted_difference[69] 0.21 0.00 0.35 -0.08 -0.02 0.01
predicted_difference[70] 0.22 0.00 0.34 -0.06 -0.02 0.01
predicted_difference[71] -0.20 0.00 0.06 -0.31 -0.23 -0.20
predicted_difference[72] -0.19 0.00 0.06 -0.30 -0.22 -0.19
predicted_difference[73] -0.18 0.00 0.06 -0.28 -0.21 -0.18
predicted_difference[74] -0.18 0.00 0.05 -0.28 -0.21 -0.17
predicted_difference[75] -0.12 0.00 0.06 -0.24 -0.16 -0.12
predicted_difference[76] -0.51 0.00 0.35 -0.83 -0.71 -0.63
predicted_difference[77] -0.51 0.00 0.35 -0.82 -0.70 -0.62
predicted_difference[78] -0.51 0.00 0.35 -0.82 -0.70 -0.62
predicted_difference[79] -0.51 0.00 0.35 -0.83 -0.71 -0.63
predicted_difference[80] -0.51 0.00 0.35 -0.82 -0.70 -0.62
predicted_difference[81] -0.50 0.00 0.35 -0.82 -0.70 -0.62
predicted_difference[82] -0.51 0.00 0.35 -0.83 -0.71 -0.63
predicted_difference[83] -0.51 0.00 0.35 -0.82 -0.70 -0.62
predicted_difference[84] -0.51 0.00 0.35 -0.82 -0.70 -0.62
predicted_difference[85] -0.56 0.00 0.09 -0.72 -0.62 -0.57
predicted_difference[86] -0.38 0.00 0.06 -0.50 -0.42 -0.38
predicted_difference[87] -0.32 0.00 0.06 -0.45 -0.36 -0.31
predicted_difference[88] -0.16 0.00 0.03 -0.23 -0.18 -0.15
predicted_difference[89] -0.15 0.00 0.03 -0.21 -0.17 -0.14
predicted_difference[90] 0.45 0.00 0.44 -0.01 0.00 0.29
predicted_difference[91] 0.45 0.00 0.44 -0.01 0.00 0.29
predicted_difference[92] 0.45 0.00 0.44 -0.01 0.00 0.29
predicted_difference[93] 0.46 0.00 0.45 0.00 0.00 0.31
predicted_difference[94] -0.50 0.00 0.36 -0.84 -0.71 -0.61
predicted_difference[95] -0.49 0.00 0.36 -0.83 -0.70 -0.60
predicted_difference[96] -0.45 0.00 0.35 -0.79 -0.65 -0.56
predicted_difference[97] -0.23 0.00 0.31 -0.59 -0.41 -0.31
predicted_difference[98] -0.23 0.00 0.31 -0.59 -0.40 -0.30
predicted_difference[99] -0.20 0.00 0.07 -0.35 -0.25 -0.20
predicted_difference[100] -0.20 0.00 0.07 -0.35 -0.25 -0.20
predicted_difference[101] -0.25 0.00 0.05 -0.36 -0.29 -0.25
predicted_difference[102] -0.21 0.00 0.04 -0.30 -0.24 -0.21
predicted_difference[103] -0.17 0.00 0.04 -0.25 -0.20 -0.17
predicted_difference[104] 0.92 0.00 0.23 0.00 0.98 0.99
predicted_difference[105] 0.92 0.00 0.23 0.00 0.98 0.99
predicted_difference[106] 0.92 0.00 0.23 0.00 0.98 0.99
predicted_difference[107] 0.92 0.00 0.23 0.00 0.98 0.99
predicted_difference[108] 0.92 0.00 0.23 0.00 0.98 0.99
predicted_difference[109] 0.92 0.00 0.23 0.00 0.98 0.99
predicted_difference[110] 0.92 0.00 0.23 0.00 0.98 0.99
predicted_difference[111] 0.92 0.00 0.24 0.00 0.98 0.99
predicted_difference[112] 0.92 0.00 0.24 0.00 0.98 0.99
predicted_difference[113] 0.92 0.00 0.24 0.00 0.98 0.99
predicted_difference[114] 0.92 0.00 0.24 0.00 0.98 1.00
predicted_difference[115] 0.92 0.00 0.24 0.00 0.98 1.00
predicted_difference[116] 0.92 0.00 0.24 0.00 0.98 1.00
predicted_difference[117] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[118] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[119] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[120] 1.00 0.00 0.01 0.98 1.00 1.00
predicted_difference[121] 1.00 0.00 0.01 0.98 1.00 1.00
predicted_difference[122] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[123] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[124] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[125] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[126] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[127] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[128] 0.34 0.00 0.46 -0.21 -0.09 0.16
predicted_difference[129] 0.38 0.00 0.47 -0.19 -0.07 0.19
predicted_difference[130] 0.34 0.00 0.46 -0.21 -0.09 0.16
predicted_difference[131] 0.26 0.00 0.42 -0.04 0.00 0.00
predicted_difference[132] 0.26 0.00 0.42 -0.04 0.00 0.00
predicted_difference[133] 0.26 0.00 0.42 -0.04 0.00 0.00
predicted_difference[134] 0.26 0.00 0.42 -0.04 0.00 0.00
predicted_difference[135] 0.26 0.00 0.42 -0.04 0.00 0.00
predicted_difference[136] -0.25 0.00 0.08 -0.40 -0.30 -0.25
predicted_difference[137] -0.31 0.00 0.06 -0.43 -0.34 -0.31
predicted_difference[138] -0.31 0.00 0.06 -0.42 -0.34 -0.30
predicted_difference[139] -0.31 0.00 0.06 -0.43 -0.35 -0.31
predicted_difference[140] -0.27 0.00 0.05 -0.38 -0.30 -0.27
predicted_difference[141] -0.25 0.00 0.05 -0.36 -0.28 -0.24
predicted_difference[142] -0.21 0.00 0.05 -0.29 -0.23 -0.21
predicted_difference[143] -0.20 0.00 0.04 -0.28 -0.23 -0.20
predicted_difference[144] -0.19 0.00 0.04 -0.28 -0.22 -0.19
predicted_difference[145] -0.20 0.00 0.04 -0.28 -0.22 -0.19
predicted_difference[146] -0.19 0.00 0.04 -0.27 -0.22 -0.19
predicted_difference[147] -0.19 0.00 0.04 -0.27 -0.22 -0.19
predicted_difference[148] -0.17 0.00 0.04 -0.26 -0.20 -0.17
predicted_difference[149] -0.14 0.00 0.05 -0.24 -0.17 -0.14
predicted_difference[150] -0.09 0.00 0.04 -0.19 -0.11 -0.08
predicted_difference[151] -0.12 0.00 0.04 -0.21 -0.14 -0.11
predicted_difference[152] -0.12 0.00 0.04 -0.21 -0.14 -0.11
predicted_difference[153] -0.08 0.00 0.03 -0.14 -0.09 -0.07
predicted_difference[154] -0.08 0.00 0.03 -0.14 -0.10 -0.08
predicted_difference[155] -0.09 0.00 0.03 -0.16 -0.11 -0.09
predicted_difference[156] -0.09 0.00 0.03 -0.16 -0.11 -0.09
predicted_difference[157] -0.08 0.00 0.03 -0.14 -0.09 -0.07
predicted_difference[158] -0.09 0.00 0.03 -0.15 -0.10 -0.08
predicted_difference[159] -0.07 0.00 0.02 -0.12 -0.08 -0.07
predicted_difference[160] -0.05 0.00 0.02 -0.09 -0.06 -0.05
predicted_difference[161] -0.04 0.00 0.02 -0.08 -0.05 -0.04
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predicted_difference[704] 0.39 0.00 0.44 -0.09 -0.04 0.21
predicted_difference[705] 0.41 0.00 0.45 -0.08 -0.03 0.22
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predicted_difference[893] 0.26 0.00 0.41 0.00 0.00 0.00
predicted_difference[894] 0.26 0.00 0.41 0.00 0.00 0.00
predicted_difference[895] 0.26 0.00 0.41 0.00 0.00 0.00
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predicted_difference[914] 0.43 0.00 0.44 -0.04 0.00 0.27
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predicted_difference[920] 0.43 0.00 0.44 -0.03 0.00 0.28
predicted_difference[921] 0.44 0.00 0.44 -0.03 0.00 0.29
predicted_difference[922] 0.45 0.00 0.45 -0.03 0.00 0.28
predicted_difference[923] 0.44 0.00 0.44 -0.03 0.00 0.29
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predicted_difference[936] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[937] 0.26 0.00 0.41 -0.01 0.00 0.00
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predicted_difference[940] 0.26 0.00 0.41 -0.01 0.00 0.00
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predicted_difference[942] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[943] 0.26 0.00 0.41 -0.01 0.00 0.00
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predicted_difference[948] -0.22 0.00 0.04 -0.31 -0.25 -0.22
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predicted_difference[951] 0.07 0.00 0.33 -0.31 -0.18 -0.08
predicted_difference[952] 0.07 0.00 0.33 -0.31 -0.18 -0.08
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predicted_difference[954] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[955] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[956] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[957] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[958] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[959] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[960] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[961] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[962] 0.08 0.00 0.31 -0.24 -0.14 -0.07
predicted_difference[963] 0.08 0.00 0.31 -0.24 -0.14 -0.07
predicted_difference[964] 0.08 0.00 0.31 -0.24 -0.14 -0.07
predicted_difference[965] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[966] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[967] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[968] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[969] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[970] 0.08 0.00 0.31 -0.23 -0.14 -0.07
predicted_difference[971] 0.09 0.00 0.31 -0.22 -0.13 -0.07
predicted_difference[972] 0.09 0.00 0.31 -0.22 -0.13 -0.07
predicted_difference[973] 0.09 0.00 0.31 -0.22 -0.13 -0.07
predicted_difference[974] 0.09 0.00 0.31 -0.22 -0.13 -0.06
predicted_difference[975] 0.09 0.00 0.31 -0.22 -0.13 -0.06
predicted_difference[976] 0.09 0.00 0.31 -0.22 -0.13 -0.06
predicted_difference[977] 0.25 0.00 0.41 -0.01 0.00 0.00
predicted_difference[978] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[979] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[980] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[981] 0.25 0.00 0.41 -0.01 0.00 0.00
predicted_difference[982] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[983] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[984] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[985] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[986] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[987] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[988] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[989] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[990] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[991] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[992] 0.26 0.00 0.41 -0.01 0.00 0.00
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predicted_difference[994] 0.22 0.00 0.40 -0.02 0.00 0.00
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predicted_difference[997] -0.87 0.00 0.28 -1.00 -0.99 -0.97
predicted_difference[998] -0.88 0.00 0.27 -1.00 -1.00 -0.98
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predicted_difference[1000] 0.25 0.00 0.41 -0.01 0.00 0.00
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predicted_difference[1002] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[1003] 0.26 0.00 0.41 -0.01 0.00 0.00
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predicted_difference[1006] 0.27 0.01 0.49 -0.36 -0.18 0.06
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predicted_difference[1008] 0.30 0.00 0.48 -0.32 -0.14 0.11
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predicted_difference[1031] 0.20 0.00 0.41 -0.11 -0.05 -0.03
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predicted_difference[1037] 0.20 0.00 0.40 -0.11 -0.04 -0.02
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predicted_difference[1040] 0.26 0.00 0.39 -0.11 -0.04 0.04
predicted_difference[1041] 0.26 0.00 0.37 -0.10 -0.03 0.05
predicted_difference[1042] 0.26 0.00 0.38 -0.10 -0.03 0.03
predicted_difference[1043] 0.25 0.00 0.37 -0.10 -0.03 0.05
predicted_difference[1044] 0.26 0.00 0.39 -0.10 -0.03 0.04
predicted_difference[1045] 0.25 0.00 0.37 -0.10 -0.03 0.05
predicted_difference[1046] 0.26 0.00 0.38 -0.10 -0.03 0.04
predicted_difference[1047] 0.25 0.00 0.37 -0.10 -0.03 0.05
predicted_difference[1048] 0.26 0.00 0.38 -0.10 -0.03 0.04
predicted_difference[1049] 0.25 0.00 0.37 -0.10 -0.03 0.05
predicted_difference[1050] 0.24 0.00 0.37 -0.08 -0.03 0.02
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predicted_difference[1053] 0.82 0.00 0.22 0.00 0.83 0.89
predicted_difference[1054] 0.83 0.00 0.22 0.00 0.84 0.90
predicted_difference[1055] 0.83 0.00 0.22 0.00 0.84 0.90
predicted_difference[1056] 0.83 0.00 0.22 0.00 0.85 0.90
predicted_difference[1057] 0.83 0.00 0.22 0.00 0.85 0.90
predicted_difference[1058] 0.84 0.00 0.22 0.00 0.85 0.90
predicted_difference[1059] 0.84 0.00 0.22 0.00 0.85 0.90
predicted_difference[1060] 0.84 0.00 0.22 0.00 0.85 0.91
predicted_difference[1061] 0.84 0.00 0.22 0.00 0.85 0.91
predicted_difference[1062] 0.83 0.00 0.22 0.00 0.83 0.89
predicted_difference[1063] 0.83 0.00 0.22 0.00 0.84 0.90
predicted_difference[1064] 0.83 0.00 0.22 0.00 0.84 0.90
predicted_difference[1065] 0.83 0.00 0.22 0.00 0.85 0.90
predicted_difference[1066] 0.84 0.00 0.22 0.00 0.85 0.90
predicted_difference[1067] 0.83 0.00 0.22 0.00 0.85 0.90
predicted_difference[1068] 0.84 0.00 0.22 0.00 0.85 0.90
predicted_difference[1069] 0.84 0.00 0.22 0.00 0.85 0.91
predicted_difference[1070] 0.84 0.00 0.22 0.00 0.85 0.91
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predicted_difference[1072] 0.26 0.00 0.42 -0.04 0.00 0.00
predicted_difference[1073] 0.26 0.00 0.42 -0.04 0.00 0.00
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predicted_difference[1075] 0.15 0.00 0.35 -0.22 -0.12 0.00
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predicted_difference[1077] 0.16 0.00 0.36 -0.26 -0.14 0.01
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predicted_difference[1079] 0.15 0.00 0.34 -0.21 -0.12 0.00
predicted_difference[1080] 0.15 0.00 0.34 -0.21 -0.12 0.00
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predicted_difference[1082] 0.15 0.00 0.34 -0.21 -0.12 0.00
predicted_difference[1083] 0.15 0.00 0.34 -0.21 -0.12 0.00
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predicted_difference[1087] 0.13 0.00 0.13 -0.07 0.03 0.12
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predicted_difference[1090] 0.11 0.00 0.11 -0.06 0.02 0.09
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predicted_difference[1092] 0.13 0.00 0.33 -0.22 -0.13 -0.05
predicted_difference[1093] 0.13 0.00 0.33 -0.22 -0.13 -0.05
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predicted_difference[1096] 0.13 0.00 0.33 -0.22 -0.13 -0.05
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predicted_difference[1100] 0.13 0.00 0.33 -0.21 -0.12 -0.05
predicted_difference[1101] 0.13 0.00 0.33 -0.21 -0.12 -0.05
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predicted_difference[1120] 0.11 0.00 0.11 -0.07 0.03 0.11
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predicted_difference[1123] 0.11 0.00 0.11 -0.07 0.03 0.10
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predicted_difference[1125] 0.11 0.00 0.11 -0.07 0.03 0.10
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predicted_difference[1136] 0.44 0.00 0.44 -0.01 0.00 0.25
predicted_difference[1137] 0.43 0.00 0.43 -0.01 0.00 0.26
predicted_difference[1138] 0.44 0.00 0.43 -0.01 0.00 0.28
predicted_difference[1139] 0.45 0.00 0.44 -0.01 0.00 0.27
predicted_difference[1140] 0.44 0.00 0.43 -0.01 0.00 0.28
predicted_difference[1141] 0.43 0.00 0.43 -0.02 0.00 0.26
predicted_difference[1142] 0.44 0.00 0.44 -0.01 0.00 0.26
predicted_difference[1143] 0.43 0.00 0.43 -0.02 0.00 0.26
predicted_difference[1144] 0.44 0.00 0.43 -0.01 0.00 0.28
predicted_difference[1145] 0.45 0.00 0.44 -0.01 0.00 0.28
predicted_difference[1146] 0.44 0.00 0.43 -0.01 0.00 0.28
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predicted_difference[1148] 0.39 0.00 0.45 -0.10 -0.04 0.18
predicted_difference[1149] 0.37 0.00 0.44 -0.12 -0.05 0.18
predicted_difference[1150] 0.39 0.00 0.44 -0.09 -0.04 0.21
predicted_difference[1151] 0.41 0.00 0.45 -0.08 -0.03 0.22
predicted_difference[1152] 0.39 0.00 0.44 -0.09 -0.04 0.21
predicted_difference[1153] 0.39 0.00 0.44 -0.09 -0.04 0.21
predicted_difference[1154] 0.41 0.00 0.45 -0.08 -0.03 0.22
predicted_difference[1155] 0.39 0.00 0.44 -0.09 -0.04 0.21
predicted_difference[1156] 0.35 0.00 0.43 -0.14 -0.07 0.17
predicted_difference[1157] 0.38 0.00 0.45 -0.12 -0.05 0.19
predicted_difference[1158] 0.35 0.00 0.43 -0.14 -0.07 0.17
predicted_difference[1159] 0.90 0.00 0.22 0.00 0.94 0.97
predicted_difference[1160] 0.90 0.00 0.22 0.01 0.95 0.98
predicted_difference[1161] 0.90 0.00 0.22 0.01 0.95 0.98
predicted_difference[1162] 0.91 0.00 0.22 0.01 0.96 0.98
predicted_difference[1163] 0.90 0.00 0.22 0.00 0.96 0.98
predicted_difference[1164] 0.91 0.00 0.22 0.00 0.96 0.98
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predicted_difference[1167] 0.38 0.00 0.45 -0.12 -0.05 0.20
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predicted_difference[1170] 0.40 0.00 0.44 -0.09 -0.03 0.23
predicted_difference[1171] 0.40 0.00 0.44 -0.10 -0.03 0.24
predicted_difference[1172] 0.42 0.00 0.46 -0.08 -0.03 0.24
predicted_difference[1173] 0.40 0.00 0.44 -0.10 -0.03 0.24
predicted_difference[1174] -0.74 0.00 0.25 -0.97 -0.89 -0.80
predicted_difference[1175] -0.73 0.00 0.25 -0.97 -0.88 -0.79
predicted_difference[1176] -0.71 0.00 0.25 -0.97 -0.87 -0.77
predicted_difference[1177] -0.70 0.00 0.25 -0.97 -0.86 -0.76
predicted_difference[1178] -0.71 0.00 0.25 -0.97 -0.87 -0.77
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predicted_difference[1182] 0.08 0.00 0.13 -0.10 -0.02 0.05
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predicted_difference[1184] 0.11 0.00 0.12 -0.04 0.02 0.08
predicted_difference[1185] 0.10 0.00 0.11 -0.04 0.02 0.07
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predicted_difference[1188] -0.43 0.00 0.23 -0.82 -0.59 -0.44
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predicted_difference[1197] 0.20 0.00 0.41 -0.11 -0.05 -0.03
predicted_difference[1198] 0.20 0.00 0.40 -0.11 -0.05 -0.02
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predicted_difference[1200] -0.06 0.01 0.44 -0.74 -0.49 -0.01
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predicted_difference[1202] -0.05 0.01 0.44 -0.72 -0.48 -0.02
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predicted_difference[1204] -0.04 0.01 0.44 -0.71 -0.47 -0.02
predicted_difference[1205] -0.04 0.01 0.44 -0.71 -0.47 -0.02
predicted_difference[1206] -0.05 0.01 0.44 -0.72 -0.47 -0.02
predicted_difference[1207] -0.02 0.00 0.44 -0.68 -0.43 -0.05
predicted_difference[1208] -0.01 0.00 0.44 -0.66 -0.42 -0.04
predicted_difference[1209] 0.23 0.00 0.40 -0.01 0.00 0.00
predicted_difference[1210] 0.23 0.00 0.40 -0.01 0.00 0.00
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predicted_difference[1212] 0.19 0.00 0.39 -0.13 -0.05 -0.03
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predicted_difference[1215] 0.20 0.00 0.39 -0.08 -0.04 -0.02
predicted_difference[1216] 0.20 0.00 0.39 -0.08 -0.03 -0.01
predicted_difference[1217] 0.03 0.00 0.34 -0.39 -0.23 -0.11
predicted_difference[1218] 0.03 0.00 0.34 -0.39 -0.23 -0.11
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predicted_difference[1220] 0.07 0.00 0.33 -0.30 -0.18 -0.09
predicted_difference[1221] 0.07 0.00 0.33 -0.30 -0.18 -0.08
predicted_difference[1222] -0.06 0.00 0.12 -0.20 -0.10 -0.06
predicted_difference[1223] -0.05 0.00 0.12 -0.20 -0.10 -0.06
predicted_difference[1224] -0.05 0.00 0.12 -0.20 -0.10 -0.06
predicted_difference[1225] -0.03 0.00 0.13 -0.14 -0.06 -0.04
predicted_difference[1226] -0.03 0.00 0.13 -0.14 -0.06 -0.04
predicted_difference[1227] -0.03 0.00 0.13 -0.14 -0.06 -0.04
predicted_difference[1228] -0.03 0.00 0.13 -0.14 -0.06 -0.04
predicted_difference[1229] -0.50 0.00 0.33 -0.85 -0.70 -0.59
predicted_difference[1230] -0.51 0.00 0.32 -0.84 -0.70 -0.61
predicted_difference[1231] -0.44 0.00 0.33 -0.81 -0.65 -0.54
predicted_difference[1232] -0.43 0.00 0.33 -0.81 -0.64 -0.53
predicted_difference[1233] -0.04 0.00 0.11 -0.23 -0.12 -0.04
predicted_difference[1234] -0.04 0.00 0.11 -0.23 -0.12 -0.04
predicted_difference[1235] -0.04 0.00 0.11 -0.22 -0.12 -0.04
predicted_difference[1236] -0.04 0.00 0.11 -0.22 -0.12 -0.04
predicted_difference[1237] -0.04 0.00 0.10 -0.21 -0.11 -0.04
predicted_difference[1238] -0.04 0.00 0.10 -0.21 -0.11 -0.04
predicted_difference[1239] 0.09 0.00 0.09 -0.07 0.02 0.08
predicted_difference[1240] 0.09 0.00 0.09 -0.07 0.02 0.08
predicted_difference[1241] 0.08 0.00 0.08 -0.06 0.02 0.08
predicted_difference[1242] 0.08 0.00 0.08 -0.06 0.02 0.08
predicted_difference[1243] 0.08 0.00 0.08 -0.06 0.02 0.07
predicted_difference[1244] 0.08 0.00 0.08 -0.06 0.02 0.07
predicted_difference[1245] 0.08 0.00 0.08 -0.06 0.02 0.07
predicted_difference[1246] 0.08 0.00 0.08 -0.06 0.02 0.07
predicted_difference[1247] 0.08 0.00 0.08 -0.06 0.02 0.07
predicted_difference[1248] 0.08 0.00 0.08 -0.06 0.02 0.07
predicted_difference[1249] 0.30 0.00 0.40 -0.16 -0.07 0.11
predicted_difference[1250] 0.34 0.00 0.43 -0.14 -0.05 0.13
predicted_difference[1251] 0.30 0.00 0.40 -0.16 -0.07 0.11
predicted_difference[1252] 0.27 0.00 0.39 -0.18 -0.09 0.08
predicted_difference[1253] 0.33 0.00 0.43 -0.14 -0.07 0.11
predicted_difference[1254] 0.27 0.00 0.39 -0.18 -0.09 0.08
predicted_difference[1255] 0.27 0.00 0.39 -0.18 -0.09 0.08
predicted_difference[1256] 0.33 0.00 0.43 -0.14 -0.07 0.11
predicted_difference[1257] 0.27 0.00 0.39 -0.18 -0.09 0.08
predicted_difference[1258] 0.27 0.00 0.39 -0.18 -0.09 0.08
predicted_difference[1259] 0.33 0.00 0.43 -0.15 -0.07 0.11
predicted_difference[1260] 0.27 0.00 0.39 -0.18 -0.09 0.08
predicted_difference[1261] 0.24 0.00 0.38 -0.21 -0.10 0.05
predicted_difference[1262] 0.31 0.00 0.42 -0.17 -0.08 0.08
predicted_difference[1263] 0.24 0.00 0.38 -0.21 -0.10 0.05
predicted_difference[1264] 0.29 0.00 0.39 -0.15 -0.07 0.11
predicted_difference[1265] 0.34 0.00 0.42 -0.13 -0.06 0.13
predicted_difference[1266] 0.29 0.00 0.39 -0.15 -0.07 0.11
predicted_difference[1267] 0.90 0.00 0.23 -0.02 0.94 0.97
predicted_difference[1268] 0.90 0.00 0.23 -0.02 0.94 0.97
predicted_difference[1269] 0.90 0.00 0.23 -0.02 0.94 0.97
predicted_difference[1270] 0.90 0.00 0.23 -0.02 0.94 0.97
predicted_difference[1271] 0.90 0.00 0.23 -0.02 0.95 0.97
predicted_difference[1272] 0.35 0.00 0.41 -0.07 -0.02 0.13
predicted_difference[1273] 0.37 0.00 0.41 -0.05 -0.01 0.16
predicted_difference[1274] 0.90 0.00 0.23 0.00 0.96 0.98
predicted_difference[1275] 0.90 0.00 0.23 0.00 0.96 0.98
predicted_difference[1276] 0.90 0.00 0.23 0.00 0.96 0.98
predicted_difference[1277] 0.90 0.00 0.23 0.00 0.96 0.98
predicted_difference[1278] 0.24 0.00 0.38 -0.12 -0.06 0.02
predicted_difference[1279] 0.24 0.00 0.38 -0.12 -0.06 0.02
predicted_difference[1280] 0.23 0.00 0.37 -0.09 -0.05 0.01
predicted_difference[1281] 0.23 0.00 0.37 -0.09 -0.05 0.01
predicted_difference[1282] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[1283] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[1284] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[1285] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[1286] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[1287] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[1288] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[1289] 0.99 0.00 0.01 0.98 0.99 1.00
predicted_difference[1290] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[1291] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[1292] 1.00 0.00 0.01 0.98 0.99 1.00
predicted_difference[1293] 0.90 0.00 0.22 0.01 0.95 0.98
predicted_difference[1294] 0.90 0.00 0.22 0.01 0.96 0.98
predicted_difference[1295] 0.90 0.00 0.22 0.01 0.96 0.98
predicted_difference[1296] 0.91 0.00 0.23 0.00 0.96 0.98
predicted_difference[1297] 0.33 0.00 0.46 -0.22 -0.10 0.11
predicted_difference[1298] 0.30 0.00 0.47 -0.24 -0.13 0.08
predicted_difference[1299] 0.24 0.00 0.47 -0.34 -0.19 0.03
predicted_difference[1300] 0.24 0.00 0.47 -0.36 -0.19 0.03
predicted_difference[1301] 0.29 0.00 0.46 -0.29 -0.13 0.09
predicted_difference[1302] -0.08 0.00 0.32 -0.52 -0.31 -0.19
predicted_difference[1303] -0.08 0.00 0.32 -0.52 -0.31 -0.19
predicted_difference[1304] 0.02 0.00 0.32 -0.36 -0.22 -0.12
predicted_difference[1305] 0.02 0.00 0.32 -0.36 -0.22 -0.12
predicted_difference[1306] 0.02 0.00 0.32 -0.36 -0.23 -0.13
predicted_difference[1307] 0.02 0.00 0.32 -0.36 -0.23 -0.13
predicted_difference[1308] 0.03 0.00 0.31 -0.29 -0.19 -0.11
predicted_difference[1309] 0.03 0.00 0.31 -0.29 -0.19 -0.11
predicted_difference[1310] 0.04 0.00 0.31 -0.28 -0.18 -0.11
predicted_difference[1311] 0.04 0.00 0.31 -0.28 -0.18 -0.11
predicted_difference[1312] 0.05 0.00 0.30 -0.26 -0.16 -0.10
predicted_difference[1313] 0.05 0.00 0.30 -0.26 -0.16 -0.10
predicted_difference[1314] 0.05 0.00 0.30 -0.25 -0.16 -0.10
predicted_difference[1315] 0.05 0.00 0.30 -0.25 -0.16 -0.10
predicted_difference[1316] 0.86 0.00 0.23 -0.03 0.87 0.93
predicted_difference[1317] 0.86 0.00 0.23 -0.02 0.89 0.94
predicted_difference[1318] 0.87 0.00 0.23 -0.02 0.89 0.94
predicted_difference[1319] 0.87 0.00 0.23 -0.02 0.90 0.94
predicted_difference[1320] 0.87 0.00 0.23 -0.02 0.90 0.94
predicted_difference[1321] 0.87 0.00 0.23 -0.02 0.90 0.94
predicted_difference[1322] 0.87 0.00 0.23 -0.02 0.90 0.95
predicted_difference[1323] 0.87 0.00 0.23 -0.02 0.90 0.95
predicted_difference[1324] 0.86 0.00 0.23 -0.03 0.87 0.93
predicted_difference[1325] 0.86 0.00 0.23 -0.02 0.89 0.94
predicted_difference[1326] 0.87 0.00 0.23 -0.02 0.89 0.94
predicted_difference[1327] 0.87 0.00 0.23 -0.02 0.90 0.94
predicted_difference[1328] 0.87 0.00 0.23 -0.02 0.90 0.94
predicted_difference[1329] 0.87 0.00 0.23 -0.02 0.90 0.94
predicted_difference[1330] 0.87 0.00 0.23 -0.02 0.90 0.95
predicted_difference[1331] 0.05 0.00 0.35 -0.37 -0.23 -0.10
predicted_difference[1332] 0.06 0.00 0.33 -0.31 -0.19 -0.09
predicted_difference[1333] 0.07 0.00 0.33 -0.30 -0.18 -0.08
predicted_difference[1334] 0.90 0.00 0.22 0.00 0.94 0.97
predicted_difference[1335] 0.90 0.00 0.22 0.00 0.95 0.97
predicted_difference[1336] 0.90 0.00 0.22 0.01 0.95 0.98
predicted_difference[1337] 0.90 0.00 0.22 0.01 0.95 0.98
predicted_difference[1338] 0.90 0.00 0.22 0.01 0.95 0.98
predicted_difference[1339] 0.90 0.00 0.22 0.01 0.95 0.98
predicted_difference[1340] 0.91 0.00 0.22 0.01 0.96 0.98
predicted_difference[1341] 0.91 0.00 0.22 0.01 0.96 0.98
predicted_difference[1342] 0.91 0.00 0.22 0.01 0.96 0.98
predicted_difference[1343] 0.90 0.00 0.23 0.00 0.96 0.98
lp__ -332.26 1.49 35.27 -400.64 -356.27 -332.64
75% 97.5% n_eff Rhat
mu[1] 0.01 0.07 10715 1.00
mu[2] 0.03 0.09 18058 1.00
mu[3] 0.03 0.10 16182 1.00
mu[4] -0.01 0.05 10873 1.00
mu[5] 0.00 0.06 7404 1.00
mu[6] 0.00 0.06 7602 1.00
mu[7] 0.02 0.08 8196 1.00
mu[8] 0.03 0.09 8765 1.00
mu[9] 0.03 0.09 13199 1.00
mu[10] 0.03 0.09 9486 1.00
mu[11] 0.04 0.10 10276 1.00
mu[12] 0.00 0.06 9725 1.00
sigma[1] 0.35 0.55 638 1.00
sigma[2] 1.04 1.31 2882 1.00
sigma[3] 0.77 1.04 1982 1.00
sigma[4] 0.36 0.51 1845 1.00
sigma[5] 0.23 0.39 867 1.01
sigma[6] 0.24 0.40 666 1.00
sigma[7] 0.23 0.38 779 1.00
sigma[8] 0.22 0.37 766 1.01
sigma[9] 0.41 0.65 582 1.01
sigma[10] 0.25 0.43 544 1.01
sigma[11] 0.29 0.49 551 1.01
sigma[12] 0.36 0.57 677 1.01
beta[1,1] 0.05 0.37 9066 1.00
beta[1,2] -0.14 0.39 8483 1.00
beta[1,3] 0.96 1.48 7083 1.00
beta[1,4] -0.38 -0.24 7191 1.00
beta[1,5] 0.10 0.39 6927 1.00
beta[1,6] 0.15 0.44 6029 1.00
beta[1,7] 0.17 0.43 6011 1.00
beta[1,8] 0.15 0.39 6231 1.00
beta[1,9] 0.53 1.24 1679 1.00
beta[1,10] 0.09 0.40 10766 1.00
beta[1,11] 0.14 0.49 10642 1.00
beta[1,12] -0.04 0.24 3475 1.00
beta[2,1] -0.21 0.02 1215 1.00
beta[2,2] -1.06 -0.72 4682 1.00
beta[2,3] 0.61 0.87 7252 1.00
beta[2,4] 0.38 0.71 4991 1.00
beta[2,5] 0.02 0.24 5798 1.00
beta[2,6] -0.01 0.21 4610 1.00
beta[2,7] 0.04 0.25 6419 1.00
beta[2,8] 0.14 0.40 6623 1.00
beta[2,9] -0.17 0.09 1075 1.01
beta[2,10] 0.11 0.49 12027 1.00
beta[2,11] 0.00 0.21 1907 1.00
beta[2,12] -0.16 0.08 1447 1.00
beta[3,1] 0.13 0.62 12260 1.00
beta[3,2] 0.48 1.68 17366 1.00
beta[3,3] 0.33 1.23 15895 1.00
beta[3,4] 0.00 0.35 9815 1.00
beta[3,5] 0.02 0.26 6149 1.00
beta[3,6] 0.03 0.28 7530 1.00
beta[3,7] 0.04 0.28 6650 1.00
beta[3,8] 0.05 0.28 7512 1.00
beta[3,9] 0.19 0.73 13777 1.00
beta[3,10] 0.11 0.47 13043 1.00
beta[3,11] 0.13 0.54 10910 1.00
beta[3,12] 0.13 0.63 13387 1.00
beta[4,1] 0.12 0.56 12720 1.00
beta[4,2] -0.04 0.68 13634 1.00
beta[4,3] -0.28 0.33 7149 1.00
beta[4,4] 0.22 0.58 9664 1.00
beta[4,5] 0.07 0.33 11536 1.00
beta[4,6] 0.04 0.28 7322 1.00
beta[4,7] 0.11 0.39 11201 1.00
beta[4,8] 0.17 0.47 6889 1.00
beta[4,9] 0.07 0.47 6613 1.00
beta[4,10] 0.11 0.45 12593 1.00
beta[4,11] 0.35 0.95 1730 1.00
beta[4,12] -0.01 0.32 3528 1.00
beta[5,1] 0.07 0.46 9932 1.00
beta[5,2] -0.77 0.20 6836 1.00
beta[5,3] 0.67 1.69 13393 1.00
beta[5,4] 0.18 0.51 9419 1.00
beta[5,5] 0.08 0.37 9987 1.00
beta[5,6] 0.06 0.31 7781 1.00
beta[5,7] 0.16 0.48 7046 1.00
beta[5,8] 0.18 0.52 4719 1.00
beta[5,9] 0.19 0.72 13846 1.00
beta[5,10] 0.10 0.44 10366 1.00
beta[5,11] 0.21 0.70 4859 1.00
beta[5,12] 0.01 0.37 4764 1.00
beta[6,1] 0.11 0.52 12623 1.00
beta[6,2] 3.50 4.82 4633 1.00
beta[6,3] 0.44 0.86 7594 1.00
beta[6,4] -0.28 0.01 4684 1.00
beta[6,5] -0.01 0.21 4154 1.00
beta[6,6] 0.05 0.31 8138 1.00
beta[6,7] 0.09 0.38 8766 1.00
beta[6,8] 0.14 0.45 6377 1.00
beta[6,9] 0.21 0.72 13286 1.00
beta[6,10] 0.11 0.46 11752 1.00
beta[6,11] 0.06 0.35 4346 1.00
beta[6,12] 0.21 0.71 7940 1.00
beta[7,1] 0.05 0.42 6783 1.00
beta[7,2] 0.17 0.85 10672 1.00
beta[7,3] 1.87 3.03 3550 1.00
beta[7,4] 0.08 0.42 8663 1.00
beta[7,5] 0.00 0.22 6178 1.00
beta[7,6] 0.01 0.22 4993 1.00
beta[7,7] 0.04 0.26 6144 1.00
beta[7,8] 0.05 0.27 5876 1.00
beta[7,9] 0.23 0.80 13090 1.00
beta[7,10] 0.11 0.45 12114 1.00
beta[7,11] 0.11 0.44 11395 1.00
beta[7,12] 0.10 0.52 13796 1.00
beta[8,1] 0.14 0.62 12389 1.00
beta[8,2] 0.60 1.88 15778 1.00
beta[8,3] 0.42 1.41 14503 1.00
beta[8,4] 0.15 0.63 16403 1.00
beta[8,5] 0.07 0.39 10728 1.00
beta[8,6] 0.09 0.44 11903 1.00
beta[8,7] 0.10 0.42 11984 1.00
beta[8,8] 0.11 0.43 12574 1.00
beta[8,9] 0.18 0.75 13906 1.00
beta[8,10] 0.11 0.45 12990 1.00
beta[8,11] 0.13 0.52 12317 1.00
beta[8,12] 0.13 0.61 12390 1.00
beta[9,1] 0.12 0.57 12798 1.00
beta[9,2] -0.09 0.94 11941 1.00
beta[9,3] -0.15 0.52 7824 1.00
beta[9,4] 0.18 0.55 8193 1.00
beta[9,5] 0.14 0.48 6798 1.00
beta[9,6] 0.21 0.57 2987 1.00
beta[9,7] 0.22 0.58 2913 1.00
beta[9,8] 0.19 0.55 4076 1.00
beta[9,9] 0.22 0.79 12585 1.00
beta[9,10] 0.11 0.47 12564 1.00
beta[9,11] 0.09 0.44 5830 1.00
beta[9,12] 0.16 0.65 11484 1.00
beta[10,1] 0.14 0.60 9477 1.00
beta[10,2] 0.35 1.48 14736 1.00
beta[10,3] 0.32 1.22 15226 1.00
beta[10,4] -0.03 0.31 8348 1.00
beta[10,5] 0.01 0.24 5835 1.00
beta[10,6] 0.02 0.25 5457 1.00
beta[10,7] 0.04 0.27 5433 1.00
beta[10,8] 0.05 0.27 6003 1.00
beta[10,9] 0.17 0.72 12171 1.00
beta[10,10] 0.11 0.46 10585 1.00
beta[10,11] 0.13 0.55 11180 1.00
beta[10,12] 0.14 0.61 11090 1.00
beta[11,1] 0.13 0.59 12005 1.00
beta[11,2] 0.43 1.63 14648 1.00
beta[11,3] 0.34 1.26 14895 1.00
beta[11,4] -0.08 0.24 8131 1.00
beta[11,5] 0.00 0.23 4133 1.00
beta[11,6] 0.01 0.24 3814 1.00
beta[11,7] 0.03 0.27 5187 1.00
beta[11,8] 0.05 0.27 5711 1.00
beta[11,9] 0.18 0.72 13678 1.00
beta[11,10] 0.11 0.45 12344 1.00
beta[11,11] 0.13 0.52 11520 1.00
beta[11,12] 0.14 0.63 13890 1.00
beta[12,1] 0.01 0.31 5371 1.00
beta[12,2] -0.13 0.80 11749 1.00
beta[12,3] 0.72 1.65 11757 1.00
beta[12,4] -0.03 0.26 8842 1.00
beta[12,5] 0.05 0.30 10169 1.00
beta[12,6] 0.12 0.42 8064 1.00
beta[12,7] 0.13 0.42 9868 1.00
beta[12,8] 0.15 0.46 8561 1.00
beta[12,9] 0.21 0.77 11619 1.00
beta[12,10] 0.11 0.47 11603 1.00
beta[12,11] 0.18 0.62 9231 1.00
beta[12,12] 0.04 0.42 6645 1.00
beta[13,1] 0.25 0.80 5796 1.00
beta[13,2] 1.87 2.69 5568 1.00
beta[13,3] -1.02 -0.46 3462 1.00
beta[13,4] 0.07 0.37 11350 1.00
beta[13,5] 0.04 0.30 8940 1.00
beta[13,6] 0.07 0.34 11574 1.00
beta[13,7] 0.11 0.40 11356 1.00
beta[13,8] 0.11 0.38 13561 1.00
beta[13,9] 0.11 0.55 11082 1.00
beta[13,10] 0.11 0.45 10950 1.00
beta[13,11] 0.24 0.74 3425 1.00
beta[13,12] -0.01 0.31 3809 1.00
beta[14,1] 0.14 0.62 13510 1.00
beta[14,2] 0.30 1.47 16134 1.00
beta[14,3] 0.25 1.10 14412 1.00
beta[14,4] 0.01 0.36 9865 1.00
beta[14,5] 0.02 0.27 6691 1.00
beta[14,6] 0.03 0.28 6210 1.00
beta[14,7] 0.05 0.29 5885 1.00
beta[14,8] 0.07 0.30 6243 1.00
beta[14,9] 0.18 0.73 14036 1.00
beta[14,10] 0.11 0.46 11993 1.00
beta[14,11] 0.14 0.54 12182 1.00
beta[14,12] 0.14 0.61 11473 1.00
beta[15,1] 0.14 0.57 12226 1.00
beta[15,2] 0.58 1.89 15238 1.00
beta[15,3] 0.42 1.33 15261 1.00
beta[15,4] 0.16 0.64 17472 1.00
beta[15,5] 0.08 0.38 12029 1.00
beta[15,6] 0.09 0.44 14032 1.00
beta[15,7] 0.10 0.42 13617 1.00
beta[15,8] 0.11 0.40 12142 1.00
beta[15,9] 0.18 0.71 13177 1.00
beta[15,10] 0.11 0.47 10330 1.00
beta[15,11] 0.14 0.56 12983 1.00
beta[15,12] 0.14 0.66 11426 1.00
beta[16,1] 0.14 0.62 13668 1.00
beta[16,2] 0.60 1.90 17002 1.00
beta[16,3] 0.43 1.40 17319 1.00
beta[16,4] 0.15 0.61 16222 1.00
beta[16,5] 0.07 0.39 10587 1.00
beta[16,6] 0.09 0.42 12017 1.00
beta[16,7] 0.10 0.44 12360 1.00
beta[16,8] 0.11 0.41 11787 1.00
beta[16,9] 0.18 0.75 14589 1.00
beta[16,10] 0.11 0.47 12036 1.00
beta[16,11] 0.14 0.55 13347 1.00
beta[16,12] 0.14 0.63 12584 1.00
beta[17,1] 0.13 0.59 11039 1.00
beta[17,2] 0.43 1.63 15229 1.00
beta[17,3] 0.34 1.28 15210 1.00
beta[17,4] -0.03 0.31 9408 1.00
beta[17,5] 0.02 0.25 6863 1.00
beta[17,6] 0.02 0.25 5013 1.00
beta[17,7] 0.04 0.26 5727 1.00
beta[17,8] 0.05 0.27 5150 1.00
beta[17,9] 0.18 0.74 12974 1.00
beta[17,10] 0.11 0.44 12960 1.00
beta[17,11] 0.14 0.53 11285 1.00
beta[17,12] 0.13 0.64 10869 1.00
beta[18,1] 0.13 0.62 11414 1.00
beta[18,2] 0.49 1.72 14231 1.00
beta[18,3] 0.36 1.33 17391 1.00
beta[18,4] 0.00 0.35 9838 1.00
beta[18,5] 0.03 0.26 8023 1.00
beta[18,6] 0.03 0.28 7663 1.00
beta[18,7] 0.06 0.31 7575 1.00
beta[18,8] 0.07 0.30 8218 1.00
beta[18,9] 0.18 0.72 12556 1.00
beta[18,10] 0.11 0.46 11149 1.00
beta[18,11] 0.14 0.56 10909 1.00
beta[18,12] 0.14 0.64 12515 1.00
beta[19,1] 0.14 0.60 11312 1.00
beta[19,2] 0.60 1.88 14824 1.00
beta[19,3] 0.43 1.42 15500 1.00
beta[19,4] 0.15 0.62 16133 1.00
beta[19,5] 0.07 0.38 11028 1.00
beta[19,6] 0.09 0.40 13577 1.00
beta[19,7] 0.10 0.41 13373 1.00
beta[19,8] 0.11 0.40 12737 1.00
beta[19,9] 0.19 0.74 13159 1.00
beta[19,10] 0.11 0.46 12985 1.00
beta[19,11] 0.14 0.54 11927 1.00
beta[19,12] 0.13 0.61 12493 1.00
beta[20,1] 0.14 0.64 12527 1.00
beta[20,2] 0.58 1.79 14600 1.00
beta[20,3] 0.42 1.34 14157 1.00
beta[20,4] 0.15 0.60 14624 1.00
beta[20,5] 0.07 0.39 12107 1.00
beta[20,6] 0.08 0.43 11456 1.00
beta[20,7] 0.10 0.42 14420 1.00
beta[20,8] 0.11 0.40 12973 1.00
beta[20,9] 0.18 0.74 13938 1.00
beta[20,10] 0.11 0.47 11705 1.00
beta[20,11] 0.14 0.55 12037 1.00
beta[20,12] 0.14 0.63 10747 1.00
beta[21,1] 0.14 0.60 12020 1.00
beta[21,2] 0.59 1.82 16537 1.00
beta[21,3] 0.43 1.35 16669 1.00
beta[21,4] 0.15 0.61 14566 1.00
beta[21,5] 0.08 0.39 8745 1.00
beta[21,6] 0.09 0.43 12785 1.00
beta[21,7] 0.10 0.42 11216 1.00
beta[21,8] 0.11 0.41 13303 1.00
beta[21,9] 0.19 0.70 13673 1.00
beta[21,10] 0.11 0.45 11517 1.00
beta[21,11] 0.14 0.56 12222 1.00
beta[21,12] 0.14 0.63 13741 1.00
beta[22,1] 0.14 0.63 12411 1.00
beta[22,2] 0.58 1.86 16164 1.00
beta[22,3] 0.43 1.40 16086 1.00
beta[22,4] 0.16 0.62 16735 1.00
beta[22,5] 0.07 0.39 11099 1.00
beta[22,6] 0.09 0.44 14591 1.00
beta[22,7] 0.10 0.42 9810 1.00
beta[22,8] 0.11 0.41 12341 1.00
beta[22,9] 0.18 0.74 14158 1.00
beta[22,10] 0.11 0.47 11335 1.00
beta[22,11] 0.14 0.54 12673 1.00
beta[22,12] 0.13 0.59 13421 1.00
mu_prior[1] 0.03 0.10 10042 1.00
mu_prior[2] 0.03 0.10 9705 1.00
mu_prior[3] 0.03 0.10 9157 1.00
mu_prior[4] 0.03 0.10 9624 1.00
mu_prior[5] 0.03 0.10 9532 1.00
mu_prior[6] 0.03 0.10 9869 1.00
mu_prior[7] 0.03 0.10 9627 1.00
mu_prior[8] 0.03 0.10 10070 1.00
mu_prior[9] 0.03 0.10 9363 1.00
mu_prior[10] 0.03 0.10 9753 1.00
mu_prior[11] 0.03 0.10 10068 1.00
mu_prior[12] 0.03 0.10 9961 1.00
sigma_prior[1] 0.26 0.44 9535 1.00
sigma_prior[2] 0.26 0.44 9783 1.00
sigma_prior[3] 0.25 0.44 10247 1.00
sigma_prior[4] 0.26 0.44 9757 1.00
sigma_prior[5] 0.26 0.43 9352 1.00
sigma_prior[6] 0.25 0.44 9706 1.00
sigma_prior[7] 0.26 0.44 9938 1.00
sigma_prior[8] 0.25 0.43 9889 1.00
sigma_prior[9] 0.25 0.45 10163 1.00
sigma_prior[10] 0.25 0.44 10090 1.00
sigma_prior[11] 0.25 0.44 9750 1.00
sigma_prior[12] 0.25 0.44 9493 1.00
p_prior[1] 0.99 1.00 10222 1.00
p_prior[2] 0.99 1.00 10216 1.00
p_prior[3] 0.99 1.00 10210 1.00
p_prior[4] 0.99 1.00 10214 1.00
p_prior[5] 0.99 1.00 10213 1.00
p_prior[6] 0.99 1.00 10212 1.00
p_prior[7] 0.99 1.00 10213 1.00
p_prior[8] 0.99 1.00 10208 1.00
p_prior[9] 0.99 1.00 9824 1.00
p_prior[10] 0.99 1.00 9829 1.00
p_prior[11] 0.98 1.00 9946 1.00
p_prior[12] 0.98 1.00 9910 1.00
p_prior[13] 0.98 1.00 9907 1.00
p_prior[14] 0.98 1.00 9906 1.00
p_prior[15] 0.98 1.00 9910 1.00
p_prior[16] 0.98 1.00 9904 1.00
p_prior[17] 0.98 1.00 9904 1.00
p_prior[18] 0.98 1.00 9904 1.00
p_prior[19] 0.98 1.00 9903 1.00
p_prior[20] 0.98 1.00 9905 1.00
p_prior[21] 0.99 1.00 10227 1.00
p_prior[22] 0.99 1.00 10208 1.00
p_prior[23] 0.99 1.00 9892 1.00
p_prior[24] 0.99 1.00 9884 1.00
p_prior[25] 0.99 1.00 9869 1.00
p_prior[26] 0.99 1.00 9827 1.00
p_prior[27] 0.99 1.00 9821 1.00
p_prior[28] 0.99 1.00 9813 1.00
p_prior[29] 0.99 1.00 9809 1.00
p_prior[30] 0.99 1.00 9900 1.00
p_prior[31] 0.99 1.00 9900 1.00
p_prior[32] 0.99 1.00 9893 1.00
p_prior[33] 0.99 1.00 9893 1.00
p_prior[34] 0.99 1.00 9886 1.00
p_prior[35] 0.99 1.00 9886 1.00
p_prior[36] 0.99 1.00 9882 1.00
p_prior[37] 0.99 1.00 9882 1.00
p_prior[38] 0.99 1.00 9898 1.00
p_prior[39] 0.99 1.00 9898 1.00
p_prior[40] 0.99 1.00 9847 1.00
p_prior[41] 0.99 1.00 9847 1.00
p_prior[42] 0.99 1.00 9848 1.00
p_prior[43] 0.99 1.00 9848 1.00
p_prior[44] 0.99 1.00 9847 1.00
p_prior[45] 0.99 1.00 9847 1.00
p_prior[46] 0.99 1.00 9847 1.00
p_prior[47] 0.99 1.00 9847 1.00
p_prior[48] 0.99 1.00 9845 1.00
p_prior[49] 0.99 1.00 9845 1.00
p_prior[50] 0.98 1.00 10201 1.00
p_prior[51] 0.98 1.00 10183 1.00
p_prior[52] 0.98 1.00 10183 1.00
p_prior[53] 0.98 1.00 10183 1.00
p_prior[54] 0.98 1.00 10153 1.00
p_prior[55] 0.98 1.00 10154 1.00
p_prior[56] 0.98 1.00 10155 1.00
p_prior[57] 0.98 1.00 10162 1.00
p_prior[58] 0.99 1.00 10182 1.00
p_prior[59] 0.99 1.00 10182 1.00
p_prior[60] 0.99 1.00 10182 1.00
p_prior[61] 0.99 1.00 10157 1.00
p_prior[62] 0.99 1.00 10154 1.00
p_prior[63] 0.99 1.00 10131 1.00
p_prior[64] 0.99 1.00 10131 1.00
p_prior[65] 0.99 1.00 10133 1.00
p_prior[66] 0.99 1.00 10132 1.00
p_prior[67] 0.99 1.00 10134 1.00
p_prior[68] 0.99 1.00 10135 1.00
p_prior[69] 0.99 1.00 10155 1.00
p_prior[70] 0.99 1.00 10144 1.00
p_prior[71] 0.99 1.00 9874 1.00
p_prior[72] 0.99 1.00 9870 1.00
p_prior[73] 0.99 1.00 9865 1.00
p_prior[74] 0.99 1.00 9865 1.00
p_prior[75] 0.99 1.00 9847 1.00
p_prior[76] 0.99 1.00 9719 1.00
p_prior[77] 0.99 1.00 9722 1.00
p_prior[78] 0.99 1.00 9723 1.00
p_prior[79] 0.99 1.00 9719 1.00
p_prior[80] 0.99 1.00 9722 1.00
p_prior[81] 0.99 1.00 9723 1.00
p_prior[82] 0.99 1.00 9720 1.00
p_prior[83] 0.99 1.00 9722 1.00
p_prior[84] 0.99 1.00 9723 1.00
p_prior[85] 0.99 1.00 9883 1.00
p_prior[86] 0.99 1.00 9888 1.00
p_prior[87] 0.99 1.00 9850 1.00
p_prior[88] 0.99 1.00 9861 1.00
p_prior[89] 0.99 1.00 9858 1.00
p_prior[90] 1.00 1.00 9945 1.00
p_prior[91] 1.00 1.00 9942 1.00
p_prior[92] 1.00 1.00 9938 1.00
p_prior[93] 1.00 1.00 9960 1.00
p_prior[94] 0.99 1.00 9705 1.00
p_prior[95] 0.99 1.00 9700 1.00
p_prior[96] 0.99 1.00 9699 1.00
p_prior[97] 0.99 1.00 9735 1.00
p_prior[98] 0.99 1.00 9740 1.00
p_prior[99] 0.99 1.00 9901 1.00
p_prior[100] 0.99 1.00 9902 1.00
p_prior[101] 0.99 1.00 9888 1.00
p_prior[102] 0.99 1.00 9870 1.00
p_prior[103] 0.99 1.00 9831 1.00
p_prior[104] 0.99 1.00 9592 1.00
p_prior[105] 0.99 1.00 9606 1.00
p_prior[106] 0.99 1.00 9606 1.00
p_prior[107] 0.99 1.00 9607 1.00
p_prior[108] 0.99 1.00 9607 1.00
p_prior[109] 0.99 1.00 9609 1.00
p_prior[110] 0.99 1.00 9609 1.00
p_prior[111] 0.99 1.00 9645 1.00
p_prior[112] 0.99 1.00 9645 1.00
p_prior[113] 0.99 1.00 9645 1.00
p_prior[114] 0.99 1.00 9645 1.00
p_prior[115] 0.99 1.00 9646 1.00
p_prior[116] 0.99 1.00 9646 1.00
p_prior[117] 0.98 1.00 9936 1.00
p_prior[118] 0.98 1.00 9936 1.00
p_prior[119] 0.98 1.00 9936 1.00
p_prior[120] 0.98 1.00 9943 1.00
p_prior[121] 0.98 1.00 9943 1.00
p_prior[122] 0.98 1.00 9908 1.00
p_prior[123] 0.98 1.00 9907 1.00
p_prior[124] 0.98 1.00 9907 1.00
p_prior[125] 0.98 1.00 9908 1.00
p_prior[126] 0.98 1.00 9908 1.00
p_prior[127] 0.98 1.00 9908 1.00
p_prior[128] 0.99 1.00 10033 1.00
p_prior[129] 1.00 1.00 10032 1.00
p_prior[130] 0.99 1.00 10033 1.00
p_prior[131] 0.99 1.00 9455 1.00
p_prior[132] 0.99 1.00 9542 1.00
p_prior[133] 0.99 1.00 9526 1.00
p_prior[134] 0.99 1.00 9467 1.00
p_prior[135] 0.99 1.00 9467 1.00
p_prior[136] 0.99 1.00 9889 1.00
p_prior[137] 0.99 1.00 9878 1.00
p_prior[138] 0.99 1.00 9877 1.00
p_prior[139] 0.99 1.00 9880 1.00
p_prior[140] 0.99 1.00 9864 1.00
p_prior[141] 0.99 1.00 9854 1.00
p_prior[142] 0.99 1.00 9815 1.00
p_prior[143] 0.99 1.00 9812 1.00
p_prior[144] 0.99 1.00 9810 1.00
p_prior[145] 0.99 1.00 9811 1.00
p_prior[146] 0.99 1.00 9809 1.00
p_prior[147] 0.99 1.00 9809 1.00
p_prior[148] 0.99 1.00 9801 1.00
p_prior[149] 0.99 1.00 9792 1.00
p_prior[150] 0.99 1.00 9944 1.00
p_prior[151] 0.99 1.00 9927 1.00
p_prior[152] 0.99 1.00 9927 1.00
p_prior[153] 0.99 1.00 9885 1.00
p_prior[154] 0.99 1.00 9887 1.00
p_prior[155] 0.99 1.00 9910 1.00
p_prior[156] 0.99 1.00 9907 1.00
p_prior[157] 0.99 1.00 9885 1.00
p_prior[158] 0.99 1.00 9904 1.00
p_prior[159] 0.99 1.00 9879 1.00
p_prior[160] 0.99 1.00 9865 1.00
p_prior[161] 0.99 1.00 9861 1.00
p_prior[162] 0.99 1.00 9858 1.00
p_prior[163] 0.62 0.84 9504 1.00
p_prior[164] 0.62 0.84 9504 1.00
p_prior[165] 0.62 0.84 9505 1.00
p_prior[166] 0.62 0.84 9505 1.00
p_prior[167] 0.63 0.84 9505 1.00
p_prior[168] 0.63 0.84 9505 1.00
p_prior[169] 0.63 0.85 9516 1.00
p_prior[170] 0.63 0.85 9516 1.00
p_prior[171] 0.99 1.00 9893 1.00
p_prior[172] 0.99 1.00 9881 1.00
p_prior[173] 0.99 1.00 9822 1.00
p_prior[174] 0.99 1.00 9813 1.00
p_prior[175] 0.99 1.00 10182 1.00
p_prior[176] 0.99 1.00 10182 1.00
p_prior[177] 0.99 1.00 10157 1.00
p_prior[178] 0.99 1.00 10153 1.00
p_prior[179] 0.99 1.00 10157 1.00
p_prior[180] 0.99 1.00 10155 1.00
p_prior[181] 0.99 1.00 10154 1.00
p_prior[182] 0.99 1.00 10132 1.00
p_prior[183] 0.99 1.00 10132 1.00
p_prior[184] 0.99 1.00 10131 1.00
p_prior[185] 0.99 1.00 10133 1.00
p_prior[186] 0.99 1.00 10133 1.00
p_prior[187] 0.98 1.00 10204 1.00
p_prior[188] 0.98 1.00 10204 1.00
p_prior[189] 0.98 1.00 10185 1.00
p_prior[190] 0.98 1.00 10184 1.00
p_prior[191] 0.98 1.00 10184 1.00
p_prior[192] 0.98 1.00 10184 1.00
p_prior[193] 0.98 1.00 10151 1.00
p_prior[194] 0.98 1.00 10153 1.00
p_prior[195] 0.98 1.00 10153 1.00
p_prior[196] 0.98 1.00 10153 1.00
p_prior[197] 0.98 1.00 10204 1.00
p_prior[198] 0.98 1.00 10204 1.00
p_prior[199] 0.98 1.00 10204 1.00
p_prior[200] 0.98 1.00 10185 1.00
p_prior[201] 0.98 1.00 10184 1.00
p_prior[202] 0.98 1.00 10154 1.00
p_prior[203] 0.98 1.00 10155 1.00
p_prior[204] 0.98 1.00 10153 1.00
p_prior[205] 0.99 1.00 9580 1.00
p_prior[206] 0.99 1.00 9572 1.00
p_prior[207] 0.99 1.00 9573 1.00
p_prior[208] 0.99 1.00 9906 1.00
p_prior[209] 0.99 1.00 9861 1.00
p_prior[210] 0.99 1.00 9884 1.00
p_prior[211] 0.99 1.00 9884 1.00
p_prior[212] 0.99 1.00 9891 1.00
p_prior[213] 0.99 1.00 9857 1.00
p_prior[214] 0.99 1.00 9854 1.00
p_prior[215] 0.99 1.00 9855 1.00
p_prior[216] 0.99 1.00 9705 1.00
p_prior[217] 0.99 1.00 9721 1.00
p_prior[218] 0.99 1.00 9725 1.00
p_prior[219] 0.99 1.00 9739 1.00
p_prior[220] 0.99 1.00 9743 1.00
p_prior[221] 0.99 1.00 9721 1.00
p_prior[222] 0.99 1.00 9727 1.00
p_prior[223] 0.99 1.00 9740 1.00
p_prior[224] 0.99 1.00 9743 1.00
p_prior[225] 0.99 1.00 9743 1.00
p_prior[226] 1.00 1.00 9962 1.00
p_prior[227] 1.00 1.00 9941 1.00
p_prior[228] 1.00 1.00 9942 1.00
p_prior[229] 1.00 1.00 9937 1.00
p_prior[230] 1.00 1.00 9940 1.00
p_prior[231] 0.99 1.00 10185 1.00
p_prior[232] 0.99 1.00 10159 1.00
p_prior[233] 0.99 1.00 10134 1.00
p_prior[234] 0.99 1.00 10136 1.00
p_prior[235] 0.99 1.00 10136 1.00
p_prior[236] 0.99 1.00 9574 1.00
p_prior[237] 0.99 1.00 9574 1.00
p_prior[238] 0.99 1.00 9573 1.00
p_prior[239] 0.99 1.00 9573 1.00
p_prior[240] 0.99 1.00 9614 1.00
p_prior[241] 0.99 1.00 9611 1.00
p_prior[242] 0.99 1.00 9611 1.00
p_prior[243] 0.99 1.00 9664 1.00
p_prior[244] 0.99 1.00 9903 1.00
p_prior[245] 0.99 1.00 9903 1.00
p_prior[246] 0.99 1.00 9903 1.00
p_prior[247] 0.99 1.00 9903 1.00
p_prior[248] 0.99 1.00 9903 1.00
p_prior[249] 0.99 1.00 9903 1.00
p_prior[250] 0.99 1.00 9890 1.00
p_prior[251] 0.99 1.00 9890 1.00
p_prior[252] 0.99 1.00 9890 1.00
p_prior[253] 0.99 1.00 9885 1.00
p_prior[254] 0.99 1.00 9885 1.00
p_prior[255] 0.99 1.00 9885 1.00
p_prior[256] 0.99 1.00 9882 1.00
p_prior[257] 0.99 1.00 9882 1.00
p_prior[258] 0.99 1.00 9882 1.00
p_prior[259] 0.99 1.00 9880 1.00
p_prior[260] 0.99 1.00 9880 1.00
p_prior[261] 0.99 1.00 9880 1.00
p_prior[262] 0.99 1.00 9836 1.00
p_prior[263] 0.99 1.00 9836 1.00
p_prior[264] 0.99 1.00 9836 1.00
p_prior[265] 0.99 1.00 9826 1.00
p_prior[266] 0.99 1.00 9826 1.00
p_prior[267] 0.99 1.00 9826 1.00
p_prior[268] 0.99 1.00 9825 1.00
p_prior[269] 0.99 1.00 9825 1.00
p_prior[270] 0.99 1.00 9825 1.00
p_prior[271] 0.99 1.00 9838 1.00
p_prior[272] 0.99 1.00 9838 1.00
p_prior[273] 0.99 1.00 9838 1.00
p_prior[274] 0.99 1.00 9836 1.00
p_prior[275] 0.99 1.00 9836 1.00
p_prior[276] 0.99 1.00 9836 1.00
p_prior[277] 0.99 1.00 10004 1.00
p_prior[278] 0.99 1.00 10004 1.00
p_prior[279] 0.99 1.00 10004 1.00
p_prior[280] 0.99 1.00 10004 1.00
p_prior[281] 0.99 1.00 10004 1.00
p_prior[282] 0.99 1.00 10004 1.00
p_prior[283] 0.99 1.00 10004 1.00
p_prior[284] 0.99 1.00 10004 1.00
p_prior[285] 0.99 1.00 9982 1.00
p_prior[286] 0.99 1.00 9982 1.00
p_prior[287] 0.99 1.00 9963 1.00
p_prior[288] 0.99 1.00 9963 1.00
p_prior[289] 0.99 1.00 9984 1.00
p_prior[290] 0.99 1.00 9984 1.00
p_prior[291] 0.99 1.00 9984 1.00
p_prior[292] 0.99 1.00 9984 1.00
p_prior[293] 0.99 1.00 9990 1.00
p_prior[294] 0.99 1.00 9990 1.00
p_prior[295] 0.99 1.00 9990 1.00
p_prior[296] 0.99 1.00 9990 1.00
p_prior[297] 0.99 1.00 9990 1.00
p_prior[298] 0.99 1.00 9990 1.00
p_prior[299] 0.99 1.00 9991 1.00
p_prior[300] 0.99 1.00 9991 1.00
p_prior[301] 0.99 1.00 9991 1.00
p_prior[302] 0.99 1.00 9991 1.00
p_prior[303] 0.99 1.00 9990 1.00
p_prior[304] 0.99 1.00 9990 1.00
p_prior[305] 0.99 1.00 9990 1.00
p_prior[306] 0.99 1.00 9990 1.00
p_prior[307] 0.99 1.00 9990 1.00
p_prior[308] 0.99 1.00 9990 1.00
p_prior[309] 0.99 1.00 9991 1.00
p_prior[310] 0.99 1.00 9991 1.00
p_prior[311] 0.99 1.00 9991 1.00
p_prior[312] 0.99 1.00 9991 1.00
p_prior[313] 0.99 1.00 9991 1.00
p_prior[314] 0.99 1.00 9991 1.00
p_prior[315] 0.99 1.00 9992 1.00
p_prior[316] 0.99 1.00 9992 1.00
p_prior[317] 0.99 1.00 9992 1.00
p_prior[318] 0.99 1.00 9992 1.00
p_prior[319] 0.99 1.00 9992 1.00
p_prior[320] 0.99 1.00 9992 1.00
p_prior[321] 0.99 1.00 9968 1.00
p_prior[322] 0.99 1.00 9968 1.00
p_prior[323] 0.99 1.00 9968 1.00
p_prior[324] 0.99 1.00 9968 1.00
p_prior[325] 0.99 1.00 9918 1.00
p_prior[326] 0.99 1.00 9936 1.00
p_prior[327] 0.98 1.00 9905 1.00
p_prior[328] 0.99 1.00 9929 1.00
p_prior[329] 0.98 1.00 9896 1.00
p_prior[330] 0.99 1.00 9929 1.00
p_prior[331] 0.98 1.00 9896 1.00
p_prior[332] 0.99 1.00 9903 1.00
p_prior[333] 0.99 1.00 9878 1.00
p_prior[334] 0.99 1.00 9899 1.00
p_prior[335] 0.99 1.00 9874 1.00
p_prior[336] 0.99 1.00 9902 1.00
p_prior[337] 0.99 1.00 9876 1.00
p_prior[338] 0.99 1.00 9976 1.00
p_prior[339] 0.99 1.00 9956 1.00
p_prior[340] 0.99 1.00 9950 1.00
p_prior[341] 0.99 1.00 9952 1.00
p_prior[342] 0.99 1.00 9954 1.00
p_prior[343] 0.99 1.00 9953 1.00
p_prior[344] 0.99 1.00 9957 1.00
p_prior[345] 0.99 1.00 9922 1.00
p_prior[346] 0.99 1.00 9850 1.00
p_prior[347] 0.99 1.00 9850 1.00
p_prior[348] 0.99 1.00 9890 1.00
p_prior[349] 0.99 1.00 9890 1.00
p_prior[350] 0.57 0.72 9545 1.00
p_prior[351] 0.57 0.72 9545 1.00
p_prior[352] 0.57 0.72 9598 1.00
p_prior[353] 0.57 0.72 9598 1.00
p_prior[354] 0.99 1.00 10210 1.00
p_prior[355] 1.00 1.00 10214 1.00
p_prior[356] 0.99 1.00 10203 1.00
p_prior[357] 1.00 1.00 10209 1.00
p_prior[358] 0.99 1.00 10198 1.00
p_prior[359] 1.00 1.00 10202 1.00
p_prior[360] 0.99 1.00 10194 1.00
p_prior[361] 1.00 1.00 10197 1.00
p_prior[362] 0.99 1.00 10165 1.00
p_prior[363] 1.00 1.00 10175 1.00
p_prior[364] 0.99 1.00 10164 1.00
p_prior[365] 1.00 1.00 10174 1.00
p_prior[366] 0.99 1.00 10156 1.00
p_prior[367] 1.00 1.00 10167 1.00
p_prior[368] 0.99 1.00 9847 1.00
p_prior[369] 0.99 1.00 9847 1.00
p_prior[370] 0.99 1.00 9847 1.00
p_prior[371] 0.99 1.00 9854 1.00
p_prior[372] 0.99 1.00 9854 1.00
p_prior[373] 0.99 1.00 9854 1.00
p_prior[374] 0.99 1.00 9852 1.00
p_prior[375] 0.99 1.00 9852 1.00
p_prior[376] 0.99 1.00 9852 1.00
p_prior[377] 0.99 1.00 9799 1.00
p_prior[378] 0.99 1.00 9799 1.00
p_prior[379] 0.99 1.00 9799 1.00
p_prior[380] 0.99 1.00 9798 1.00
p_prior[381] 0.99 1.00 9798 1.00
p_prior[382] 0.99 1.00 9798 1.00
p_prior[383] 0.99 1.00 9797 1.00
p_prior[384] 0.99 1.00 9797 1.00
p_prior[385] 0.99 1.00 9797 1.00
p_prior[386] 0.99 1.00 9797 1.00
p_prior[387] 0.99 1.00 9797 1.00
p_prior[388] 0.99 1.00 9797 1.00
p_prior[389] 0.99 1.00 9797 1.00
p_prior[390] 0.99 1.00 9797 1.00
p_prior[391] 0.99 1.00 9797 1.00
p_prior[392] 0.99 1.00 9797 1.00
p_prior[393] 0.99 1.00 9797 1.00
p_prior[394] 0.99 1.00 9797 1.00
p_prior[395] 0.99 1.00 9798 1.00
p_prior[396] 0.99 1.00 9798 1.00
p_prior[397] 0.99 1.00 9798 1.00
p_prior[398] 0.99 1.00 9798 1.00
p_prior[399] 0.99 1.00 9798 1.00
p_prior[400] 0.99 1.00 9798 1.00
p_prior[401] 0.99 1.00 9797 1.00
p_prior[402] 0.99 1.00 9797 1.00
p_prior[403] 0.99 1.00 9797 1.00
p_prior[404] 0.99 1.00 9798 1.00
p_prior[405] 0.99 1.00 9798 1.00
p_prior[406] 0.99 1.00 9798 1.00
p_prior[407] 0.99 1.00 9797 1.00
p_prior[408] 0.99 1.00 9797 1.00
p_prior[409] 0.99 1.00 9797 1.00
p_prior[410] 1.00 1.00 10025 1.00
p_prior[411] 1.00 1.00 10025 1.00
p_prior[412] 1.00 1.00 10025 1.00
p_prior[413] 1.00 1.00 9993 1.00
p_prior[414] 0.99 1.00 9910 1.00
p_prior[415] 0.99 1.00 9901 1.00
p_prior[416] 0.99 1.00 9899 1.00
p_prior[417] 0.99 1.00 9896 1.00
p_prior[418] 0.99 1.00 9899 1.00
p_prior[419] 0.99 1.00 9901 1.00
p_prior[420] 0.99 1.00 9867 1.00
p_prior[421] 0.99 1.00 9895 1.00
p_prior[422] 0.99 1.00 9857 1.00
p_prior[423] 0.99 1.00 9893 1.00
p_prior[424] 0.99 1.00 9859 1.00
p_prior[425] 0.99 1.00 9858 1.00
p_prior[426] 0.99 1.00 9863 1.00
p_prior[427] 0.99 1.00 9911 1.00
p_prior[428] 0.99 1.00 9905 1.00
p_prior[429] 0.99 1.00 9899 1.00
p_prior[430] 0.99 1.00 9866 1.00
p_prior[431] 0.99 1.00 9858 1.00
p_prior[432] 0.99 1.00 9859 1.00
p_prior[433] 0.99 1.00 9851 1.00
p_prior[434] 0.99 1.00 9830 1.00
p_prior[435] 0.99 1.00 9782 1.00
p_prior[436] 0.99 1.00 9782 1.00
p_prior[437] 0.99 1.00 9796 1.00
p_prior[438] 0.99 1.00 9747 1.00
p_prior[439] 0.99 1.00 9742 1.00
p_prior[440] 0.99 1.00 9742 1.00
p_prior[441] 0.99 1.00 9740 1.00
p_prior[442] 0.99 1.00 9892 1.00
p_prior[443] 0.99 1.00 9882 1.00
p_prior[444] 0.99 1.00 9875 1.00
p_prior[445] 0.99 1.00 9877 1.00
p_prior[446] 0.99 1.00 9874 1.00
p_prior[447] 0.99 1.00 9863 1.00
p_prior[448] 0.99 1.00 9870 1.00
p_prior[449] 0.99 1.00 9828 1.00
p_prior[450] 0.99 1.00 9824 1.00
p_prior[451] 0.99 1.00 9863 1.00
p_prior[452] 0.99 1.00 9822 1.00
p_prior[453] 0.99 1.00 9820 1.00
p_prior[454] 0.99 1.00 9816 1.00
p_prior[455] 0.99 1.00 9814 1.00
p_prior[456] 0.99 1.00 9824 1.00
p_prior[457] 0.99 1.00 9788 1.00
p_prior[458] 0.99 1.00 9788 1.00
p_prior[459] 0.99 1.00 9785 1.00
p_prior[460] 0.99 1.00 9891 1.00
p_prior[461] 0.99 1.00 9882 1.00
p_prior[462] 0.99 1.00 9871 1.00
p_prior[463] 0.99 1.00 9863 1.00
p_prior[464] 0.99 1.00 9860 1.00
p_prior[465] 0.99 1.00 9856 1.00
p_prior[466] 0.99 1.00 9816 1.00
p_prior[467] 0.99 1.00 9816 1.00
p_prior[468] 0.99 1.00 9814 1.00
p_prior[469] 0.59 0.78 9805 1.00
p_prior[470] 0.59 0.79 9487 1.00
p_prior[471] 0.60 0.79 9489 1.00
p_prior[472] 0.60 0.79 9486 1.00
p_prior[473] 0.60 0.79 9498 1.00
p_prior[474] 0.60 0.78 9507 1.00
p_prior[475] 0.60 0.78 9502 1.00
p_prior[476] 0.60 0.78 9497 1.00
p_prior[477] 0.62 0.82 9508 1.00
p_prior[478] 0.62 0.83 9519 1.00
p_prior[479] 0.63 0.85 9553 1.00
p_prior[480] 0.64 0.86 9571 1.00
p_prior[481] 0.99 1.00 9914 1.00
p_prior[482] 0.99 1.00 9899 1.00
p_prior[483] 0.99 1.00 9901 1.00
p_prior[484] 0.99 1.00 9839 1.00
p_prior[485] 0.99 1.00 9835 1.00
p_prior[486] 0.99 1.00 9833 1.00
p_prior[487] 0.99 1.00 9834 1.00
p_prior[488] 0.99 1.00 9832 1.00
p_prior[489] 0.99 1.00 9830 1.00
p_prior[490] 0.99 1.00 9828 1.00
p_prior[491] 1.00 1.00 10007 1.00
p_prior[492] 1.00 1.00 10007 1.00
p_prior[493] 1.00 1.00 10007 1.00
p_prior[494] 1.00 1.00 10007 1.00
p_prior[495] 1.00 1.00 9970 1.00
p_prior[496] 1.00 1.00 9970 1.00
p_prior[497] 1.00 1.00 9986 1.00
p_prior[498] 1.00 1.00 9986 1.00
p_prior[499] 1.00 1.00 9983 1.00
p_prior[500] 1.00 1.00 9983 1.00
p_prior[501] 0.59 0.78 9802 1.00
p_prior[502] 0.59 0.79 9489 1.00
p_prior[503] 0.60 0.78 9503 1.00
p_prior[504] 0.60 0.78 9500 1.00
p_prior[505] 0.60 0.78 9497 1.00
p_prior[506] 0.60 0.78 9496 1.00
p_prior[507] 0.60 0.79 9494 1.00
p_prior[508] 0.61 0.82 9505 1.00
p_prior[509] 0.62 0.82 9512 1.00
p_prior[510] 0.63 0.84 9542 1.00
p_prior[511] 0.63 0.86 9559 1.00
p_prior[512] 0.99 1.00 9568 1.00
p_prior[513] 0.99 1.00 9482 1.00
p_prior[514] 0.99 1.00 9464 1.00
p_prior[515] 0.99 1.00 9454 1.00
p_prior[516] 0.99 1.00 9434 1.00
p_prior[517] 0.99 1.00 9463 1.00
p_prior[518] 0.99 1.00 9451 1.00
p_prior[519] 0.99 1.00 9456 1.00
p_prior[520] 0.99 1.00 9453 1.00
p_prior[521] 0.99 1.00 9450 1.00
p_prior[522] 0.99 1.00 9408 1.00
p_prior[523] 0.99 1.00 9742 1.00
p_prior[524] 0.99 1.00 9739 1.00
p_prior[525] 0.99 1.00 9889 1.00
p_prior[526] 0.99 1.00 9889 1.00
p_prior[527] 0.99 1.00 9889 1.00
p_prior[528] 0.99 1.00 9879 1.00
p_prior[529] 0.99 1.00 9879 1.00
p_prior[530] 0.99 1.00 9879 1.00
p_prior[531] 0.99 1.00 9892 1.00
p_prior[532] 0.99 1.00 9892 1.00
p_prior[533] 0.99 1.00 9892 1.00
p_prior[534] 0.99 1.00 9889 1.00
p_prior[535] 0.99 1.00 9889 1.00
p_prior[536] 0.99 1.00 9889 1.00
p_prior[537] 0.99 1.00 9849 1.00
p_prior[538] 0.99 1.00 9849 1.00
p_prior[539] 0.99 1.00 9849 1.00
p_prior[540] 0.99 1.00 9900 1.00
p_prior[541] 0.99 1.00 9900 1.00
p_prior[542] 0.99 1.00 9900 1.00
p_prior[543] 0.99 1.00 9893 1.00
p_prior[544] 0.99 1.00 9893 1.00
p_prior[545] 0.99 1.00 9893 1.00
p_prior[546] 0.99 1.00 9860 1.00
p_prior[547] 0.99 1.00 9860 1.00
p_prior[548] 0.99 1.00 9860 1.00
p_prior[549] 0.99 1.00 9862 1.00
p_prior[550] 0.99 1.00 9862 1.00
p_prior[551] 0.99 1.00 9862 1.00
p_prior[552] 0.99 1.00 10004 1.00
p_prior[553] 0.99 1.00 10004 1.00
p_prior[554] 0.99 1.00 10004 1.00
p_prior[555] 0.99 1.00 9982 1.00
p_prior[556] 0.99 1.00 9984 1.00
p_prior[557] 0.99 1.00 9985 1.00
p_prior[558] 0.99 1.00 9989 1.00
p_prior[559] 0.99 1.00 9990 1.00
p_prior[560] 0.99 1.00 9990 1.00
p_prior[561] 0.99 1.00 9990 1.00
p_prior[562] 0.99 1.00 9990 1.00
p_prior[563] 0.99 1.00 9991 1.00
p_prior[564] 0.99 1.00 9992 1.00
p_prior[565] 0.99 1.00 9990 1.00
p_prior[566] 0.99 1.00 9991 1.00
p_prior[567] 0.99 1.00 9991 1.00
p_prior[568] 0.99 1.00 9992 1.00
p_prior[569] 0.99 1.00 9992 1.00
p_prior[570] 0.99 1.00 9993 1.00
p_prior[571] 0.99 1.00 9661 1.00
p_prior[572] 0.99 1.00 9674 1.00
p_prior[573] 0.99 1.00 9682 1.00
p_prior[574] 0.99 1.00 9684 1.00
p_prior[575] 0.99 1.00 9771 1.00
p_prior[576] 1.00 1.00 10225 1.00
p_prior[577] 1.00 1.00 10197 1.00
p_prior[578] 1.00 1.00 10190 1.00
p_prior[579] 1.00 1.00 10006 1.00
p_prior[580] 1.00 1.00 10004 1.00
p_prior[581] 1.00 1.00 10020 1.00
p_prior[582] 0.99 1.00 9893 1.00
p_prior[583] 0.99 1.00 9883 1.00
p_prior[584] 0.99 1.00 9861 1.00
p_prior[585] 0.99 1.00 9820 1.00
p_prior[586] 0.99 1.00 9812 1.00
p_prior[587] 0.99 1.00 10214 1.00
p_prior[588] 1.00 1.00 10219 1.00
p_prior[589] 0.99 1.00 10206 1.00
p_prior[590] 1.00 1.00 10211 1.00
p_prior[591] 0.99 1.00 10218 1.00
p_prior[592] 1.00 1.00 10221 1.00
p_prior[593] 0.99 1.00 10212 1.00
p_prior[594] 1.00 1.00 10217 1.00
p_prior[595] 0.99 1.00 10180 1.00
p_prior[596] 1.00 1.00 10191 1.00
p_prior[597] 0.57 0.71 9992 1.00
p_prior[598] 0.57 0.72 9546 1.00
p_prior[599] 0.57 0.72 9551 1.00
p_prior[600] 0.57 0.72 9558 1.00
p_prior[601] 0.57 0.72 9593 1.00
p_prior[602] 0.57 0.72 9612 1.00
p_prior[603] 0.59 0.76 9578 1.00
p_prior[604] 0.59 0.77 9581 1.00
p_prior[605] 0.60 0.78 9598 1.00
p_prior[606] 0.62 0.82 9647 1.00
p_prior[607] 0.99 1.00 10018 1.00
p_prior[608] 0.99 1.00 10018 1.00
p_prior[609] 0.99 1.00 9994 1.00
p_prior[610] 0.99 1.00 9994 1.00
p_prior[611] 0.99 1.00 9996 1.00
p_prior[612] 0.99 1.00 9996 1.00
p_prior[613] 0.99 1.00 9980 1.00
p_prior[614] 0.99 1.00 9980 1.00
p_prior[615] 0.99 1.00 9981 1.00
p_prior[616] 0.99 1.00 9981 1.00
p_prior[617] 0.57 0.70 9675 1.00
p_prior[618] 0.57 0.70 9676 1.00
p_prior[619] 0.57 0.72 9671 1.00
p_prior[620] 0.99 1.00 10008 1.00
p_prior[621] 0.99 1.00 10092 1.00
p_prior[622] 0.99 1.00 9808 1.00
p_prior[623] 0.99 1.00 9836 1.00
p_prior[624] 0.99 1.00 9854 1.00
p_prior[625] 0.99 1.00 9917 1.00
p_prior[626] 1.00 1.00 9992 1.00
p_prior[627] 1.00 1.00 9975 1.00
p_prior[628] 1.00 1.00 9973 1.00
p_prior[629] 1.00 1.00 10001 1.00
p_prior[630] 0.99 1.00 9911 1.00
p_prior[631] 0.99 1.00 9898 1.00
p_prior[632] 0.99 1.00 9855 1.00
p_prior[633] 0.99 1.00 9855 1.00
p_prior[634] 0.99 1.00 9854 1.00
p_prior[635] 0.99 1.00 9835 1.00
p_prior[636] 0.99 1.00 10214 1.00
p_prior[637] 1.00 1.00 10216 1.00
p_prior[638] 0.99 1.00 10206 1.00
p_prior[639] 1.00 1.00 10209 1.00
p_prior[640] 0.99 1.00 10195 1.00
p_prior[641] 1.00 1.00 10199 1.00
p_prior[642] 0.99 1.00 10197 1.00
p_prior[643] 1.00 1.00 10201 1.00
p_prior[644] 0.99 1.00 10161 1.00
p_prior[645] 1.00 1.00 10172 1.00
p_prior[646] 0.99 1.00 10146 1.00
p_prior[647] 1.00 1.00 10158 1.00
p_prior[648] 0.99 1.00 10149 1.00
p_prior[649] 1.00 1.00 10161 1.00
p_prior[650] 0.97 1.00 10176 1.00
p_prior[651] 0.97 1.00 10176 1.00
p_prior[652] 0.97 1.00 10178 1.00
p_prior[653] 0.97 1.00 10178 1.00
p_prior[654] 0.97 1.00 10175 1.00
p_prior[655] 0.97 1.00 10175 1.00
p_prior[656] 0.99 1.00 10160 1.00
p_prior[657] 0.99 1.00 10160 1.00
p_prior[658] 0.99 1.00 10159 1.00
p_prior[659] 0.99 1.00 10159 1.00
p_prior[660] 0.99 1.00 10129 1.00
p_prior[661] 0.99 1.00 10129 1.00
p_prior[662] 0.99 1.00 10129 1.00
p_prior[663] 0.99 1.00 10129 1.00
p_prior[664] 0.99 1.00 10130 1.00
p_prior[665] 0.99 1.00 10130 1.00
p_prior[666] 0.99 1.00 10130 1.00
p_prior[667] 0.99 1.00 10130 1.00
p_prior[668] 0.99 1.00 9806 1.00
p_prior[669] 0.99 1.00 9806 1.00
p_prior[670] 0.99 1.00 9831 1.00
p_prior[671] 0.99 1.00 9820 1.00
p_prior[672] 0.99 1.00 9838 1.00
p_prior[673] 0.99 1.00 9840 1.00
p_prior[674] 0.99 1.00 9851 1.00
p_prior[675] 0.99 1.00 9838 1.00
p_prior[676] 0.99 1.00 9876 1.00
p_prior[677] 0.99 1.00 9882 1.00
p_prior[678] 0.99 1.00 9882 1.00
p_prior[679] 0.99 1.00 9883 1.00
p_prior[680] 0.99 1.00 9883 1.00
p_prior[681] 0.99 1.00 9884 1.00
p_prior[682] 0.99 1.00 9888 1.00
p_prior[683] 0.99 1.00 9891 1.00
p_prior[684] 0.99 1.00 9892 1.00
p_prior[685] 0.99 1.00 9893 1.00
p_prior[686] 0.99 1.00 9895 1.00
p_prior[687] 0.99 1.00 9896 1.00
p_prior[688] 0.99 1.00 9897 1.00
p_prior[689] 0.99 1.00 9987 1.00
p_prior[690] 1.00 1.00 9988 1.00
p_prior[691] 0.99 1.00 9987 1.00
p_prior[692] 0.99 1.00 9962 1.00
p_prior[693] 1.00 1.00 9966 1.00
p_prior[694] 0.99 1.00 9962 1.00
p_prior[695] 0.99 1.00 9959 1.00
p_prior[696] 1.00 1.00 9964 1.00
p_prior[697] 0.99 1.00 9959 1.00
p_prior[698] 0.99 1.00 9952 1.00
p_prior[699] 1.00 1.00 9958 1.00
p_prior[700] 0.99 1.00 9952 1.00
p_prior[701] 0.99 1.00 9986 1.00
p_prior[702] 1.00 1.00 9985 1.00
p_prior[703] 0.99 1.00 9986 1.00
p_prior[704] 0.99 1.00 9986 1.00
p_prior[705] 1.00 1.00 9985 1.00
p_prior[706] 0.99 1.00 9986 1.00
p_prior[707] 0.99 1.00 9984 1.00
p_prior[708] 1.00 1.00 9983 1.00
p_prior[709] 0.99 1.00 9984 1.00
p_prior[710] 0.99 1.00 9890 1.00
p_prior[711] 0.99 1.00 9839 1.00
p_prior[712] 0.59 0.76 9833 1.00
p_prior[713] 0.59 0.77 9506 1.00
p_prior[714] 0.59 0.77 9506 1.00
p_prior[715] 0.59 0.77 9505 1.00
p_prior[716] 0.59 0.77 9506 1.00
p_prior[717] 0.59 0.76 9544 1.00
p_prior[718] 0.59 0.77 9532 1.00
p_prior[719] 0.60 0.79 9539 1.00
p_prior[720] 0.61 0.81 9548 1.00
p_prior[721] 1.00 1.00 9749 1.00
p_prior[722] 1.00 1.00 9784 1.00
p_prior[723] 1.00 1.00 9763 1.00
p_prior[724] 1.00 1.00 9764 1.00
p_prior[725] 1.00 1.00 9768 1.00
p_prior[726] 1.00 1.00 9768 1.00
p_prior[727] 1.00 1.00 9769 1.00
p_prior[728] 1.00 1.00 9774 1.00
p_prior[729] 1.00 1.00 9834 1.00
p_prior[730] 0.99 1.00 10180 1.00
p_prior[731] 0.99 1.00 10171 1.00
p_prior[732] 0.99 1.00 10118 1.00
p_prior[733] 0.99 1.00 10182 1.00
p_prior[734] 0.99 1.00 10183 1.00
p_prior[735] 0.99 1.00 10187 1.00
p_prior[736] 0.99 1.00 10149 1.00
p_prior[737] 0.99 1.00 10150 1.00
p_prior[738] 0.99 1.00 10155 1.00
p_prior[739] 0.99 1.00 10158 1.00
p_prior[740] 0.99 1.00 10160 1.00
p_prior[741] 0.99 1.00 9674 1.00
p_prior[742] 0.99 1.00 9674 1.00
p_prior[743] 0.99 1.00 9832 1.00
p_prior[744] 0.99 1.00 9835 1.00
p_prior[745] 0.99 1.00 9844 1.00
p_prior[746] 0.99 1.00 9828 1.00
p_prior[747] 0.99 1.00 9838 1.00
p_prior[748] 0.99 1.00 9783 1.00
p_prior[749] 0.99 1.00 9789 1.00
p_prior[750] 0.99 1.00 9788 1.00
p_prior[751] 0.99 1.00 9835 1.00
p_prior[752] 0.99 1.00 9905 1.00
p_prior[753] 0.99 1.00 9914 1.00
p_prior[754] 0.99 1.00 9911 1.00
p_prior[755] 0.99 1.00 9917 1.00
p_prior[756] 0.99 1.00 9920 1.00
p_prior[757] 0.99 1.00 9918 1.00
p_prior[758] 0.99 1.00 9769 1.00
p_prior[759] 0.99 1.00 9769 1.00
p_prior[760] 0.99 1.00 9768 1.00
p_prior[761] 0.99 1.00 9766 1.00
p_prior[762] 0.99 1.00 9764 1.00
p_prior[763] 0.99 1.00 9767 1.00
p_prior[764] 0.99 1.00 9766 1.00
p_prior[765] 0.99 1.00 9767 1.00
p_prior[766] 0.99 1.00 9766 1.00
p_prior[767] 0.99 1.00 9764 1.00
p_prior[768] 0.99 1.00 9764 1.00
p_prior[769] 0.99 1.00 9839 1.00
p_prior[770] 0.99 1.00 9769 1.00
p_prior[771] 0.99 1.00 9768 1.00
p_prior[772] 0.99 1.00 9758 1.00
p_prior[773] 0.99 1.00 9762 1.00
p_prior[774] 0.99 1.00 9765 1.00
p_prior[775] 0.99 1.00 9764 1.00
p_prior[776] 0.99 1.00 9764 1.00
p_prior[777] 0.99 1.00 9765 1.00
p_prior[778] 0.99 1.00 9836 1.00
p_prior[779] 0.99 1.00 9836 1.00
p_prior[780] 0.99 1.00 9839 1.00
p_prior[781] 0.99 1.00 9769 1.00
p_prior[782] 0.99 1.00 9769 1.00
p_prior[783] 0.99 1.00 9765 1.00
p_prior[784] 0.99 1.00 9764 1.00
p_prior[785] 0.99 1.00 9764 1.00
p_prior[786] 0.99 1.00 9764 1.00
p_prior[787] 0.99 1.00 9837 1.00
p_prior[788] 0.99 1.00 9839 1.00
p_prior[789] 0.99 1.00 9843 1.00
p_prior[790] 0.99 1.00 9843 1.00
p_prior[791] 0.57 0.72 9549 1.00
p_prior[792] 0.57 0.72 9555 1.00
p_prior[793] 0.57 0.72 9556 1.00
p_prior[794] 0.57 0.72 9551 1.00
p_prior[795] 0.57 0.71 9631 1.00
p_prior[796] 0.57 0.71 9616 1.00
p_prior[797] 0.57 0.71 9616 1.00
p_prior[798] 0.57 0.72 9613 1.00
p_prior[799] 1.00 1.00 9826 1.00
p_prior[800] 1.00 1.00 9831 1.00
p_prior[801] 1.00 1.00 9833 1.00
p_prior[802] 1.00 1.00 9834 1.00
p_prior[803] 1.00 1.00 9837 1.00
p_prior[804] 1.00 1.00 9840 1.00
p_prior[805] 1.00 1.00 9880 1.00
p_prior[806] 1.00 1.00 9881 1.00
p_prior[807] 0.99 1.00 10004 1.00
p_prior[808] 0.99 1.00 9982 1.00
p_prior[809] 0.99 1.00 10004 1.00
p_prior[810] 0.99 1.00 9982 1.00
p_prior[811] 0.99 1.00 9989 1.00
p_prior[812] 0.99 1.00 9991 1.00
p_prior[813] 0.99 1.00 9969 1.00
p_prior[814] 0.99 1.00 9991 1.00
p_prior[815] 0.99 1.00 9991 1.00
p_prior[816] 0.99 1.00 9992 1.00
p_prior[817] 0.99 1.00 9993 1.00
p_prior[818] 0.99 1.00 9834 1.00
p_prior[819] 0.99 1.00 9864 1.00
p_prior[820] 0.99 1.00 9866 1.00
p_prior[821] 0.99 1.00 9892 1.00
p_prior[822] 0.99 1.00 9927 1.00
p_prior[823] 0.99 1.00 9907 1.00
p_prior[824] 0.99 1.00 9917 1.00
p_prior[825] 0.99 1.00 9934 1.00
p_prior[826] 0.99 1.00 9928 1.00
p_prior[827] 0.99 1.00 9954 1.00
p_prior[828] 0.99 1.00 9699 1.00
p_prior[829] 0.99 1.00 9792 1.00
p_prior[830] 1.00 1.00 10258 1.00
p_prior[831] 1.00 1.00 10254 1.00
p_prior[832] 1.00 1.00 10217 1.00
p_prior[833] 1.00 1.00 10209 1.00
p_prior[834] 1.00 1.00 10207 1.00
p_prior[835] 1.00 1.00 10214 1.00
p_prior[836] 0.98 1.00 9923 1.00
p_prior[837] 0.62 0.85 9716 1.00
p_prior[838] 0.98 1.00 9901 1.00
p_prior[839] 0.62 0.85 9470 1.00
p_prior[840] 0.98 1.00 9903 1.00
p_prior[841] 0.62 0.85 9471 1.00
p_prior[842] 0.98 1.00 9902 1.00
p_prior[843] 0.62 0.85 9470 1.00
p_prior[844] 0.98 1.00 9899 1.00
p_prior[845] 0.63 0.87 9479 1.00
p_prior[846] 0.98 1.00 9900 1.00
p_prior[847] 0.63 0.87 9479 1.00
p_prior[848] 0.98 1.00 9893 1.00
p_prior[849] 0.63 0.87 9479 1.00
p_prior[850] 0.99 1.00 9865 1.00
p_prior[851] 0.63 0.86 9474 1.00
p_prior[852] 0.99 1.00 9845 1.00
p_prior[853] 0.64 0.87 9474 1.00
p_prior[854] 0.99 1.00 9840 1.00
p_prior[855] 0.65 0.88 9480 1.00
p_prior[856] 0.99 1.00 9833 1.00
p_prior[857] 0.66 0.89 9508 1.00
p_prior[858] 0.57 0.72 9650 1.00
p_prior[859] 0.57 0.72 9663 1.00
p_prior[860] 0.57 0.72 9681 1.00
p_prior[861] 0.57 0.72 9687 1.00
p_prior[862] 0.57 0.73 9714 1.00
p_prior[863] 0.57 0.73 9731 1.00
p_prior[864] 0.57 0.73 9695 1.00
p_prior[865] 0.58 0.73 9694 1.00
p_prior[866] 0.58 0.74 9699 1.00
p_prior[867] 0.58 0.74 9695 1.00
p_prior[868] 0.58 0.73 9695 1.00
p_prior[869] 0.58 0.73 9694 1.00
p_prior[870] 0.99 1.00 9660 1.00
p_prior[871] 0.99 1.00 9674 1.00
p_prior[872] 0.99 1.00 9684 1.00
p_prior[873] 0.99 1.00 9686 1.00
p_prior[874] 0.99 1.00 9770 1.00
p_prior[875] 0.99 1.00 9771 1.00
p_prior[876] 0.98 1.00 9964 1.00
p_prior[877] 0.98 1.00 9898 1.00
p_prior[878] 0.98 1.00 9876 1.00
p_prior[879] 0.98 1.00 9874 1.00
p_prior[880] 0.99 1.00 10173 1.00
p_prior[881] 0.99 1.00 10153 1.00
p_prior[882] 0.99 1.00 10125 1.00
p_prior[883] 0.99 1.00 10124 1.00
p_prior[884] 0.99 1.00 10126 1.00
p_prior[885] 0.98 1.00 9868 1.00
p_prior[886] 0.99 1.00 9699 1.00
p_prior[887] 0.99 1.00 9699 1.00
p_prior[888] 0.99 1.00 9696 1.00
p_prior[889] 0.99 1.00 9695 1.00
p_prior[890] 0.99 1.00 9681 1.00
p_prior[891] 0.99 1.00 9679 1.00
p_prior[892] 0.99 1.00 9679 1.00
p_prior[893] 0.99 1.00 9679 1.00
p_prior[894] 0.99 1.00 9679 1.00
p_prior[895] 0.99 1.00 9675 1.00
p_prior[896] 0.99 1.00 9782 1.00
p_prior[897] 0.99 1.00 9783 1.00
p_prior[898] 0.99 1.00 9786 1.00
p_prior[899] 0.99 1.00 9833 1.00
p_prior[900] 0.99 1.00 9836 1.00
p_prior[901] 0.59 0.76 9833 1.00
p_prior[902] 0.59 0.77 9505 1.00
p_prior[903] 0.60 0.79 9539 1.00
p_prior[904] 0.60 0.79 9536 1.00
p_prior[905] 0.61 0.80 9538 1.00
p_prior[906] 0.61 0.81 9553 1.00
p_prior[907] 0.99 1.00 9910 1.00
p_prior[908] 0.99 1.00 9898 1.00
p_prior[909] 0.99 1.00 9853 1.00
p_prior[910] 0.99 1.00 9843 1.00
p_prior[911] 0.99 1.00 9843 1.00
p_prior[912] 0.99 1.00 10017 1.00
p_prior[913] 1.00 1.00 10013 1.00
p_prior[914] 0.99 1.00 10017 1.00
p_prior[915] 0.99 1.00 9977 1.00
p_prior[916] 1.00 1.00 9978 1.00
p_prior[917] 0.99 1.00 9977 1.00
p_prior[918] 0.99 1.00 9996 1.00
p_prior[919] 1.00 1.00 9993 1.00
p_prior[920] 0.99 1.00 9996 1.00
p_prior[921] 0.99 1.00 9982 1.00
p_prior[922] 1.00 1.00 9980 1.00
p_prior[923] 0.99 1.00 9982 1.00
p_prior[924] 0.98 1.00 9874 1.00
p_prior[925] 0.98 1.00 9849 1.00
p_prior[926] 0.98 1.00 9848 1.00
p_prior[927] 0.98 1.00 9851 1.00
p_prior[928] 0.99 1.00 9946 1.00
p_prior[929] 0.99 1.00 9928 1.00
p_prior[930] 0.99 1.00 9886 1.00
p_prior[931] 0.99 1.00 9858 1.00
p_prior[932] 0.99 1.00 9769 1.00
p_prior[933] 0.99 1.00 9769 1.00
p_prior[934] 0.99 1.00 9843 1.00
p_prior[935] 0.99 1.00 9769 1.00
p_prior[936] 0.99 1.00 9768 1.00
p_prior[937] 0.99 1.00 9765 1.00
p_prior[938] 0.99 1.00 9765 1.00
p_prior[939] 0.99 1.00 9764 1.00
p_prior[940] 0.99 1.00 9766 1.00
p_prior[941] 0.99 1.00 9766 1.00
p_prior[942] 0.99 1.00 9764 1.00
p_prior[943] 0.99 1.00 9763 1.00
p_prior[944] 0.99 1.00 9876 1.00
p_prior[945] 0.99 1.00 9870 1.00
p_prior[946] 0.99 1.00 9825 1.00
p_prior[947] 0.99 1.00 9825 1.00
p_prior[948] 0.99 1.00 9826 1.00
p_prior[949] 0.99 1.00 9822 1.00
p_prior[950] 0.97 1.00 10207 1.00
p_prior[951] 0.97 1.00 10207 1.00
p_prior[952] 0.97 1.00 10207 1.00
p_prior[953] 0.97 1.00 10166 1.00
p_prior[954] 0.97 1.00 10166 1.00
p_prior[955] 0.97 1.00 10166 1.00
p_prior[956] 0.97 1.00 10166 1.00
p_prior[957] 0.97 1.00 10166 1.00
p_prior[958] 0.97 1.00 10166 1.00
p_prior[959] 0.97 1.00 10171 1.00
p_prior[960] 0.97 1.00 10171 1.00
p_prior[961] 0.97 1.00 10171 1.00
p_prior[962] 0.97 1.00 10165 1.00
p_prior[963] 0.97 1.00 10165 1.00
p_prior[964] 0.97 1.00 10165 1.00
p_prior[965] 0.97 1.00 10165 1.00
p_prior[966] 0.97 1.00 10165 1.00
p_prior[967] 0.97 1.00 10165 1.00
p_prior[968] 0.97 1.00 10165 1.00
p_prior[969] 0.97 1.00 10165 1.00
p_prior[970] 0.97 1.00 10165 1.00
p_prior[971] 0.97 1.00 10166 1.00
p_prior[972] 0.97 1.00 10166 1.00
p_prior[973] 0.97 1.00 10166 1.00
p_prior[974] 0.97 1.00 10167 1.00
p_prior[975] 0.97 1.00 10167 1.00
p_prior[976] 0.97 1.00 10167 1.00
p_prior[977] 0.98 1.00 9843 1.00
p_prior[978] 0.99 1.00 9769 1.00
p_prior[979] 0.99 1.00 9769 1.00
p_prior[980] 0.99 1.00 9769 1.00
p_prior[981] 0.99 1.00 9843 1.00
p_prior[982] 0.99 1.00 9769 1.00
p_prior[983] 0.99 1.00 9767 1.00
p_prior[984] 0.99 1.00 9765 1.00
p_prior[985] 0.99 1.00 9759 1.00
p_prior[986] 0.99 1.00 9760 1.00
p_prior[987] 0.99 1.00 9764 1.00
p_prior[988] 0.99 1.00 9766 1.00
p_prior[989] 0.99 1.00 9766 1.00
p_prior[990] 0.99 1.00 9764 1.00
p_prior[991] 0.99 1.00 9763 1.00
p_prior[992] 0.99 1.00 9842 1.00
p_prior[993] 0.99 1.00 9741 1.00
p_prior[994] 0.99 1.00 9819 1.00
p_prior[995] 0.99 1.00 9828 1.00
p_prior[996] 0.99 1.00 10204 1.00
p_prior[997] 0.99 1.00 10235 1.00
p_prior[998] 0.99 1.00 10223 1.00
p_prior[999] 0.99 1.00 9822 1.00
p_prior[1000] 0.99 1.00 9824 1.00
p_prior[1001] 0.99 1.00 9837 1.00
p_prior[1002] 0.99 1.00 9838 1.00
p_prior[1003] 0.99 1.00 9841 1.00
p_prior[1004] 1.00 1.00 10026 1.00
p_prior[1005] 1.00 1.00 9994 1.00
p_prior[1006] 1.00 1.00 9995 1.00
p_prior[1007] 1.00 1.00 10008 1.00
p_prior[1008] 1.00 1.00 9995 1.00
p_prior[1009] 0.99 1.00 10035 1.00
p_prior[1010] 0.99 1.00 10000 1.00
p_prior[1011] 0.99 1.00 10017 1.00
p_prior[1012] 0.99 1.00 9861 1.00
p_prior[1013] 0.99 1.00 9929 1.00
p_prior[1014] 0.99 1.00 9953 1.00
p_prior[1015] 0.99 1.00 9832 1.00
p_prior[1016] 0.99 1.00 9869 1.00
p_prior[1017] 0.99 1.00 9939 1.00
p_prior[1018] 0.99 1.00 9945 1.00
p_prior[1019] 0.99 1.00 9950 1.00
p_prior[1020] 0.99 1.00 9969 1.00
p_prior[1021] 0.99 1.00 9970 1.00
p_prior[1022] 0.99 1.00 9970 1.00
p_prior[1023] 0.99 1.00 9970 1.00
p_prior[1024] 0.99 1.00 9969 1.00
p_prior[1025] 0.99 1.00 9969 1.00
p_prior[1026] 0.99 1.00 9969 1.00
p_prior[1027] 0.99 1.00 9834 1.00
p_prior[1028] 0.99 1.00 9864 1.00
p_prior[1029] 0.99 1.00 9903 1.00
p_prior[1030] 0.99 1.00 9947 1.00
p_prior[1031] 0.99 1.00 9949 1.00
p_prior[1032] 0.99 1.00 9970 1.00
p_prior[1033] 0.99 1.00 9970 1.00
p_prior[1034] 0.99 1.00 9970 1.00
p_prior[1035] 0.99 1.00 9969 1.00
p_prior[1036] 0.99 1.00 9969 1.00
p_prior[1037] 0.99 1.00 9969 1.00
p_prior[1038] 0.99 1.00 10164 1.00
p_prior[1039] 0.99 1.00 10176 1.00
p_prior[1040] 0.99 1.00 10162 1.00
p_prior[1041] 0.99 1.00 10174 1.00
p_prior[1042] 0.99 1.00 10161 1.00
p_prior[1043] 0.99 1.00 10171 1.00
p_prior[1044] 0.99 1.00 10160 1.00
p_prior[1045] 0.99 1.00 10170 1.00
p_prior[1046] 0.99 1.00 10160 1.00
p_prior[1047] 0.99 1.00 10170 1.00
p_prior[1048] 0.99 1.00 10159 1.00
p_prior[1049] 0.99 1.00 10170 1.00
p_prior[1050] 0.99 1.00 10141 1.00
p_prior[1051] 0.99 1.00 10146 1.00
p_prior[1052] 0.98 1.00 9595 1.00
p_prior[1053] 0.98 1.00 9595 1.00
p_prior[1054] 0.98 1.00 9593 1.00
p_prior[1055] 0.98 1.00 9593 1.00
p_prior[1056] 0.98 1.00 9593 1.00
p_prior[1057] 0.98 1.00 9593 1.00
p_prior[1058] 0.98 1.00 9592 1.00
p_prior[1059] 0.98 1.00 9592 1.00
p_prior[1060] 0.98 1.00 9647 1.00
p_prior[1061] 0.98 1.00 9647 1.00
p_prior[1062] 0.98 1.00 9595 1.00
p_prior[1063] 0.98 1.00 9593 1.00
p_prior[1064] 0.98 1.00 9593 1.00
p_prior[1065] 0.98 1.00 9593 1.00
p_prior[1066] 0.98 1.00 9592 1.00
p_prior[1067] 0.98 1.00 9593 1.00
p_prior[1068] 0.98 1.00 9593 1.00
p_prior[1069] 0.98 1.00 9648 1.00
p_prior[1070] 0.98 1.00 9647 1.00
p_prior[1071] 0.99 1.00 9581 1.00
p_prior[1072] 0.99 1.00 9544 1.00
p_prior[1073] 0.99 1.00 9460 1.00
p_prior[1074] 0.97 1.00 10172 1.00
p_prior[1075] 0.97 1.00 10172 1.00
p_prior[1076] 0.97 1.00 10212 1.00
p_prior[1077] 0.97 1.00 10212 1.00
p_prior[1078] 0.97 1.00 10174 1.00
p_prior[1079] 0.97 1.00 10174 1.00
p_prior[1080] 0.97 1.00 10175 1.00
p_prior[1081] 0.97 1.00 10175 1.00
p_prior[1082] 0.97 1.00 10177 1.00
p_prior[1083] 0.97 1.00 10177 1.00
p_prior[1084] 0.60 0.78 9798 1.00
p_prior[1085] 0.59 0.79 9519 1.00
p_prior[1086] 0.60 0.79 9547 1.00
p_prior[1087] 0.60 0.79 9541 1.00
p_prior[1088] 0.60 0.79 9536 1.00
p_prior[1089] 0.61 0.80 9542 1.00
p_prior[1090] 0.61 0.81 9544 1.00
p_prior[1091] 0.98 1.00 10189 1.00
p_prior[1092] 0.98 1.00 10189 1.00
p_prior[1093] 0.98 1.00 10190 1.00
p_prior[1094] 0.98 1.00 10189 1.00
p_prior[1095] 0.98 1.00 10189 1.00
p_prior[1096] 0.98 1.00 10189 1.00
p_prior[1097] 0.98 1.00 10189 1.00
p_prior[1098] 0.98 1.00 10189 1.00
p_prior[1099] 0.98 1.00 10189 1.00
p_prior[1100] 0.98 1.00 10189 1.00
p_prior[1101] 0.98 1.00 10189 1.00
p_prior[1102] 0.98 1.00 10191 1.00
p_prior[1103] 0.99 1.00 10158 1.00
p_prior[1104] 0.99 1.00 10158 1.00
p_prior[1105] 0.99 1.00 10159 1.00
p_prior[1106] 0.99 1.00 9854 1.00
p_prior[1107] 0.99 1.00 9854 1.00
p_prior[1108] 0.99 1.00 9849 1.00
p_prior[1109] 0.99 1.00 9807 1.00
p_prior[1110] 0.99 1.00 9824 1.00
p_prior[1111] 0.99 1.00 9859 1.00
p_prior[1112] 0.99 1.00 9852 1.00
p_prior[1113] 0.99 1.00 9851 1.00
p_prior[1114] 0.99 1.00 9807 1.00
p_prior[1115] 0.99 1.00 9818 1.00
p_prior[1116] 0.58 0.75 9543 1.00
p_prior[1117] 0.58 0.75 9545 1.00
p_prior[1118] 0.58 0.75 9560 1.00
p_prior[1119] 0.58 0.75 9581 1.00
p_prior[1120] 0.58 0.75 9579 1.00
p_prior[1121] 0.59 0.76 9576 1.00
p_prior[1122] 0.59 0.76 9577 1.00
p_prior[1123] 0.59 0.75 9577 1.00
p_prior[1124] 0.59 0.76 9576 1.00
p_prior[1125] 0.59 0.75 9577 1.00
p_prior[1126] 0.59 0.76 9577 1.00
p_prior[1127] 0.97 1.00 10225 1.00
p_prior[1128] 0.97 1.00 10207 1.00
p_prior[1129] 0.97 1.00 10207 1.00
p_prior[1130] 0.97 1.00 10167 1.00
p_prior[1131] 0.97 1.00 10169 1.00
p_prior[1132] 0.99 1.00 10241 1.00
p_prior[1133] 0.99 1.00 9681 1.00
p_prior[1134] 0.99 1.00 9774 1.00
p_prior[1135] 0.99 1.00 9951 1.00
p_prior[1136] 1.00 1.00 9945 1.00
p_prior[1137] 0.99 1.00 9951 1.00
p_prior[1138] 0.99 1.00 9964 1.00
p_prior[1139] 1.00 1.00 9956 1.00
p_prior[1140] 0.99 1.00 9964 1.00
p_prior[1141] 0.99 1.00 9935 1.00
p_prior[1142] 1.00 1.00 9929 1.00
p_prior[1143] 0.99 1.00 9935 1.00
p_prior[1144] 0.99 1.00 9959 1.00
p_prior[1145] 1.00 1.00 9951 1.00
p_prior[1146] 0.99 1.00 9959 1.00
p_prior[1147] 0.99 1.00 9973 1.00
p_prior[1148] 1.00 1.00 9976 1.00
p_prior[1149] 0.99 1.00 9973 1.00
p_prior[1150] 0.99 1.00 9984 1.00
p_prior[1151] 1.00 1.00 9983 1.00
p_prior[1152] 0.99 1.00 9984 1.00
p_prior[1153] 0.99 1.00 9989 1.00
p_prior[1154] 1.00 1.00 9988 1.00
p_prior[1155] 0.99 1.00 9989 1.00
p_prior[1156] 0.99 1.00 9975 1.00
p_prior[1157] 1.00 1.00 9975 1.00
p_prior[1158] 0.99 1.00 9975 1.00
p_prior[1159] 0.98 1.00 9586 1.00
p_prior[1160] 0.98 1.00 9584 1.00
p_prior[1161] 0.98 1.00 9584 1.00
p_prior[1162] 0.98 1.00 9584 1.00
p_prior[1163] 0.98 1.00 9644 1.00
p_prior[1164] 0.98 1.00 9643 1.00
p_prior[1165] 0.99 1.00 9989 1.00
p_prior[1166] 1.00 1.00 9991 1.00
p_prior[1167] 0.99 1.00 9989 1.00
p_prior[1168] 0.99 1.00 10007 1.00
p_prior[1169] 1.00 1.00 10006 1.00
p_prior[1170] 0.99 1.00 10007 1.00
p_prior[1171] 0.99 1.00 9990 1.00
p_prior[1172] 1.00 1.00 9991 1.00
p_prior[1173] 0.99 1.00 9990 1.00
p_prior[1174] 1.00 1.00 10172 1.00
p_prior[1175] 1.00 1.00 10167 1.00
p_prior[1176] 1.00 1.00 10165 1.00
p_prior[1177] 1.00 1.00 10161 1.00
p_prior[1178] 1.00 1.00 10185 1.00
p_prior[1179] 0.63 0.86 9716 1.00
p_prior[1180] 0.63 0.87 9499 1.00
p_prior[1181] 0.64 0.87 9501 1.00
p_prior[1182] 0.64 0.88 9507 1.00
p_prior[1183] 0.64 0.87 9500 1.00
p_prior[1184] 0.65 0.88 9509 1.00
p_prior[1185] 0.65 0.89 9518 1.00
p_prior[1186] 0.99 1.00 10144 1.00
p_prior[1187] 0.99 1.00 10136 1.00
p_prior[1188] 0.99 1.00 10134 1.00
p_prior[1189] 0.99 1.00 9864 1.00
p_prior[1190] 0.99 1.00 9944 1.00
p_prior[1191] 0.99 1.00 9956 1.00
p_prior[1192] 0.99 1.00 9962 1.00
p_prior[1193] 0.99 1.00 9969 1.00
p_prior[1194] 0.99 1.00 9864 1.00
p_prior[1195] 0.99 1.00 9943 1.00
p_prior[1196] 0.99 1.00 9949 1.00
p_prior[1197] 0.99 1.00 9956 1.00
p_prior[1198] 0.99 1.00 9969 1.00
p_prior[1199] 0.99 1.00 10186 1.00
p_prior[1200] 0.99 1.00 10186 1.00
p_prior[1201] 0.99 1.00 10194 1.00
p_prior[1202] 0.99 1.00 10189 1.00
p_prior[1203] 0.99 1.00 10190 1.00
p_prior[1204] 0.99 1.00 10190 1.00
p_prior[1205] 0.99 1.00 10191 1.00
p_prior[1206] 0.99 1.00 10190 1.00
p_prior[1207] 0.99 1.00 10149 1.00
p_prior[1208] 0.99 1.00 10155 1.00
p_prior[1209] 1.00 1.00 9727 1.00
p_prior[1210] 1.00 1.00 9793 1.00
p_prior[1211] 0.99 1.00 9795 1.00
p_prior[1212] 0.99 1.00 9793 1.00
p_prior[1213] 0.99 1.00 9813 1.00
p_prior[1214] 0.99 1.00 9896 1.00
p_prior[1215] 0.99 1.00 9910 1.00
p_prior[1216] 0.99 1.00 9957 1.00
p_prior[1217] 0.97 1.00 10189 1.00
p_prior[1218] 0.97 1.00 10190 1.00
p_prior[1219] 0.97 1.00 10174 1.00
p_prior[1220] 0.97 1.00 10173 1.00
p_prior[1221] 0.97 1.00 10175 1.00
p_prior[1222] 0.99 1.00 10261 1.00
p_prior[1223] 0.99 1.00 10247 1.00
p_prior[1224] 0.99 1.00 10241 1.00
p_prior[1225] 0.99 1.00 10212 1.00
p_prior[1226] 0.99 1.00 10211 1.00
p_prior[1227] 0.99 1.00 10212 1.00
p_prior[1228] 0.99 1.00 10211 1.00
p_prior[1229] 0.99 1.00 9691 1.00
p_prior[1230] 0.99 1.00 9706 1.00
p_prior[1231] 0.99 1.00 9710 1.00
p_prior[1232] 0.99 1.00 9714 1.00
p_prior[1233] 0.56 0.68 9628 1.00
p_prior[1234] 0.56 0.68 9628 1.00
p_prior[1235] 0.56 0.68 9662 1.00
p_prior[1236] 0.56 0.68 9662 1.00
p_prior[1237] 0.56 0.69 9692 1.00
p_prior[1238] 0.56 0.69 9692 1.00
p_prior[1239] 0.56 0.68 9708 1.00
p_prior[1240] 0.56 0.68 9708 1.00
p_prior[1241] 0.56 0.70 9682 1.00
p_prior[1242] 0.56 0.70 9682 1.00
p_prior[1243] 0.57 0.71 9673 1.00
p_prior[1244] 0.57 0.71 9673 1.00
p_prior[1245] 0.57 0.70 9674 1.00
p_prior[1246] 0.57 0.70 9674 1.00
p_prior[1247] 0.57 0.71 9672 1.00
p_prior[1248] 0.57 0.71 9672 1.00
p_prior[1249] 0.99 1.00 9968 1.00
p_prior[1250] 1.00 1.00 9989 1.00
p_prior[1251] 0.99 1.00 9968 1.00
p_prior[1252] 0.99 1.00 9953 1.00
p_prior[1253] 1.00 1.00 9953 1.00
p_prior[1254] 0.99 1.00 9953 1.00
p_prior[1255] 0.99 1.00 9949 1.00
p_prior[1256] 1.00 1.00 9950 1.00
p_prior[1257] 0.99 1.00 9949 1.00
p_prior[1258] 0.99 1.00 9944 1.00
p_prior[1259] 1.00 1.00 9945 1.00
p_prior[1260] 0.99 1.00 9944 1.00
p_prior[1261] 0.99 1.00 9942 1.00
p_prior[1262] 1.00 1.00 9943 1.00
p_prior[1263] 0.99 1.00 9942 1.00
p_prior[1264] 0.99 1.00 9976 1.00
p_prior[1265] 1.00 1.00 9972 1.00
p_prior[1266] 0.99 1.00 9976 1.00
p_prior[1267] 0.99 1.00 9598 1.00
p_prior[1268] 0.99 1.00 9598 1.00
p_prior[1269] 0.99 1.00 9598 1.00
p_prior[1270] 0.99 1.00 9598 1.00
p_prior[1271] 0.99 1.00 9654 1.00
p_prior[1272] 0.99 1.00 9936 1.00
p_prior[1273] 0.99 1.00 9962 1.00
p_prior[1274] 0.98 1.00 9585 1.00
p_prior[1275] 0.98 1.00 9585 1.00
p_prior[1276] 0.98 1.00 9585 1.00
p_prior[1277] 0.98 1.00 9642 1.00
p_prior[1278] 0.99 1.00 10167 1.00
p_prior[1279] 0.99 1.00 10167 1.00
p_prior[1280] 0.99 1.00 10143 1.00
p_prior[1281] 0.99 1.00 10143 1.00
p_prior[1282] 0.98 1.00 9974 1.00
p_prior[1283] 0.98 1.00 9947 1.00
p_prior[1284] 0.98 1.00 9950 1.00
p_prior[1285] 0.98 1.00 9951 1.00
p_prior[1286] 0.98 1.00 9915 1.00
p_prior[1287] 0.98 1.00 9915 1.00
p_prior[1288] 0.98 1.00 9915 1.00
p_prior[1289] 0.98 1.00 9914 1.00
p_prior[1290] 0.98 1.00 9913 1.00
p_prior[1291] 0.98 1.00 9913 1.00
p_prior[1292] 0.98 1.00 9912 1.00
p_prior[1293] 0.98 1.00 9584 1.00
p_prior[1294] 0.98 1.00 9584 1.00
p_prior[1295] 0.98 1.00 9645 1.00
p_prior[1296] 0.98 1.00 9643 1.00
p_prior[1297] 1.00 1.00 10012 1.00
p_prior[1298] 1.00 1.00 9980 1.00
p_prior[1299] 1.00 1.00 9954 1.00
p_prior[1300] 1.00 1.00 9945 1.00
p_prior[1301] 1.00 1.00 9975 1.00
p_prior[1302] 0.97 1.00 10225 1.00
p_prior[1303] 0.97 1.00 10225 1.00
p_prior[1304] 0.97 1.00 10207 1.00
p_prior[1305] 0.97 1.00 10207 1.00
p_prior[1306] 0.97 1.00 10205 1.00
p_prior[1307] 0.97 1.00 10205 1.00
p_prior[1308] 0.97 1.00 10166 1.00
p_prior[1309] 0.97 1.00 10166 1.00
p_prior[1310] 0.97 1.00 10165 1.00
p_prior[1311] 0.97 1.00 10165 1.00
p_prior[1312] 0.97 1.00 10165 1.00
p_prior[1313] 0.97 1.00 10165 1.00
p_prior[1314] 0.97 1.00 10165 1.00
p_prior[1315] 0.97 1.00 10165 1.00
p_prior[1316] 0.98 1.00 9589 1.00
p_prior[1317] 0.98 1.00 9583 1.00
p_prior[1318] 0.98 1.00 9583 1.00
p_prior[1319] 0.98 1.00 9583 1.00
p_prior[1320] 0.98 1.00 9646 1.00
p_prior[1321] 0.98 1.00 9646 1.00
p_prior[1322] 0.98 1.00 9645 1.00
p_prior[1323] 0.98 1.00 9645 1.00
p_prior[1324] 0.98 1.00 9589 1.00
p_prior[1325] 0.98 1.00 9583 1.00
p_prior[1326] 0.98 1.00 9583 1.00
p_prior[1327] 0.98 1.00 9583 1.00
p_prior[1328] 0.98 1.00 9646 1.00
p_prior[1329] 0.98 1.00 9646 1.00
p_prior[1330] 0.98 1.00 9645 1.00
p_prior[1331] 0.97 1.00 10208 1.00
p_prior[1332] 0.97 1.00 10171 1.00
p_prior[1333] 0.97 1.00 10176 1.00
p_prior[1334] 0.98 1.00 9587 1.00
p_prior[1335] 0.98 1.00 9587 1.00
p_prior[1336] 0.98 1.00 9584 1.00
p_prior[1337] 0.98 1.00 9584 1.00
p_prior[1338] 0.98 1.00 9584 1.00
p_prior[1339] 0.98 1.00 9584 1.00
p_prior[1340] 0.98 1.00 9584 1.00
p_prior[1341] 0.98 1.00 9584 1.00
p_prior[1342] 0.98 1.00 9584 1.00
p_prior[1343] 0.98 1.00 9645 1.00
p_predicted[1] 0.26 0.37 8347 1.00
p_predicted[2] 0.26 0.36 8069 1.00
p_predicted[3] 0.26 0.36 7877 1.00
p_predicted[4] 0.23 0.33 7014 1.00
p_predicted[5] 0.23 0.33 6999 1.00
p_predicted[6] 0.23 0.33 6981 1.00
p_predicted[7] 0.22 0.33 7411 1.00
p_predicted[8] 0.22 0.33 7397 1.00
p_predicted[9] 0.43 0.53 5322 1.00
p_predicted[10] 0.44 0.55 6139 1.00
p_predicted[11] 0.01 0.02 5561 1.00
p_predicted[12] 0.01 0.02 4740 1.00
p_predicted[13] 0.01 0.02 4907 1.00
p_predicted[14] 0.01 0.02 4934 1.00
p_predicted[15] 0.01 0.02 4798 1.00
p_predicted[16] 0.03 0.06 4806 1.00
p_predicted[17] 0.02 0.06 4820 1.00
p_predicted[18] 0.02 0.06 4834 1.00
p_predicted[19] 0.02 0.06 4840 1.00
p_predicted[20] 0.02 0.06 4834 1.00
p_predicted[21] 0.12 0.24 9692 1.00
p_predicted[22] 0.11 0.23 9955 1.00
p_predicted[23] 0.41 0.47 5904 1.00
p_predicted[24] 0.39 0.45 7275 1.00
p_predicted[25] 0.36 0.42 3271 1.00
p_predicted[26] 0.30 0.35 4739 1.00
p_predicted[27] 0.29 0.34 4882 1.00
p_predicted[28] 0.27 0.33 3519 1.00
p_predicted[29] 0.26 0.32 2561 1.00
p_predicted[30] 0.36 0.42 5728 1.00
p_predicted[31] 0.36 0.42 5728 1.00
p_predicted[32] 0.33 0.39 6587 1.00
p_predicted[33] 0.33 0.39 6587 1.00
p_predicted[34] 0.32 0.37 4585 1.00
p_predicted[35] 0.32 0.37 4585 1.00
p_predicted[36] 0.31 0.37 3650 1.00
p_predicted[37] 0.31 0.37 3650 1.00
p_predicted[38] 0.23 0.28 4909 1.00
p_predicted[39] 0.23 0.28 4909 1.00
p_predicted[40] 0.16 0.19 6914 1.00
p_predicted[41] 0.16 0.19 6914 1.00
p_predicted[42] 0.16 0.19 7026 1.00
p_predicted[43] 0.16 0.19 7026 1.00
p_predicted[44] 0.16 0.19 6923 1.00
p_predicted[45] 0.16 0.19 6923 1.00
p_predicted[46] 0.16 0.19 6879 1.00
p_predicted[47] 0.16 0.19 6879 1.00
p_predicted[48] 0.15 0.19 6012 1.00
p_predicted[49] 0.15 0.19 6012 1.00
p_predicted[50] 0.14 0.25 4000 1.00
p_predicted[51] 0.11 0.17 8146 1.00
p_predicted[52] 0.10 0.17 8101 1.00
p_predicted[53] 0.10 0.16 7972 1.00
p_predicted[54] 0.08 0.13 8003 1.00
p_predicted[55] 0.07 0.13 8497 1.00
p_predicted[56] 0.07 0.13 8686 1.00
p_predicted[57] 0.07 0.14 10099 1.00
p_predicted[58] 0.09 0.16 4313 1.00
p_predicted[59] 0.09 0.16 4317 1.00
p_predicted[60] 0.09 0.16 4318 1.00
p_predicted[61] 0.06 0.10 8042 1.00
p_predicted[62] 0.06 0.10 7783 1.00
p_predicted[63] 0.05 0.08 7245 1.00
p_predicted[64] 0.05 0.08 7376 1.00
p_predicted[65] 0.05 0.08 7015 1.00
p_predicted[66] 0.04 0.07 8160 1.00
p_predicted[67] 0.04 0.08 9355 1.00
p_predicted[68] 0.04 0.08 9709 1.00
p_predicted[69] 0.04 0.12 7911 1.00
p_predicted[70] 0.04 0.09 9240 1.00
p_predicted[71] 0.23 0.31 5979 1.00
p_predicted[72] 0.22 0.30 6813 1.00
p_predicted[73] 0.21 0.28 6796 1.00
p_predicted[74] 0.21 0.28 6707 1.00
p_predicted[75] 0.16 0.24 2014 1.00
p_predicted[76] 0.72 0.83 9820 1.00
p_predicted[77] 0.71 0.82 9636 1.00
p_predicted[78] 0.71 0.82 9614 1.00
p_predicted[79] 0.72 0.83 9820 1.00
p_predicted[80] 0.71 0.82 9636 1.00
p_predicted[81] 0.71 0.82 9601 1.00
p_predicted[82] 0.72 0.83 9811 1.00
p_predicted[83] 0.71 0.82 9647 1.00
p_predicted[84] 0.71 0.82 9608 1.00
p_predicted[85] 0.62 0.72 8728 1.00
p_predicted[86] 0.42 0.50 6787 1.00
p_predicted[87] 0.36 0.45 4662 1.00
p_predicted[88] 0.18 0.23 4210 1.00
p_predicted[89] 0.17 0.21 5454 1.00
p_predicted[90] 0.01 0.03 4978 1.00
p_predicted[91] 0.01 0.03 4930 1.00
p_predicted[92] 0.01 0.03 4888 1.00
p_predicted[93] 0.01 0.03 4864 1.00
p_predicted[94] 0.72 0.85 11013 1.00
p_predicted[95] 0.70 0.83 9822 1.00
p_predicted[96] 0.67 0.80 10064 1.00
p_predicted[97] 0.45 0.63 8329 1.00
p_predicted[98] 0.44 0.62 8795 1.00
p_predicted[99] 0.25 0.35 1757 1.00
p_predicted[100] 0.25 0.35 1755 1.00
p_predicted[101] 0.29 0.36 4584 1.00
p_predicted[102] 0.24 0.30 6914 1.00
p_predicted[103] 0.20 0.25 4937 1.00
p_predicted[104] 0.01 0.06 6821 1.00
p_predicted[105] 0.01 0.05 6549 1.00
p_predicted[106] 0.01 0.05 6551 1.00
p_predicted[107] 0.01 0.05 6590 1.00
p_predicted[108] 0.01 0.05 6593 1.00
p_predicted[109] 0.01 0.04 6720 1.00
p_predicted[110] 0.01 0.04 6729 1.00
p_predicted[111] 0.01 0.04 6775 1.00
p_predicted[112] 0.01 0.04 6748 1.00
p_predicted[113] 0.01 0.04 6783 1.00
p_predicted[114] 0.01 0.04 6849 1.00
p_predicted[115] 0.01 0.04 6862 1.00
p_predicted[116] 0.01 0.04 6920 1.00
p_predicted[117] 0.01 0.02 5138 1.00
p_predicted[118] 0.01 0.02 5123 1.00
p_predicted[119] 0.01 0.02 5118 1.00
p_predicted[120] 0.00 0.02 5594 1.00
p_predicted[121] 0.00 0.02 5621 1.00
p_predicted[122] 0.01 0.02 4865 1.00
p_predicted[123] 0.01 0.02 4869 1.00
p_predicted[124] 0.01 0.02 4884 1.00
p_predicted[125] 0.01 0.02 4895 1.00
p_predicted[126] 0.01 0.02 4899 1.00
p_predicted[127] 0.01 0.02 4901 1.00
p_predicted[128] 0.13 0.22 9912 1.00
p_predicted[129] 0.09 0.20 9651 1.00
p_predicted[130] 0.13 0.22 9912 1.00
p_predicted[131] 0.00 0.06 10668 1.00
p_predicted[132] 0.00 0.06 10783 1.00
p_predicted[133] 0.00 0.06 10775 1.00
p_predicted[134] 0.00 0.06 10762 1.00
p_predicted[135] 0.00 0.06 10760 1.00
p_predicted[136] 0.30 0.40 1633 1.00
p_predicted[137] 0.35 0.43 5955 1.00
p_predicted[138] 0.34 0.42 6076 1.00
p_predicted[139] 0.35 0.43 5764 1.00
p_predicted[140] 0.31 0.38 7825 1.00
p_predicted[141] 0.28 0.36 4809 1.00
p_predicted[142] 0.23 0.29 7118 1.00
p_predicted[143] 0.23 0.28 7698 1.00
p_predicted[144] 0.22 0.28 7717 1.00
p_predicted[145] 0.22 0.28 7802 1.00
p_predicted[146] 0.22 0.27 7559 1.00
p_predicted[147] 0.22 0.27 7384 1.00
p_predicted[148] 0.20 0.26 4536 1.00
p_predicted[149] 0.17 0.24 2295 1.00
p_predicted[150] 0.11 0.20 2570 1.00
p_predicted[151] 0.14 0.21 3786 1.00
p_predicted[152] 0.14 0.21 3766 1.00
p_predicted[153] 0.09 0.14 4169 1.00
p_predicted[154] 0.10 0.15 3793 1.00
p_predicted[155] 0.11 0.16 4493 1.00
p_predicted[156] 0.11 0.16 4299 1.00
p_predicted[157] 0.09 0.14 4191 1.00
p_predicted[158] 0.10 0.15 4058 1.00
p_predicted[159] 0.08 0.12 5446 1.00
p_predicted[160] 0.06 0.09 5431 1.00
p_predicted[161] 0.05 0.08 4207 1.00
p_predicted[162] 0.05 0.08 3654 1.00
p_predicted[163] 0.16 0.22 7336 1.00
p_predicted[164] 0.16 0.22 7336 1.00
p_predicted[165] 0.09 0.13 10729 1.00
p_predicted[166] 0.09 0.13 10729 1.00
p_predicted[167] 0.09 0.12 10620 1.00
p_predicted[168] 0.09 0.12 10620 1.00
p_predicted[169] 0.07 0.11 10890 1.00
p_predicted[170] 0.07 0.11 10890 1.00
p_predicted[171] 0.41 0.47 5908 1.00
p_predicted[172] 0.38 0.44 6546 1.00
p_predicted[173] 0.29 0.34 4973 1.00
p_predicted[174] 0.15 0.23 1564 1.00
p_predicted[175] 0.09 0.16 4305 1.00
p_predicted[176] 0.09 0.16 4283 1.00
p_predicted[177] 0.06 0.10 8061 1.00
p_predicted[178] 0.06 0.10 7547 1.00
p_predicted[179] 0.06 0.10 8020 1.00
p_predicted[180] 0.06 0.10 7780 1.00
p_predicted[181] 0.06 0.10 7641 1.00
p_predicted[182] 0.04 0.07 7777 1.00
p_predicted[183] 0.04 0.07 7941 1.00
p_predicted[184] 0.04 0.07 8460 1.00
p_predicted[185] 0.04 0.07 8763 1.00
p_predicted[186] 0.04 0.07 8822 1.00
p_predicted[187] 0.17 0.28 3389 1.00
p_predicted[188] 0.17 0.28 3381 1.00
p_predicted[189] 0.12 0.18 8720 1.00
p_predicted[190] 0.12 0.17 8287 1.00
p_predicted[191] 0.12 0.17 8082 1.00
p_predicted[192] 0.12 0.17 8353 1.00
p_predicted[193] 0.09 0.14 7042 1.00
p_predicted[194] 0.09 0.13 7824 1.00
p_predicted[195] 0.09 0.13 7980 1.00
p_predicted[196] 0.09 0.13 8174 1.00
p_predicted[197] 0.17 0.28 3389 1.00
p_predicted[198] 0.17 0.28 3392 1.00
p_predicted[199] 0.17 0.28 3375 1.00
p_predicted[200] 0.12 0.18 8720 1.00
p_predicted[201] 0.12 0.17 8082 1.00
p_predicted[202] 0.09 0.13 8339 1.00
p_predicted[203] 0.09 0.13 9089 1.00
p_predicted[204] 0.09 0.13 7826 1.00
p_predicted[205] 0.01 0.14 9967 1.00
p_predicted[206] 0.01 0.13 9474 1.00
p_predicted[207] 0.01 0.13 9465 1.00
p_predicted[208] 0.20 0.27 4032 1.00
p_predicted[209] 0.15 0.20 3243 1.00
p_predicted[210] 0.16 0.21 5087 1.00
p_predicted[211] 0.16 0.21 5057 1.00
p_predicted[212] 0.13 0.18 5299 1.00
p_predicted[213] 0.10 0.14 6607 1.00
p_predicted[214] 0.09 0.13 7815 1.00
p_predicted[215] 0.10 0.13 7419 1.00
p_predicted[216] 0.67 0.80 11365 1.00
p_predicted[217] 0.67 0.78 9692 1.00
p_predicted[218] 0.66 0.78 9499 1.00
p_predicted[219] 0.43 0.61 8592 1.00
p_predicted[220] 0.43 0.61 8950 1.00
p_predicted[221] 0.67 0.78 9703 1.00
p_predicted[222] 0.66 0.77 9462 1.00
p_predicted[223] 0.43 0.61 8657 1.00
p_predicted[224] 0.42 0.61 8986 1.00
p_predicted[225] 0.42 0.61 9013 1.00
p_predicted[226] 0.34 0.53 6499 1.00
p_predicted[227] 0.36 0.54 6901 1.00
p_predicted[228] 0.36 0.53 6895 1.00
p_predicted[229] 0.36 0.54 6949 1.00
p_predicted[230] 0.36 0.54 6928 1.00
p_predicted[231] 0.11 0.19 4048 1.00
p_predicted[232] 0.08 0.12 8441 1.00
p_predicted[233] 0.06 0.09 7358 1.00
p_predicted[234] 0.05 0.08 9585 1.00
p_predicted[235] 0.05 0.08 9715 1.00
p_predicted[236] 0.01 0.13 9901 1.00
p_predicted[237] 0.01 0.13 9901 1.00
p_predicted[238] 0.01 0.14 9593 1.00
p_predicted[239] 0.01 0.14 9593 1.00
p_predicted[240] 0.29 0.57 5815 1.00
p_predicted[241] 0.26 0.52 5398 1.00
p_predicted[242] 0.26 0.52 5404 1.00
p_predicted[243] 0.24 0.49 5489 1.00
p_predicted[244] 0.46 0.57 1634 1.00
p_predicted[245] 0.46 0.57 1634 1.00
p_predicted[246] 0.46 0.57 1634 1.00
p_predicted[247] 0.46 0.57 1632 1.00
p_predicted[248] 0.46 0.57 1632 1.00
p_predicted[249] 0.46 0.57 1632 1.00
p_predicted[250] 0.51 0.58 8355 1.00
p_predicted[251] 0.51 0.58 8355 1.00
p_predicted[252] 0.51 0.58 8355 1.00
p_predicted[253] 0.50 0.57 8237 1.00
p_predicted[254] 0.50 0.57 8237 1.00
p_predicted[255] 0.50 0.57 8237 1.00
p_predicted[256] 0.49 0.56 7198 1.00
p_predicted[257] 0.49 0.56 7198 1.00
p_predicted[258] 0.49 0.56 7198 1.00
p_predicted[259] 0.48 0.56 6406 1.00
p_predicted[260] 0.48 0.56 6406 1.00
p_predicted[261] 0.48 0.56 6406 1.00
p_predicted[262] 0.42 0.50 4471 1.00
p_predicted[263] 0.42 0.50 4471 1.00
p_predicted[264] 0.42 0.50 4471 1.00
p_predicted[265] 0.39 0.47 5242 1.00
p_predicted[266] 0.39 0.47 5242 1.00
p_predicted[267] 0.39 0.47 5242 1.00
p_predicted[268] 0.38 0.46 5068 1.00
p_predicted[269] 0.38 0.46 5068 1.00
p_predicted[270] 0.38 0.46 5068 1.00
p_predicted[271] 0.22 0.27 6405 1.00
p_predicted[272] 0.22 0.27 6405 1.00
p_predicted[273] 0.22 0.27 6405 1.00
p_predicted[274] 0.22 0.26 5830 1.00
p_predicted[275] 0.22 0.26 5830 1.00
p_predicted[276] 0.22 0.26 5830 1.00
p_predicted[277] 0.03 0.07 7125 1.00
p_predicted[278] 0.03 0.07 7125 1.00
p_predicted[279] 0.03 0.07 7125 1.00
p_predicted[280] 0.03 0.07 7125 1.00
p_predicted[281] 0.03 0.07 7125 1.00
p_predicted[282] 0.03 0.07 7125 1.00
p_predicted[283] 0.03 0.07 7126 1.00
p_predicted[284] 0.03 0.07 7126 1.00
p_predicted[285] 0.03 0.05 8289 1.00
p_predicted[286] 0.03 0.05 8289 1.00
p_predicted[287] 0.03 0.07 6286 1.00
p_predicted[288] 0.03 0.07 6286 1.00
p_predicted[289] 0.02 0.05 8213 1.00
p_predicted[290] 0.02 0.05 8213 1.00
p_predicted[291] 0.02 0.05 8206 1.00
p_predicted[292] 0.02 0.05 8206 1.00
p_predicted[293] 0.05 0.09 10128 1.00
p_predicted[294] 0.05 0.09 10128 1.00
p_predicted[295] 0.05 0.09 10125 1.00
p_predicted[296] 0.05 0.09 10125 1.00
p_predicted[297] 0.05 0.09 10124 1.00
p_predicted[298] 0.05 0.09 10124 1.00
p_predicted[299] 0.05 0.09 10119 1.00
p_predicted[300] 0.05 0.09 10119 1.00
p_predicted[301] 0.05 0.09 10119 1.00
p_predicted[302] 0.05 0.09 10119 1.00
p_predicted[303] 0.05 0.09 10123 1.00
p_predicted[304] 0.05 0.09 10123 1.00
p_predicted[305] 0.05 0.09 10120 1.00
p_predicted[306] 0.05 0.09 10120 1.00
p_predicted[307] 0.05 0.09 10122 1.00
p_predicted[308] 0.05 0.09 10122 1.00
p_predicted[309] 0.05 0.09 10113 1.00
p_predicted[310] 0.05 0.09 10113 1.00
p_predicted[311] 0.05 0.09 10113 1.00
p_predicted[312] 0.05 0.09 10113 1.00
p_predicted[313] 0.05 0.09 10113 1.00
p_predicted[314] 0.05 0.09 10113 1.00
p_predicted[315] 0.05 0.09 10117 1.00
p_predicted[316] 0.05 0.09 10117 1.00
p_predicted[317] 0.05 0.09 10114 1.00
p_predicted[318] 0.05 0.09 10114 1.00
p_predicted[319] 0.05 0.09 10117 1.00
p_predicted[320] 0.05 0.09 10117 1.00
p_predicted[321] 0.06 0.12 7410 1.00
p_predicted[322] 0.06 0.12 7410 1.00
p_predicted[323] 0.06 0.12 7414 1.00
p_predicted[324] 0.06 0.12 7414 1.00
p_predicted[325] 0.12 0.18 4462 1.00
p_predicted[326] 0.16 0.22 3557 1.00
p_predicted[327] 0.18 0.26 4471 1.00
p_predicted[328] 0.10 0.15 4359 1.00
p_predicted[329] 0.11 0.17 5490 1.00
p_predicted[330] 0.08 0.12 4369 1.00
p_predicted[331] 0.09 0.14 5226 1.00
p_predicted[332] 0.06 0.10 4055 1.00
p_predicted[333] 0.07 0.12 5305 1.00
p_predicted[334] 0.06 0.10 4737 1.00
p_predicted[335] 0.07 0.11 6203 1.00
p_predicted[336] 0.05 0.09 4848 1.00
p_predicted[337] 0.06 0.10 6290 1.00
p_predicted[338] 0.38 0.62 11258 1.00
p_predicted[339] 0.34 0.56 11317 1.00
p_predicted[340] 0.33 0.54 11387 1.00
p_predicted[341] 0.33 0.54 11369 1.00
p_predicted[342] 0.33 0.54 11362 1.00
p_predicted[343] 0.33 0.54 11366 1.00
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p_predicted[347] 0.47 0.56 2563 1.00
p_predicted[348] 0.51 0.58 8322 1.00
p_predicted[349] 0.51 0.58 8322 1.00
p_predicted[350] 0.40 0.46 4740 1.00
p_predicted[351] 0.40 0.46 4740 1.00
p_predicted[352] 0.30 0.36 3301 1.00
p_predicted[353] 0.30 0.36 3301 1.00
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p_predicted[360] 0.23 0.33 6430 1.00
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p_predicted[365] 0.17 0.26 5779 1.00
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p_predicted[369] 0.41 0.48 6162 1.00
p_predicted[370] 0.41 0.48 6162 1.00
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p_predicted[374] 0.24 0.30 7087 1.00
p_predicted[375] 0.24 0.30 7087 1.00
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p_predicted[395] 0.16 0.20 8187 1.00
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p_predicted[397] 0.16 0.20 8187 1.00
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p_predicted[401] 0.16 0.20 8232 1.00
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p_predicted[407] 0.16 0.20 8245 1.00
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p_predicted[435] 0.30 0.39 5479 1.00
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p_predicted[439] 0.19 0.30 1625 1.00
p_predicted[440] 0.19 0.30 1602 1.00
p_predicted[441] 0.17 0.30 1435 1.00
p_predicted[442] 0.30 0.41 1647 1.00
p_predicted[443] 0.35 0.43 5617 1.00
p_predicted[444] 0.33 0.40 7688 1.00
p_predicted[445] 0.33 0.41 7221 1.00
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p_predicted[448] 0.31 0.38 8175 1.00
p_predicted[449] 0.26 0.33 4048 1.00
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p_predicted[453] 0.24 0.30 6842 1.00
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p_predicted[455] 0.22 0.28 7828 1.00
p_predicted[456] 0.21 0.32 1362 1.00
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p_predicted[458] 0.16 0.24 1801 1.00
p_predicted[459] 0.15 0.23 1565 1.00
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p_predicted[468] 0.22 0.28 7854 1.00
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p_predicted[482] 0.28 0.34 5402 1.00
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p_predicted[492] 0.09 0.15 8078 1.00
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p_predicted[494] 0.09 0.15 8123 1.00
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p_predicted[498] 0.08 0.13 6205 1.00
p_predicted[499] 0.08 0.13 6204 1.00
p_predicted[500] 0.08 0.13 6204 1.00
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p_predicted[526] 0.53 0.61 8577 1.00
p_predicted[527] 0.53 0.61 8577 1.00
p_predicted[528] 0.51 0.59 5876 1.00
p_predicted[529] 0.51 0.59 5876 1.00
p_predicted[530] 0.51 0.59 5876 1.00
p_predicted[531] 0.33 0.39 6275 1.00
p_predicted[532] 0.33 0.39 6275 1.00
p_predicted[533] 0.33 0.39 6275 1.00
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p_predicted[535] 0.32 0.38 5348 1.00
p_predicted[536] 0.32 0.38 5348 1.00
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p_predicted[538] 0.27 0.32 4104 1.00
p_predicted[539] 0.27 0.32 4104 1.00
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p_predicted[541] 0.41 0.49 7073 1.00
p_predicted[542] 0.41 0.49 7073 1.00
p_predicted[543] 0.39 0.47 6245 1.00
p_predicted[544] 0.39 0.47 6245 1.00
p_predicted[545] 0.39 0.47 6245 1.00
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p_predicted[547] 0.23 0.29 4104 1.00
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p_predicted[550] 0.24 0.30 3649 1.00
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p_predicted[580] 0.11 0.22 8975 1.00
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p_predicted[583] 0.35 0.43 5690 1.00
p_predicted[584] 0.28 0.36 5200 1.00
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p_predicted[605] 0.13 0.18 2284 1.00
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p_predicted[608] 0.68 0.85 7444 1.00
p_predicted[609] 0.64 0.81 6205 1.00
p_predicted[610] 0.64 0.81 6205 1.00
p_predicted[611] 0.64 0.81 6206 1.00
p_predicted[612] 0.64 0.81 6206 1.00
p_predicted[613] 0.67 0.84 6644 1.00
p_predicted[614] 0.67 0.84 6644 1.00
p_predicted[615] 0.77 0.91 5734 1.00
p_predicted[616] 0.77 0.91 5734 1.00
p_predicted[617] 0.22 0.27 2647 1.00
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p_predicted[619] 0.21 0.26 2072 1.00
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p_predicted[629] 0.04 0.09 8433 1.00
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p_predicted[631] 0.41 0.49 6748 1.00
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p_predicted[633] 0.34 0.42 3761 1.00
p_predicted[634] 0.34 0.42 3941 1.00
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p_predicted[653] 0.33 0.50 7807 1.00
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p_predicted[689] 0.05 0.09 5456 1.00
p_predicted[690] 0.03 0.07 6860 1.00
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p_predicted[693] 0.07 0.11 6645 1.00
p_predicted[694] 0.09 0.14 5497 1.00
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p_predicted[699] 0.07 0.12 6404 1.00
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p_predicted[702] 0.05 0.09 6511 1.00
p_predicted[703] 0.07 0.12 5235 1.00
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p_predicted[705] 0.05 0.09 6509 1.00
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p_predicted[708] 0.05 0.09 6459 1.00
p_predicted[709] 0.07 0.12 5178 1.00
p_predicted[710] 0.51 0.58 8318 1.00
p_predicted[711] 0.43 0.51 4023 1.00
p_predicted[712] 0.25 0.33 1538 1.00
p_predicted[713] 0.28 0.35 7576 1.00
p_predicted[714] 0.28 0.35 7563 1.00
p_predicted[715] 0.27 0.34 7991 1.00
p_predicted[716] 0.27 0.33 7390 1.00
p_predicted[717] 0.22 0.27 4538 1.00
p_predicted[718] 0.21 0.26 5606 1.00
p_predicted[719] 0.14 0.18 7688 1.00
p_predicted[720] 0.11 0.15 3567 1.00
p_predicted[721] 0.00 0.02 9861 1.00
p_predicted[722] 0.00 0.02 9854 1.00
p_predicted[723] 0.00 0.02 9918 1.00
p_predicted[724] 0.00 0.02 9918 1.00
p_predicted[725] 0.00 0.02 9930 1.00
p_predicted[726] 0.00 0.02 9929 1.00
p_predicted[727] 0.00 0.02 9935 1.00
p_predicted[728] 0.00 0.02 9933 1.00
p_predicted[729] 0.00 0.02 10083 1.00
p_predicted[730] 0.40 0.57 8339 1.00
p_predicted[731] 0.39 0.56 8085 1.00
p_predicted[732] 0.31 0.48 7390 1.00
p_predicted[733] 0.61 0.76 9868 1.00
p_predicted[734] 0.61 0.76 9858 1.00
p_predicted[735] 0.59 0.74 9694 1.00
p_predicted[736] 0.51 0.67 9747 1.00
p_predicted[737] 0.51 0.67 9840 1.00
p_predicted[738] 0.50 0.67 10330 1.00
p_predicted[739] 0.50 0.68 10488 1.00
p_predicted[740] 0.50 0.68 10591 1.00
p_predicted[741] 0.10 0.20 8994 1.00
p_predicted[742] 0.10 0.20 8994 1.00
p_predicted[743] 0.00 0.01 9367 1.00
p_predicted[744] 0.00 0.01 9370 1.00
p_predicted[745] 0.00 0.01 9317 1.00
p_predicted[746] 0.00 0.01 9363 1.00
p_predicted[747] 0.00 0.01 9349 1.00
p_predicted[748] 0.00 0.01 9579 1.00
p_predicted[749] 0.00 0.01 9587 1.00
p_predicted[750] 0.00 0.01 9578 1.00
p_predicted[751] 0.00 0.01 9724 1.00
p_predicted[752] 0.02 0.19 10547 1.00
p_predicted[753] 0.02 0.18 10633 1.00
p_predicted[754] 0.02 0.18 10635 1.00
p_predicted[755] 0.02 0.18 10631 1.00
p_predicted[756] 0.01 0.18 10629 1.00
p_predicted[757] 0.01 0.19 10339 1.00
p_predicted[758] 0.00 0.01 9401 1.00
p_predicted[759] 0.00 0.01 9402 1.00
p_predicted[760] 0.00 0.01 9381 1.00
p_predicted[761] 0.00 0.01 9353 1.00
p_predicted[762] 0.00 0.01 9284 1.00
p_predicted[763] 0.00 0.01 9315 1.00
p_predicted[764] 0.00 0.01 9306 1.00
p_predicted[765] 0.00 0.01 9304 1.00
p_predicted[766] 0.00 0.01 9297 1.00
p_predicted[767] 0.00 0.01 9280 1.00
p_predicted[768] 0.00 0.01 9274 1.00
p_predicted[769] 0.00 0.01 9348 1.00
p_predicted[770] 0.00 0.01 9402 1.00
p_predicted[771] 0.00 0.01 9382 1.00
p_predicted[772] 0.00 0.01 9268 1.00
p_predicted[773] 0.00 0.01 9284 1.00
p_predicted[774] 0.00 0.01 9313 1.00
p_predicted[775] 0.00 0.01 9302 1.00
p_predicted[776] 0.00 0.01 9290 1.00
p_predicted[777] 0.00 0.01 9289 1.00
p_predicted[778] 0.00 0.01 9370 1.00
p_predicted[779] 0.00 0.01 9370 1.00
p_predicted[780] 0.00 0.01 9346 1.00
p_predicted[781] 0.00 0.01 9401 1.00
p_predicted[782] 0.00 0.01 9396 1.00
p_predicted[783] 0.00 0.01 9310 1.00
p_predicted[784] 0.00 0.01 9287 1.00
p_predicted[785] 0.00 0.01 9278 1.00
p_predicted[786] 0.00 0.01 9280 1.00
p_predicted[787] 0.00 0.01 9361 1.00
p_predicted[788] 0.00 0.01 9348 1.00
p_predicted[789] 0.00 0.01 9322 1.00
p_predicted[790] 0.00 0.01 9320 1.00
p_predicted[791] 0.38 0.44 4121 1.00
p_predicted[792] 0.37 0.44 3524 1.00
p_predicted[793] 0.37 0.43 3396 1.00
p_predicted[794] 0.38 0.44 3875 1.00
p_predicted[795] 0.33 0.39 2796 1.00
p_predicted[796] 0.32 0.38 3037 1.00
p_predicted[797] 0.32 0.38 3032 1.00
p_predicted[798] 0.31 0.38 3092 1.00
p_predicted[799] 0.00 0.04 9340 1.00
p_predicted[800] 0.00 0.03 9353 1.00
p_predicted[801] 0.00 0.04 9356 1.00
p_predicted[802] 0.00 0.03 9379 1.00
p_predicted[803] 0.00 0.04 9399 1.00
p_predicted[804] 0.00 0.04 9404 1.00
p_predicted[805] 0.00 0.02 9903 1.00
p_predicted[806] 0.00 0.02 9892 1.00
p_predicted[807] 0.03 0.07 7124 1.00
p_predicted[808] 0.03 0.05 8280 1.00
p_predicted[809] 0.03 0.07 7114 1.00
p_predicted[810] 0.03 0.05 8277 1.00
p_predicted[811] 0.05 0.09 10135 1.00
p_predicted[812] 0.05 0.09 10119 1.00
p_predicted[813] 0.06 0.12 7333 1.00
p_predicted[814] 0.05 0.09 10119 1.00
p_predicted[815] 0.05 0.09 10121 1.00
p_predicted[816] 0.05 0.09 10135 1.00
p_predicted[817] 0.05 0.09 10143 1.00
p_predicted[818] 0.09 0.18 9968 1.00
p_predicted[819] 0.09 0.17 9638 1.00
p_predicted[820] 0.09 0.17 9664 1.00
p_predicted[821] 0.08 0.16 9829 1.00
p_predicted[822] 0.07 0.12 10385 1.00
p_predicted[823] 0.08 0.15 9722 1.00
p_predicted[824] 0.07 0.13 10124 1.00
p_predicted[825] 0.06 0.12 10513 1.00
p_predicted[826] 0.07 0.12 10415 1.00
p_predicted[827] 0.06 0.11 10601 1.00
p_predicted[828] 0.00 0.02 9751 1.00
p_predicted[829] 0.00 0.02 9811 1.00
p_predicted[830] 0.08 0.18 8423 1.00
p_predicted[831] 0.10 0.20 9497 1.00
p_predicted[832] 0.07 0.14 8586 1.00
p_predicted[833] 0.06 0.14 8760 1.00
p_predicted[834] 0.06 0.14 8799 1.00
p_predicted[835] 0.06 0.15 9279 1.00
p_predicted[836] 0.19 0.27 2072 1.00
p_predicted[837] 0.19 0.29 2265 1.00
p_predicted[838] 0.22 0.28 4812 1.00
p_predicted[839] 0.23 0.31 9627 1.00
p_predicted[840] 0.22 0.29 4607 1.00
p_predicted[841] 0.23 0.31 9401 1.00
p_predicted[842] 0.22 0.28 4752 1.00
p_predicted[843] 0.23 0.31 9567 1.00
p_predicted[844] 0.13 0.18 5184 1.00
p_predicted[845] 0.14 0.20 8198 1.00
p_predicted[846] 0.13 0.18 5203 1.00
p_predicted[847] 0.14 0.20 8341 1.00
p_predicted[848] 0.12 0.17 4665 1.00
p_predicted[849] 0.13 0.19 6677 1.00
p_predicted[850] 0.10 0.13 5668 1.00
p_predicted[851] 0.10 0.15 10367 1.00
p_predicted[852] 0.07 0.10 4686 1.00
p_predicted[853] 0.08 0.12 5450 1.00
p_predicted[854] 0.07 0.10 3430 1.00
p_predicted[855] 0.07 0.11 3684 1.00
p_predicted[856] 0.05 0.08 2187 1.00
p_predicted[857] 0.05 0.09 2324 1.00
p_predicted[858] 0.21 0.27 5737 1.00
p_predicted[859] 0.20 0.26 6608 1.00
p_predicted[860] 0.19 0.25 6985 1.00
p_predicted[861] 0.19 0.24 6903 1.00
p_predicted[862] 0.18 0.23 5802 1.00
p_predicted[863] 0.17 0.23 4750 1.00
p_predicted[864] 0.14 0.18 5401 1.00
p_predicted[865] 0.13 0.17 6423 1.00
p_predicted[866] 0.12 0.16 5737 1.00
p_predicted[867] 0.12 0.16 6326 1.00
p_predicted[868] 0.13 0.17 6416 1.00
p_predicted[869] 0.13 0.17 6392 1.00
p_predicted[870] 0.00 0.04 10666 1.00
p_predicted[871] 0.00 0.04 10451 1.00
p_predicted[872] 0.00 0.04 10516 1.00
p_predicted[873] 0.00 0.04 10544 1.00
p_predicted[874] 0.00 0.04 10652 1.00
p_predicted[875] 0.00 0.04 10664 1.00
p_predicted[876] 0.34 0.46 7073 1.00
p_predicted[877] 0.25 0.35 6012 1.00
p_predicted[878] 0.19 0.28 4375 1.00
p_predicted[879] 0.18 0.27 3702 1.00
p_predicted[880] 0.06 0.12 5760 1.00
p_predicted[881] 0.04 0.09 7684 1.00
p_predicted[882] 0.03 0.07 7533 1.00
p_predicted[883] 0.03 0.06 7745 1.00
p_predicted[884] 0.03 0.06 7872 1.00
p_predicted[885] 0.00 0.02 9591 1.00
p_predicted[886] 0.00 0.02 9752 1.00
p_predicted[887] 0.00 0.02 9749 1.00
p_predicted[888] 0.00 0.02 9739 1.00
p_predicted[889] 0.00 0.02 9730 1.00
p_predicted[890] 0.00 0.02 9882 1.00
p_predicted[891] 0.00 0.02 10167 1.00
p_predicted[892] 0.00 0.02 10165 1.00
p_predicted[893] 0.00 0.02 10168 1.00
p_predicted[894] 0.00 0.02 10171 1.00
p_predicted[895] 0.00 0.02 10128 1.00
p_predicted[896] 0.00 0.02 10122 1.00
p_predicted[897] 0.00 0.01 9578 1.00
p_predicted[898] 0.00 0.01 9566 1.00
p_predicted[899] 0.00 0.01 9703 1.00
p_predicted[900] 0.00 0.01 9767 1.00
p_predicted[901] 0.25 0.33 1537 1.00
p_predicted[902] 0.28 0.35 7806 1.00
p_predicted[903] 0.14 0.18 7687 1.00
p_predicted[904] 0.13 0.17 9222 1.00
p_predicted[905] 0.12 0.16 6996 1.00
p_predicted[906] 0.10 0.15 2976 1.00
p_predicted[907] 0.31 0.38 6813 1.00
p_predicted[908] 0.28 0.35 5863 1.00
p_predicted[909] 0.23 0.29 4904 1.00
p_predicted[910] 0.20 0.26 5942 1.00
p_predicted[911] 0.20 0.26 5862 1.00
p_predicted[912] 0.02 0.06 5351 1.00
p_predicted[913] 0.02 0.04 6308 1.00
p_predicted[914] 0.02 0.06 5351 1.00
p_predicted[915] 0.03 0.06 4759 1.00
p_predicted[916] 0.02 0.05 5664 1.00
p_predicted[917] 0.03 0.06 4759 1.00
p_predicted[918] 0.02 0.05 4782 1.00
p_predicted[919] 0.01 0.04 5557 1.00
p_predicted[920] 0.02 0.05 4782 1.00
p_predicted[921] 0.02 0.05 4689 1.00
p_predicted[922] 0.01 0.04 5484 1.00
p_predicted[923] 0.02 0.05 4689 1.00
p_predicted[924] 0.41 0.53 1772 1.00
p_predicted[925] 0.43 0.52 7042 1.00
p_predicted[926] 0.43 0.52 6964 1.00
p_predicted[927] 0.44 0.53 7126 1.00
p_predicted[928] 0.13 0.21 2275 1.00
p_predicted[929] 0.16 0.22 3658 1.00
p_predicted[930] 0.09 0.13 4093 1.00
p_predicted[931] 0.06 0.09 2634 1.00
p_predicted[932] 0.00 0.01 9394 1.00
p_predicted[933] 0.00 0.01 9399 1.00
p_predicted[934] 0.00 0.02 8922 1.00
p_predicted[935] 0.00 0.01 9399 1.00
p_predicted[936] 0.00 0.01 9387 1.00
p_predicted[937] 0.00 0.01 9340 1.00
p_predicted[938] 0.00 0.01 9337 1.00
p_predicted[939] 0.00 0.01 9283 1.00
p_predicted[940] 0.00 0.01 9303 1.00
p_predicted[941] 0.00 0.01 9295 1.00
p_predicted[942] 0.00 0.01 9273 1.00
p_predicted[943] 0.00 0.01 9268 1.00
p_predicted[944] 0.55 0.63 6816 1.00
p_predicted[945] 0.53 0.62 6607 1.00
p_predicted[946] 0.47 0.56 4147 1.00
p_predicted[947] 0.25 0.31 6742 1.00
p_predicted[948] 0.25 0.31 6757 1.00
p_predicted[949] 0.24 0.30 5999 1.00
p_predicted[950] 0.27 0.38 9408 1.00
p_predicted[951] 0.27 0.38 9408 1.00
p_predicted[952] 0.27 0.38 9408 1.00
p_predicted[953] 0.22 0.30 8537 1.00
p_predicted[954] 0.22 0.30 8537 1.00
p_predicted[955] 0.22 0.30 8537 1.00
p_predicted[956] 0.21 0.29 8690 1.00
p_predicted[957] 0.21 0.29 8690 1.00
p_predicted[958] 0.21 0.29 8690 1.00
p_predicted[959] 0.21 0.29 10154 1.00
p_predicted[960] 0.21 0.29 10154 1.00
p_predicted[961] 0.21 0.29 10154 1.00
p_predicted[962] 0.22 0.30 8083 1.00
p_predicted[963] 0.22 0.30 8083 1.00
p_predicted[964] 0.22 0.30 8083 1.00
p_predicted[965] 0.21 0.29 8281 1.00
p_predicted[966] 0.21 0.29 8281 1.00
p_predicted[967] 0.21 0.29 8281 1.00
p_predicted[968] 0.21 0.29 8385 1.00
p_predicted[969] 0.21 0.29 8385 1.00
p_predicted[970] 0.21 0.29 8385 1.00
p_predicted[971] 0.20 0.29 8792 1.00
p_predicted[972] 0.20 0.29 8792 1.00
p_predicted[973] 0.20 0.29 8792 1.00
p_predicted[974] 0.20 0.28 9422 1.00
p_predicted[975] 0.20 0.28 9422 1.00
p_predicted[976] 0.20 0.28 9422 1.00
p_predicted[977] 0.00 0.02 8915 1.00
p_predicted[978] 0.00 0.01 9394 1.00
p_predicted[979] 0.00 0.01 9394 1.00
p_predicted[980] 0.00 0.01 9391 1.00
p_predicted[981] 0.00 0.02 8922 1.00
p_predicted[982] 0.00 0.01 9392 1.00
p_predicted[983] 0.00 0.01 9365 1.00
p_predicted[984] 0.00 0.01 9340 1.00
p_predicted[985] 0.00 0.01 9268 1.00
p_predicted[986] 0.00 0.01 9270 1.00
p_predicted[987] 0.00 0.01 9283 1.00
p_predicted[988] 0.00 0.01 9303 1.00
p_predicted[989] 0.00 0.01 9291 1.00
p_predicted[990] 0.00 0.01 9273 1.00
p_predicted[991] 0.00 0.01 9268 1.00
p_predicted[992] 0.00 0.01 9326 1.00
p_predicted[993] 0.00 0.02 9748 1.00
p_predicted[994] 0.00 0.02 10268 1.00
p_predicted[995] 0.00 0.02 10139 1.00
p_predicted[996] 0.99 1.00 10582 1.00
p_predicted[997] 0.99 1.00 9718 1.00
p_predicted[998] 1.00 1.00 10035 1.00
p_predicted[999] 0.00 0.01 8664 1.00
p_predicted[1000] 0.00 0.01 8625 1.00
p_predicted[1001] 0.00 0.01 9359 1.00
p_predicted[1002] 0.00 0.01 9346 1.00
p_predicted[1003] 0.00 0.01 9325 1.00
p_predicted[1004] 0.19 0.34 7140 1.00
p_predicted[1005] 0.21 0.37 5897 1.00
p_predicted[1006] 0.21 0.37 5896 1.00
p_predicted[1007] 0.17 0.31 5937 1.00
p_predicted[1008] 0.18 0.33 6148 1.00
p_predicted[1009] 0.37 0.57 5807 1.00
p_predicted[1010] 0.41 0.60 4785 1.00
p_predicted[1011] 0.46 0.68 4206 1.00
p_predicted[1012] 0.09 0.17 9584 1.00
p_predicted[1013] 0.06 0.12 10425 1.00
p_predicted[1014] 0.06 0.11 10604 1.00
p_predicted[1015] 0.09 0.19 9926 1.00
p_predicted[1016] 0.08 0.17 9715 1.00
p_predicted[1017] 0.06 0.12 10571 1.00
p_predicted[1018] 0.06 0.12 10605 1.00
p_predicted[1019] 0.06 0.11 10610 1.00
p_predicted[1020] 0.05 0.12 10284 1.00
p_predicted[1021] 0.05 0.11 10240 1.00
p_predicted[1022] 0.05 0.11 10089 1.00
p_predicted[1023] 0.05 0.11 10020 1.00
p_predicted[1024] 0.05 0.12 10029 1.00
p_predicted[1025] 0.05 0.12 10128 1.00
p_predicted[1026] 0.05 0.12 10202 1.00
p_predicted[1027] 0.09 0.19 9959 1.00
p_predicted[1028] 0.09 0.17 9638 1.00
p_predicted[1029] 0.08 0.15 9766 1.00
p_predicted[1030] 0.06 0.12 10608 1.00
p_predicted[1031] 0.06 0.11 10610 1.00
p_predicted[1032] 0.05 0.11 10280 1.00
p_predicted[1033] 0.05 0.11 10204 1.00
p_predicted[1034] 0.05 0.11 10144 1.00
p_predicted[1035] 0.05 0.11 10023 1.00
p_predicted[1036] 0.05 0.11 10005 1.00
p_predicted[1037] 0.05 0.12 10095 1.00
p_predicted[1038] 0.05 0.12 7798 1.00
p_predicted[1039] 0.06 0.11 7912 1.00
p_predicted[1040] 0.05 0.11 7732 1.00
p_predicted[1041] 0.06 0.11 7815 1.00
p_predicted[1042] 0.05 0.11 7626 1.00
p_predicted[1043] 0.05 0.11 7694 1.00
p_predicted[1044] 0.05 0.11 7633 1.00
p_predicted[1045] 0.05 0.11 7744 1.00
p_predicted[1046] 0.05 0.11 7609 1.00
p_predicted[1047] 0.05 0.11 7725 1.00
p_predicted[1048] 0.05 0.11 7602 1.00
p_predicted[1049] 0.05 0.11 7654 1.00
p_predicted[1050] 0.04 0.09 8605 1.00
p_predicted[1051] 0.04 0.09 8300 1.00
p_predicted[1052] 0.13 0.26 10740 1.00
p_predicted[1053] 0.13 0.26 10740 1.00
p_predicted[1054] 0.12 0.23 10881 1.00
p_predicted[1055] 0.12 0.23 10881 1.00
p_predicted[1056] 0.12 0.22 10716 1.00
p_predicted[1057] 0.12 0.22 10716 1.00
p_predicted[1058] 0.12 0.22 10662 1.00
p_predicted[1059] 0.12 0.22 10662 1.00
p_predicted[1060] 0.11 0.23 11110 1.00
p_predicted[1061] 0.11 0.23 11110 1.00
p_predicted[1062] 0.13 0.25 10792 1.00
p_predicted[1063] 0.12 0.24 10894 1.00
p_predicted[1064] 0.12 0.23 10889 1.00
p_predicted[1065] 0.12 0.22 10724 1.00
p_predicted[1066] 0.12 0.22 10692 1.00
p_predicted[1067] 0.12 0.22 10789 1.00
p_predicted[1068] 0.12 0.22 10700 1.00
p_predicted[1069] 0.11 0.23 11156 1.00
p_predicted[1070] 0.11 0.23 11102 1.00
p_predicted[1071] 0.00 0.06 10812 1.00
p_predicted[1072] 0.00 0.06 10781 1.00
p_predicted[1073] 0.00 0.06 10766 1.00
p_predicted[1074] 0.16 0.23 7570 1.00
p_predicted[1075] 0.16 0.23 7570 1.00
p_predicted[1076] 0.20 0.27 7985 1.00
p_predicted[1077] 0.20 0.27 7985 1.00
p_predicted[1078] 0.16 0.23 8213 1.00
p_predicted[1079] 0.16 0.23 8213 1.00
p_predicted[1080] 0.16 0.23 8419 1.00
p_predicted[1081] 0.16 0.23 8419 1.00
p_predicted[1082] 0.16 0.23 8891 1.00
p_predicted[1083] 0.16 0.23 8891 1.00
p_predicted[1084] 0.17 0.25 1806 1.00
p_predicted[1085] 0.20 0.26 7620 1.00
p_predicted[1086] 0.15 0.20 5555 1.00
p_predicted[1087] 0.14 0.19 6764 1.00
p_predicted[1088] 0.13 0.17 9212 1.00
p_predicted[1089] 0.11 0.15 5153 1.00
p_predicted[1090] 0.11 0.15 4424 1.00
p_predicted[1091] 0.22 0.32 9504 1.00
p_predicted[1092] 0.22 0.32 9390 1.00
p_predicted[1093] 0.22 0.32 9572 1.00
p_predicted[1094] 0.22 0.32 9458 1.00
p_predicted[1095] 0.21 0.30 8497 1.00
p_predicted[1096] 0.22 0.31 9340 1.00
p_predicted[1097] 0.21 0.30 8494 1.00
p_predicted[1098] 0.22 0.31 9203 1.00
p_predicted[1099] 0.21 0.30 8559 1.00
p_predicted[1100] 0.21 0.30 8532 1.00
p_predicted[1101] 0.21 0.30 8368 1.00
p_predicted[1102] 0.20 0.29 8299 1.00
p_predicted[1103] 0.15 0.23 8823 1.00
p_predicted[1104] 0.15 0.22 8975 1.00
p_predicted[1105] 0.14 0.22 9200 1.00
p_predicted[1106] 0.48 0.58 5330 1.00
p_predicted[1107] 0.48 0.58 5407 1.00
p_predicted[1108] 0.46 0.56 5984 1.00
p_predicted[1109] 0.41 0.52 3013 1.00
p_predicted[1110] 0.25 0.35 3048 1.00
p_predicted[1111] 0.28 0.37 6808 1.00
p_predicted[1112] 0.27 0.35 6353 1.00
p_predicted[1113] 0.27 0.35 6283 1.00
p_predicted[1114] 0.21 0.28 6847 1.00
p_predicted[1115] 0.23 0.31 4365 1.00
p_predicted[1116] 0.25 0.31 6239 1.00
p_predicted[1117] 0.24 0.30 6958 1.00
p_predicted[1118] 0.22 0.28 6292 1.00
p_predicted[1119] 0.17 0.22 5341 1.00
p_predicted[1120] 0.17 0.21 5648 1.00
p_predicted[1121] 0.16 0.20 6016 1.00
p_predicted[1122] 0.16 0.20 5818 1.00
p_predicted[1123] 0.16 0.21 6062 1.00
p_predicted[1124] 0.16 0.20 5969 1.00
p_predicted[1125] 0.17 0.21 6000 1.00
p_predicted[1126] 0.15 0.20 5396 1.00
p_predicted[1127] 0.35 0.53 3361 1.00
p_predicted[1128] 0.28 0.38 9464 1.00
p_predicted[1129] 0.27 0.37 9151 1.00
p_predicted[1130] 0.21 0.29 8940 1.00
p_predicted[1131] 0.21 0.29 9886 1.00
p_predicted[1132] 0.09 0.27 8624 1.00
p_predicted[1133] 0.00 0.05 10169 1.00
p_predicted[1134] 0.00 0.05 10477 1.00
p_predicted[1135] 0.02 0.08 5080 1.00
p_predicted[1136] 0.01 0.05 4925 1.00
p_predicted[1137] 0.02 0.08 5080 1.00
p_predicted[1138] 0.01 0.07 4867 1.00
p_predicted[1139] 0.01 0.04 4706 1.00
p_predicted[1140] 0.01 0.07 4867 1.00
p_predicted[1141] 0.02 0.09 4933 1.00
p_predicted[1142] 0.01 0.05 4770 1.00
p_predicted[1143] 0.02 0.09 4933 1.00
p_predicted[1144] 0.01 0.07 4847 1.00
p_predicted[1145] 0.01 0.04 4685 1.00
p_predicted[1146] 0.01 0.07 4847 1.00
p_predicted[1147] 0.09 0.14 5969 1.00
p_predicted[1148] 0.06 0.11 6845 1.00
p_predicted[1149] 0.09 0.14 5969 1.00
p_predicted[1150] 0.07 0.12 5178 1.00
p_predicted[1151] 0.05 0.09 6460 1.00
p_predicted[1152] 0.07 0.12 5178 1.00
p_predicted[1153] 0.07 0.11 5243 1.00
p_predicted[1154] 0.05 0.09 6557 1.00
p_predicted[1155] 0.07 0.11 5243 1.00
p_predicted[1156] 0.12 0.19 5209 1.00
p_predicted[1157] 0.08 0.14 6069 1.00
p_predicted[1158] 0.12 0.19 5209 1.00
p_predicted[1159] 0.04 0.10 6357 1.00
p_predicted[1160] 0.04 0.08 6098 1.00
p_predicted[1161] 0.04 0.08 6148 1.00
p_predicted[1162] 0.03 0.07 6366 1.00
p_predicted[1163] 0.03 0.07 6409 1.00
p_predicted[1164] 0.03 0.07 6649 1.00
p_predicted[1165] 0.07 0.13 6689 1.00
p_predicted[1166] 0.05 0.11 8833 1.00
p_predicted[1167] 0.07 0.13 6689 1.00
p_predicted[1168] 0.05 0.10 7218 1.00
p_predicted[1169] 0.04 0.08 8814 1.00
p_predicted[1170] 0.05 0.10 7218 1.00
p_predicted[1171] 0.06 0.11 6402 1.00
p_predicted[1172] 0.04 0.09 7614 1.00
p_predicted[1173] 0.06 0.11 6402 1.00
p_predicted[1174] 0.90 0.98 9851 1.00
p_predicted[1175] 0.89 0.97 9852 1.00
p_predicted[1176] 0.88 0.97 9817 1.00
p_predicted[1177] 0.88 0.97 9835 1.00
p_predicted[1178] 0.88 0.97 10191 1.00
p_predicted[1179] 0.10 0.18 2615 1.00
p_predicted[1180] 0.13 0.19 7210 1.00
p_predicted[1181] 0.07 0.10 10255 1.00
p_predicted[1182] 0.08 0.13 5584 1.00
p_predicted[1183] 0.06 0.09 10786 1.00
p_predicted[1184] 0.05 0.08 5371 1.00
p_predicted[1185] 0.05 0.08 3439 1.00
p_predicted[1186] 0.67 0.87 9391 1.00
p_predicted[1187] 0.64 0.86 10141 1.00
p_predicted[1188] 0.64 0.86 10157 1.00
p_predicted[1189] 0.09 0.17 9635 1.00
p_predicted[1190] 0.06 0.12 10599 1.00
p_predicted[1191] 0.06 0.11 10595 1.00
p_predicted[1192] 0.06 0.11 10559 1.00
p_predicted[1193] 0.05 0.11 9986 1.00
p_predicted[1194] 0.09 0.17 9627 1.00
p_predicted[1195] 0.06 0.12 10596 1.00
p_predicted[1196] 0.06 0.12 10610 1.00
p_predicted[1197] 0.06 0.11 10595 1.00
p_predicted[1198] 0.05 0.11 10119 1.00
p_predicted[1199] 0.67 0.80 9646 1.00
p_predicted[1200] 0.67 0.80 9645 1.00
p_predicted[1201] 0.74 0.87 5100 1.00
p_predicted[1202] 0.64 0.78 9645 1.00
p_predicted[1203] 0.64 0.78 9508 1.00
p_predicted[1204] 0.64 0.78 9498 1.00
p_predicted[1205] 0.64 0.77 9490 1.00
p_predicted[1206] 0.64 0.78 9533 1.00
p_predicted[1207] 0.58 0.74 9109 1.00
p_predicted[1208] 0.57 0.73 10052 1.00
p_predicted[1209] 0.00 0.02 9970 1.00
p_predicted[1210] 0.00 0.02 10373 1.00
p_predicted[1211] 0.08 0.18 10525 1.00
p_predicted[1212] 0.09 0.18 10484 1.00
p_predicted[1213] 0.08 0.17 10599 1.00
p_predicted[1214] 0.06 0.12 10989 1.00
p_predicted[1215] 0.06 0.12 10808 1.00
p_predicted[1216] 0.05 0.12 10319 1.00
p_predicted[1217] 0.32 0.44 9278 1.00
p_predicted[1218] 0.31 0.44 9013 1.00
p_predicted[1219] 0.25 0.35 7916 1.00
p_predicted[1220] 0.25 0.35 7855 1.00
p_predicted[1221] 0.25 0.35 8416 1.00
p_predicted[1222] 0.11 0.24 10758 1.00
p_predicted[1223] 0.11 0.24 10396 1.00
p_predicted[1224] 0.11 0.23 10143 1.00
p_predicted[1225] 0.07 0.17 10354 1.00
p_predicted[1226] 0.07 0.17 10374 1.00
p_predicted[1227] 0.07 0.17 10352 1.00
p_predicted[1228] 0.07 0.17 10373 1.00
p_predicted[1229] 0.73 0.86 12246 1.00
p_predicted[1230] 0.73 0.86 11144 1.00
p_predicted[1231] 0.67 0.82 10128 1.00
p_predicted[1232] 0.67 0.82 10012 1.00
p_predicted[1233] 0.33 0.39 3757 1.00
p_predicted[1234] 0.33 0.39 3757 1.00
p_predicted[1235] 0.31 0.36 3390 1.00
p_predicted[1236] 0.31 0.36 3390 1.00
p_predicted[1237] 0.30 0.35 2783 1.00
p_predicted[1238] 0.30 0.35 2783 1.00
p_predicted[1239] 0.25 0.30 2581 1.00
p_predicted[1240] 0.25 0.30 2581 1.00
p_predicted[1241] 0.23 0.28 2846 1.00
p_predicted[1242] 0.23 0.28 2846 1.00
p_predicted[1243] 0.22 0.27 2444 1.00
p_predicted[1244] 0.22 0.27 2444 1.00
p_predicted[1245] 0.22 0.27 2573 1.00
p_predicted[1246] 0.22 0.27 2573 1.00
p_predicted[1247] 0.22 0.26 2375 1.00
p_predicted[1248] 0.22 0.26 2375 1.00
p_predicted[1249] 0.18 0.32 9613 1.00
p_predicted[1250] 0.12 0.22 9418 1.00
p_predicted[1251] 0.18 0.32 9613 1.00
p_predicted[1252] 0.20 0.33 9481 1.00
p_predicted[1253] 0.14 0.22 9051 1.00
p_predicted[1254] 0.20 0.33 9481 1.00
p_predicted[1255] 0.20 0.33 9485 1.00
p_predicted[1256] 0.14 0.22 9153 1.00
p_predicted[1257] 0.20 0.33 9485 1.00
p_predicted[1258] 0.20 0.33 9470 1.00
p_predicted[1259] 0.14 0.23 9284 1.00
p_predicted[1260] 0.20 0.33 9470 1.00
p_predicted[1261] 0.24 0.38 10060 1.00
p_predicted[1262] 0.16 0.27 9917 1.00
p_predicted[1263] 0.24 0.38 10060 1.00
p_predicted[1264] 0.19 0.34 7777 1.00
p_predicted[1265] 0.13 0.23 7554 1.00
p_predicted[1266] 0.19 0.34 7777 1.00
p_predicted[1267] 0.05 0.11 10827 1.00
p_predicted[1268] 0.05 0.11 10831 1.00
p_predicted[1269] 0.04 0.11 10767 1.00
p_predicted[1270] 0.04 0.11 10697 1.00
p_predicted[1271] 0.04 0.11 10452 1.00
p_predicted[1272] 0.10 0.21 6730 1.00
p_predicted[1273] 0.07 0.18 6012 1.00
p_predicted[1274] 0.03 0.07 6138 1.00
p_predicted[1275] 0.03 0.07 6514 1.00
p_predicted[1276] 0.03 0.07 6642 1.00
p_predicted[1277] 0.03 0.07 6643 1.00
p_predicted[1278] 0.09 0.14 8116 1.00
p_predicted[1279] 0.09 0.14 8116 1.00
p_predicted[1280] 0.06 0.10 8003 1.00
p_predicted[1281] 0.06 0.10 8003 1.00
p_predicted[1282] 0.01 0.02 4944 1.00
p_predicted[1283] 0.01 0.02 5600 1.00
p_predicted[1284] 0.01 0.02 5570 1.00
p_predicted[1285] 0.01 0.02 5564 1.00
p_predicted[1286] 0.01 0.02 4812 1.00
p_predicted[1287] 0.01 0.02 4808 1.00
p_predicted[1288] 0.01 0.02 4811 1.00
p_predicted[1289] 0.01 0.02 4830 1.00
p_predicted[1290] 0.01 0.02 4961 1.00
p_predicted[1291] 0.01 0.02 4962 1.00
p_predicted[1292] 0.01 0.02 4971 1.00
p_predicted[1293] 0.04 0.08 6152 1.00
p_predicted[1294] 0.03 0.07 6286 1.00
p_predicted[1295] 0.03 0.07 6326 1.00
p_predicted[1296] 0.03 0.07 6667 1.00
p_predicted[1297] 0.14 0.24 8304 1.00
p_predicted[1298] 0.16 0.26 6815 1.00
p_predicted[1299] 0.24 0.37 6325 1.00
p_predicted[1300] 0.25 0.39 6389 1.00
p_predicted[1301] 0.20 0.33 5325 1.00
p_predicted[1302] 0.43 0.61 2968 1.00
p_predicted[1303] 0.43 0.61 2968 1.00
p_predicted[1304] 0.33 0.45 7512 1.00
p_predicted[1305] 0.33 0.45 7512 1.00
p_predicted[1306] 0.34 0.45 8015 1.00
p_predicted[1307] 0.34 0.45 8015 1.00
p_predicted[1308] 0.27 0.37 7974 1.00
p_predicted[1309] 0.27 0.37 7974 1.00
p_predicted[1310] 0.26 0.35 7571 1.00
p_predicted[1311] 0.26 0.35 7571 1.00
p_predicted[1312] 0.24 0.33 8416 1.00
p_predicted[1313] 0.24 0.33 8416 1.00
p_predicted[1314] 0.24 0.33 8595 1.00
p_predicted[1315] 0.24 0.33 8595 1.00
p_predicted[1316] 0.10 0.21 11416 1.00
p_predicted[1317] 0.09 0.18 11563 1.00
p_predicted[1318] 0.09 0.18 11556 1.00
p_predicted[1319] 0.08 0.17 11428 1.00
p_predicted[1320] 0.08 0.17 11597 1.00
p_predicted[1321] 0.08 0.17 11519 1.00
p_predicted[1322] 0.08 0.17 11276 1.00
p_predicted[1323] 0.08 0.17 11202 1.00
p_predicted[1324] 0.10 0.21 11416 1.00
p_predicted[1325] 0.09 0.18 11563 1.00
p_predicted[1326] 0.09 0.18 11556 1.00
p_predicted[1327] 0.08 0.17 11428 1.00
p_predicted[1328] 0.08 0.17 11595 1.00
p_predicted[1329] 0.08 0.17 11519 1.00
p_predicted[1330] 0.08 0.17 11207 1.00
p_predicted[1331] 0.32 0.43 8511 1.00
p_predicted[1332] 0.26 0.36 6861 1.00
p_predicted[1333] 0.25 0.35 8784 1.00
p_predicted[1334] 0.04 0.10 6359 1.00
p_predicted[1335] 0.04 0.10 6375 1.00
p_predicted[1336] 0.04 0.08 6128 1.00
p_predicted[1337] 0.03 0.08 6224 1.00
p_predicted[1338] 0.03 0.08 6240 1.00
p_predicted[1339] 0.03 0.08 6260 1.00
p_predicted[1340] 0.03 0.07 6264 1.00
p_predicted[1341] 0.03 0.07 6264 1.00
p_predicted[1342] 0.03 0.07 6303 1.00
p_predicted[1343] 0.03 0.07 6509 1.00
p_predicted_default[1] 0.26 0.37 8347 1.00
p_predicted_default[2] 0.26 0.36 8069 1.00
p_predicted_default[3] 0.26 0.36 7877 1.00
p_predicted_default[4] 0.23 0.33 7014 1.00
p_predicted_default[5] 0.23 0.33 6999 1.00
p_predicted_default[6] 0.23 0.33 6981 1.00
p_predicted_default[7] 0.22 0.33 7411 1.00
p_predicted_default[8] 0.22 0.33 7397 1.00
p_predicted_default[9] 0.43 0.53 5322 1.00
p_predicted_default[10] 0.44 0.55 6139 1.00
p_predicted_default[11] 0.01 0.02 5561 1.00
p_predicted_default[12] 0.01 0.02 4740 1.00
p_predicted_default[13] 0.01 0.02 4907 1.00
p_predicted_default[14] 0.01 0.02 4934 1.00
p_predicted_default[15] 0.01 0.02 4798 1.00
p_predicted_default[16] 0.03 0.06 4806 1.00
p_predicted_default[17] 0.02 0.06 4820 1.00
p_predicted_default[18] 0.02 0.06 4834 1.00
p_predicted_default[19] 0.02 0.06 4840 1.00
p_predicted_default[20] 0.02 0.06 4834 1.00
p_predicted_default[21] 0.12 0.24 9692 1.00
p_predicted_default[22] 0.11 0.23 9955 1.00
p_predicted_default[23] 0.41 0.47 5904 1.00
p_predicted_default[24] 0.39 0.45 7275 1.00
p_predicted_default[25] 0.36 0.42 3271 1.00
p_predicted_default[26] 0.30 0.35 4739 1.00
p_predicted_default[27] 0.29 0.34 4882 1.00
p_predicted_default[28] 0.27 0.33 3519 1.00
p_predicted_default[29] 0.26 0.32 2561 1.00
p_predicted_default[30] 0.36 0.42 5728 1.00
p_predicted_default[31] 0.36 0.42 5728 1.00
p_predicted_default[32] 0.33 0.39 6587 1.00
p_predicted_default[33] 0.33 0.39 6587 1.00
p_predicted_default[34] 0.32 0.37 4585 1.00
p_predicted_default[35] 0.32 0.37 4585 1.00
p_predicted_default[36] 0.31 0.37 3650 1.00
p_predicted_default[37] 0.31 0.37 3650 1.00
p_predicted_default[38] 0.23 0.28 4909 1.00
p_predicted_default[39] 0.23 0.28 4909 1.00
p_predicted_default[40] 0.16 0.19 6914 1.00
p_predicted_default[41] 0.16 0.19 6914 1.00
p_predicted_default[42] 0.16 0.19 7026 1.00
p_predicted_default[43] 0.16 0.19 7026 1.00
p_predicted_default[44] 0.16 0.19 6923 1.00
p_predicted_default[45] 0.16 0.19 6923 1.00
p_predicted_default[46] 0.16 0.19 6879 1.00
p_predicted_default[47] 0.16 0.19 6879 1.00
p_predicted_default[48] 0.15 0.19 6012 1.00
p_predicted_default[49] 0.15 0.19 6012 1.00
p_predicted_default[50] 0.14 0.25 4000 1.00
p_predicted_default[51] 0.11 0.17 8146 1.00
p_predicted_default[52] 0.10 0.17 8101 1.00
p_predicted_default[53] 0.10 0.16 7972 1.00
p_predicted_default[54] 0.08 0.13 8003 1.00
p_predicted_default[55] 0.07 0.13 8497 1.00
p_predicted_default[56] 0.07 0.13 8686 1.00
p_predicted_default[57] 0.07 0.14 10099 1.00
p_predicted_default[58] 0.09 0.16 4313 1.00
p_predicted_default[59] 0.09 0.16 4317 1.00
p_predicted_default[60] 0.09 0.16 4318 1.00
p_predicted_default[61] 0.06 0.10 8042 1.00
p_predicted_default[62] 0.06 0.10 7783 1.00
p_predicted_default[63] 0.05 0.08 7245 1.00
p_predicted_default[64] 0.05 0.08 7376 1.00
p_predicted_default[65] 0.05 0.08 7015 1.00
p_predicted_default[66] 0.04 0.07 8160 1.00
p_predicted_default[67] 0.04 0.08 9355 1.00
p_predicted_default[68] 0.04 0.08 9709 1.00
p_predicted_default[69] 0.04 0.12 7911 1.00
p_predicted_default[70] 0.04 0.09 9240 1.00
p_predicted_default[71] 0.23 0.31 5979 1.00
p_predicted_default[72] 0.22 0.30 6813 1.00
p_predicted_default[73] 0.21 0.28 6796 1.00
p_predicted_default[74] 0.21 0.28 6707 1.00
p_predicted_default[75] 0.16 0.24 2014 1.00
p_predicted_default[76] 0.72 0.83 9820 1.00
p_predicted_default[77] 0.71 0.82 9636 1.00
p_predicted_default[78] 0.71 0.82 9614 1.00
p_predicted_default[79] 0.72 0.83 9820 1.00
p_predicted_default[80] 0.71 0.82 9636 1.00
p_predicted_default[81] 0.71 0.82 9601 1.00
p_predicted_default[82] 0.72 0.83 9811 1.00
p_predicted_default[83] 0.71 0.82 9647 1.00
p_predicted_default[84] 0.71 0.82 9608 1.00
p_predicted_default[85] 0.62 0.72 8728 1.00
p_predicted_default[86] 0.42 0.50 6787 1.00
p_predicted_default[87] 0.36 0.45 4662 1.00
p_predicted_default[88] 0.18 0.23 4210 1.00
p_predicted_default[89] 0.17 0.21 5454 1.00
p_predicted_default[90] 0.01 0.03 4978 1.00
p_predicted_default[91] 0.01 0.03 4930 1.00
p_predicted_default[92] 0.01 0.03 4888 1.00
p_predicted_default[93] 0.01 0.03 4864 1.00
p_predicted_default[94] 0.72 0.85 11013 1.00
p_predicted_default[95] 0.70 0.83 9822 1.00
p_predicted_default[96] 0.67 0.80 10064 1.00
p_predicted_default[97] 0.45 0.63 8329 1.00
p_predicted_default[98] 0.44 0.62 8795 1.00
p_predicted_default[99] 0.25 0.35 1757 1.00
p_predicted_default[100] 0.25 0.35 1755 1.00
p_predicted_default[101] 0.29 0.36 4584 1.00
p_predicted_default[102] 0.24 0.30 6914 1.00
p_predicted_default[103] 0.20 0.25 4937 1.00
p_predicted_default[104] 0.01 0.06 6821 1.00
p_predicted_default[105] 0.01 0.05 6549 1.00
p_predicted_default[106] 0.01 0.05 6551 1.00
p_predicted_default[107] 0.01 0.05 6590 1.00
p_predicted_default[108] 0.01 0.05 6593 1.00
p_predicted_default[109] 0.01 0.04 6720 1.00
p_predicted_default[110] 0.01 0.04 6729 1.00
p_predicted_default[111] 0.01 0.04 6775 1.00
p_predicted_default[112] 0.01 0.04 6748 1.00
p_predicted_default[113] 0.01 0.04 6783 1.00
p_predicted_default[114] 0.01 0.04 6849 1.00
p_predicted_default[115] 0.01 0.04 6862 1.00
p_predicted_default[116] 0.01 0.04 6920 1.00
p_predicted_default[117] 0.01 0.02 5138 1.00
p_predicted_default[118] 0.01 0.02 5123 1.00
p_predicted_default[119] 0.01 0.02 5118 1.00
p_predicted_default[120] 0.00 0.02 5594 1.00
p_predicted_default[121] 0.00 0.02 5621 1.00
p_predicted_default[122] 0.01 0.02 4865 1.00
p_predicted_default[123] 0.01 0.02 4869 1.00
p_predicted_default[124] 0.01 0.02 4884 1.00
p_predicted_default[125] 0.01 0.02 4895 1.00
p_predicted_default[126] 0.01 0.02 4899 1.00
p_predicted_default[127] 0.01 0.02 4901 1.00
p_predicted_default[128] 0.13 0.22 9912 1.00
p_predicted_default[129] 0.09 0.20 9651 1.00
p_predicted_default[130] 0.13 0.22 9912 1.00
p_predicted_default[131] 0.00 0.06 10668 1.00
p_predicted_default[132] 0.00 0.06 10783 1.00
p_predicted_default[133] 0.00 0.06 10775 1.00
p_predicted_default[134] 0.00 0.06 10762 1.00
p_predicted_default[135] 0.00 0.06 10760 1.00
p_predicted_default[136] 0.30 0.40 1633 1.00
p_predicted_default[137] 0.35 0.43 5955 1.00
p_predicted_default[138] 0.34 0.42 6076 1.00
p_predicted_default[139] 0.35 0.43 5764 1.00
p_predicted_default[140] 0.31 0.38 7825 1.00
p_predicted_default[141] 0.28 0.36 4809 1.00
p_predicted_default[142] 0.23 0.29 7118 1.00
p_predicted_default[143] 0.23 0.28 7698 1.00
p_predicted_default[144] 0.22 0.28 7717 1.00
p_predicted_default[145] 0.22 0.28 7802 1.00
p_predicted_default[146] 0.22 0.27 7559 1.00
p_predicted_default[147] 0.22 0.27 7384 1.00
p_predicted_default[148] 0.20 0.26 4536 1.00
p_predicted_default[149] 0.17 0.24 2295 1.00
p_predicted_default[150] 0.11 0.20 2570 1.00
p_predicted_default[151] 0.14 0.21 3786 1.00
p_predicted_default[152] 0.14 0.21 3766 1.00
p_predicted_default[153] 0.09 0.14 4169 1.00
p_predicted_default[154] 0.10 0.15 3793 1.00
p_predicted_default[155] 0.11 0.16 4493 1.00
p_predicted_default[156] 0.11 0.16 4299 1.00
p_predicted_default[157] 0.09 0.14 4191 1.00
p_predicted_default[158] 0.10 0.15 4058 1.00
p_predicted_default[159] 0.08 0.12 5446 1.00
p_predicted_default[160] 0.06 0.09 5431 1.00
p_predicted_default[161] 0.05 0.08 4207 1.00
p_predicted_default[162] 0.05 0.08 3654 1.00
p_predicted_default[163] 0.16 0.22 7336 1.00
p_predicted_default[164] 0.16 0.22 7336 1.00
p_predicted_default[165] 0.09 0.13 10729 1.00
p_predicted_default[166] 0.09 0.13 10729 1.00
p_predicted_default[167] 0.09 0.12 10620 1.00
p_predicted_default[168] 0.09 0.12 10620 1.00
p_predicted_default[169] 0.07 0.11 10890 1.00
p_predicted_default[170] 0.07 0.11 10890 1.00
p_predicted_default[171] 0.41 0.47 5908 1.00
p_predicted_default[172] 0.38 0.44 6546 1.00
p_predicted_default[173] 0.29 0.34 4973 1.00
p_predicted_default[174] 0.15 0.23 1564 1.00
p_predicted_default[175] 0.09 0.16 4305 1.00
p_predicted_default[176] 0.09 0.16 4283 1.00
p_predicted_default[177] 0.06 0.10 8061 1.00
p_predicted_default[178] 0.06 0.10 7547 1.00
p_predicted_default[179] 0.06 0.10 8020 1.00
p_predicted_default[180] 0.06 0.10 7780 1.00
p_predicted_default[181] 0.06 0.10 7641 1.00
p_predicted_default[182] 0.04 0.07 7777 1.00
p_predicted_default[183] 0.04 0.07 7941 1.00
p_predicted_default[184] 0.04 0.07 8460 1.00
p_predicted_default[185] 0.04 0.07 8763 1.00
p_predicted_default[186] 0.04 0.07 8822 1.00
p_predicted_default[187] 0.17 0.28 3389 1.00
p_predicted_default[188] 0.17 0.28 3381 1.00
p_predicted_default[189] 0.12 0.18 8720 1.00
p_predicted_default[190] 0.12 0.17 8287 1.00
p_predicted_default[191] 0.12 0.17 8082 1.00
p_predicted_default[192] 0.12 0.17 8353 1.00
p_predicted_default[193] 0.09 0.14 7042 1.00
p_predicted_default[194] 0.09 0.13 7824 1.00
p_predicted_default[195] 0.09 0.13 7980 1.00
p_predicted_default[196] 0.09 0.13 8174 1.00
p_predicted_default[197] 0.17 0.28 3389 1.00
p_predicted_default[198] 0.17 0.28 3392 1.00
p_predicted_default[199] 0.17 0.28 3375 1.00
p_predicted_default[200] 0.12 0.18 8720 1.00
p_predicted_default[201] 0.12 0.17 8082 1.00
p_predicted_default[202] 0.09 0.13 8339 1.00
p_predicted_default[203] 0.09 0.13 9089 1.00
p_predicted_default[204] 0.09 0.13 7826 1.00
p_predicted_default[205] 0.01 0.14 9967 1.00
p_predicted_default[206] 0.01 0.13 9474 1.00
p_predicted_default[207] 0.01 0.13 9465 1.00
p_predicted_default[208] 0.20 0.27 4032 1.00
p_predicted_default[209] 0.15 0.20 3243 1.00
p_predicted_default[210] 0.16 0.21 5087 1.00
p_predicted_default[211] 0.16 0.21 5057 1.00
p_predicted_default[212] 0.13 0.18 5299 1.00
p_predicted_default[213] 0.10 0.14 6607 1.00
p_predicted_default[214] 0.09 0.13 7815 1.00
p_predicted_default[215] 0.10 0.13 7419 1.00
p_predicted_default[216] 0.67 0.80 11365 1.00
p_predicted_default[217] 0.67 0.78 9692 1.00
p_predicted_default[218] 0.66 0.78 9499 1.00
p_predicted_default[219] 0.43 0.61 8592 1.00
p_predicted_default[220] 0.43 0.61 8950 1.00
p_predicted_default[221] 0.67 0.78 9703 1.00
p_predicted_default[222] 0.66 0.77 9462 1.00
p_predicted_default[223] 0.43 0.61 8657 1.00
p_predicted_default[224] 0.42 0.61 8986 1.00
p_predicted_default[225] 0.42 0.61 9013 1.00
p_predicted_default[226] 0.34 0.53 6499 1.00
p_predicted_default[227] 0.36 0.54 6901 1.00
p_predicted_default[228] 0.36 0.53 6895 1.00
p_predicted_default[229] 0.36 0.54 6949 1.00
p_predicted_default[230] 0.36 0.54 6928 1.00
p_predicted_default[231] 0.11 0.19 4048 1.00
p_predicted_default[232] 0.08 0.12 8441 1.00
p_predicted_default[233] 0.06 0.09 7358 1.00
p_predicted_default[234] 0.05 0.08 9585 1.00
p_predicted_default[235] 0.05 0.08 9715 1.00
p_predicted_default[236] 0.01 0.13 9901 1.00
p_predicted_default[237] 0.01 0.13 9901 1.00
p_predicted_default[238] 0.01 0.14 9593 1.00
p_predicted_default[239] 0.01 0.14 9593 1.00
p_predicted_default[240] 0.29 0.57 5815 1.00
p_predicted_default[241] 0.26 0.52 5398 1.00
p_predicted_default[242] 0.26 0.52 5404 1.00
p_predicted_default[243] 0.24 0.49 5489 1.00
p_predicted_default[244] 0.46 0.57 1634 1.00
p_predicted_default[245] 0.46 0.57 1634 1.00
p_predicted_default[246] 0.46 0.57 1634 1.00
p_predicted_default[247] 0.46 0.57 1632 1.00
p_predicted_default[248] 0.46 0.57 1632 1.00
p_predicted_default[249] 0.46 0.57 1632 1.00
p_predicted_default[250] 0.51 0.58 8355 1.00
p_predicted_default[251] 0.51 0.58 8355 1.00
p_predicted_default[252] 0.51 0.58 8355 1.00
p_predicted_default[253] 0.50 0.57 8237 1.00
p_predicted_default[254] 0.50 0.57 8237 1.00
p_predicted_default[255] 0.50 0.57 8237 1.00
p_predicted_default[256] 0.49 0.56 7198 1.00
p_predicted_default[257] 0.49 0.56 7198 1.00
p_predicted_default[258] 0.49 0.56 7198 1.00
p_predicted_default[259] 0.48 0.56 6406 1.00
p_predicted_default[260] 0.48 0.56 6406 1.00
p_predicted_default[261] 0.48 0.56 6406 1.00
p_predicted_default[262] 0.42 0.50 4471 1.00
p_predicted_default[263] 0.42 0.50 4471 1.00
p_predicted_default[264] 0.42 0.50 4471 1.00
p_predicted_default[265] 0.39 0.47 5242 1.00
p_predicted_default[266] 0.39 0.47 5242 1.00
p_predicted_default[267] 0.39 0.47 5242 1.00
p_predicted_default[268] 0.38 0.46 5068 1.00
p_predicted_default[269] 0.38 0.46 5068 1.00
p_predicted_default[270] 0.38 0.46 5068 1.00
p_predicted_default[271] 0.22 0.27 6405 1.00
p_predicted_default[272] 0.22 0.27 6405 1.00
p_predicted_default[273] 0.22 0.27 6405 1.00
p_predicted_default[274] 0.22 0.26 5830 1.00
p_predicted_default[275] 0.22 0.26 5830 1.00
p_predicted_default[276] 0.22 0.26 5830 1.00
p_predicted_default[277] 0.03 0.07 7125 1.00
p_predicted_default[278] 0.03 0.07 7125 1.00
p_predicted_default[279] 0.03 0.07 7125 1.00
p_predicted_default[280] 0.03 0.07 7125 1.00
p_predicted_default[281] 0.03 0.07 7125 1.00
p_predicted_default[282] 0.03 0.07 7125 1.00
p_predicted_default[283] 0.03 0.07 7126 1.00
p_predicted_default[284] 0.03 0.07 7126 1.00
p_predicted_default[285] 0.03 0.05 8289 1.00
p_predicted_default[286] 0.03 0.05 8289 1.00
p_predicted_default[287] 0.03 0.07 6286 1.00
p_predicted_default[288] 0.03 0.07 6286 1.00
p_predicted_default[289] 0.02 0.05 8213 1.00
p_predicted_default[290] 0.02 0.05 8213 1.00
p_predicted_default[291] 0.02 0.05 8206 1.00
p_predicted_default[292] 0.02 0.05 8206 1.00
p_predicted_default[293] 0.05 0.09 10128 1.00
p_predicted_default[294] 0.05 0.09 10128 1.00
p_predicted_default[295] 0.05 0.09 10125 1.00
p_predicted_default[296] 0.05 0.09 10125 1.00
p_predicted_default[297] 0.05 0.09 10124 1.00
p_predicted_default[298] 0.05 0.09 10124 1.00
p_predicted_default[299] 0.05 0.09 10119 1.00
p_predicted_default[300] 0.05 0.09 10119 1.00
p_predicted_default[301] 0.05 0.09 10119 1.00
p_predicted_default[302] 0.05 0.09 10119 1.00
p_predicted_default[303] 0.05 0.09 10123 1.00
p_predicted_default[304] 0.05 0.09 10123 1.00
p_predicted_default[305] 0.05 0.09 10120 1.00
p_predicted_default[306] 0.05 0.09 10120 1.00
p_predicted_default[307] 0.05 0.09 10122 1.00
p_predicted_default[308] 0.05 0.09 10122 1.00
p_predicted_default[309] 0.05 0.09 10113 1.00
p_predicted_default[310] 0.05 0.09 10113 1.00
p_predicted_default[311] 0.05 0.09 10113 1.00
p_predicted_default[312] 0.05 0.09 10113 1.00
p_predicted_default[313] 0.05 0.09 10113 1.00
p_predicted_default[314] 0.05 0.09 10113 1.00
p_predicted_default[315] 0.05 0.09 10117 1.00
p_predicted_default[316] 0.05 0.09 10117 1.00
p_predicted_default[317] 0.05 0.09 10114 1.00
p_predicted_default[318] 0.05 0.09 10114 1.00
p_predicted_default[319] 0.05 0.09 10117 1.00
p_predicted_default[320] 0.05 0.09 10117 1.00
p_predicted_default[321] 0.06 0.12 7410 1.00
p_predicted_default[322] 0.06 0.12 7410 1.00
p_predicted_default[323] 0.06 0.12 7414 1.00
p_predicted_default[324] 0.06 0.12 7414 1.00
p_predicted_default[325] 0.12 0.18 4462 1.00
p_predicted_default[326] 0.16 0.22 3557 1.00
p_predicted_default[327] 0.18 0.26 4471 1.00
p_predicted_default[328] 0.10 0.15 4359 1.00
p_predicted_default[329] 0.11 0.17 5490 1.00
p_predicted_default[330] 0.08 0.12 4369 1.00
p_predicted_default[331] 0.09 0.14 5226 1.00
p_predicted_default[332] 0.06 0.10 4055 1.00
p_predicted_default[333] 0.07 0.12 5305 1.00
p_predicted_default[334] 0.06 0.10 4737 1.00
p_predicted_default[335] 0.07 0.11 6203 1.00
p_predicted_default[336] 0.05 0.09 4848 1.00
p_predicted_default[337] 0.06 0.10 6290 1.00
p_predicted_default[338] 0.38 0.62 11258 1.00
p_predicted_default[339] 0.34 0.56 11317 1.00
p_predicted_default[340] 0.33 0.54 11387 1.00
p_predicted_default[341] 0.33 0.54 11369 1.00
p_predicted_default[342] 0.33 0.54 11362 1.00
p_predicted_default[343] 0.33 0.54 11366 1.00
p_predicted_default[344] 0.32 0.53 11359 1.00
p_predicted_default[345] 0.36 0.59 8041 1.00
p_predicted_default[346] 0.47 0.56 2563 1.00
p_predicted_default[347] 0.47 0.56 2563 1.00
p_predicted_default[348] 0.51 0.58 8322 1.00
p_predicted_default[349] 0.51 0.58 8322 1.00
p_predicted_default[350] 0.40 0.46 4740 1.00
p_predicted_default[351] 0.40 0.46 4740 1.00
p_predicted_default[352] 0.30 0.36 3301 1.00
p_predicted_default[353] 0.30 0.36 3301 1.00
p_predicted_default[354] 0.26 0.42 6570 1.00
p_predicted_default[355] 0.27 0.46 6943 1.00
p_predicted_default[356] 0.32 0.46 10220 1.00
p_predicted_default[357] 0.33 0.52 11257 1.00
p_predicted_default[358] 0.25 0.34 6430 1.00
p_predicted_default[359] 0.26 0.37 8913 1.00
p_predicted_default[360] 0.23 0.33 6430 1.00
p_predicted_default[361] 0.23 0.33 7471 1.00
p_predicted_default[362] 0.17 0.25 5914 1.00
p_predicted_default[363] 0.17 0.26 5727 1.00
p_predicted_default[364] 0.17 0.25 5968 1.00
p_predicted_default[365] 0.17 0.26 5779 1.00
p_predicted_default[366] 0.17 0.26 6381 1.00
p_predicted_default[367] 0.17 0.27 6198 1.00
p_predicted_default[368] 0.41 0.48 6162 1.00
p_predicted_default[369] 0.41 0.48 6162 1.00
p_predicted_default[370] 0.41 0.48 6162 1.00
p_predicted_default[371] 0.24 0.31 6640 1.00
p_predicted_default[372] 0.24 0.31 6640 1.00
p_predicted_default[373] 0.24 0.31 6640 1.00
p_predicted_default[374] 0.24 0.30 7087 1.00
p_predicted_default[375] 0.24 0.30 7087 1.00
p_predicted_default[376] 0.24 0.30 7087 1.00
p_predicted_default[377] 0.16 0.20 7834 1.00
p_predicted_default[378] 0.16 0.20 7834 1.00
p_predicted_default[379] 0.16 0.20 7834 1.00
p_predicted_default[380] 0.16 0.20 8134 1.00
p_predicted_default[381] 0.16 0.20 8134 1.00
p_predicted_default[382] 0.16 0.20 8134 1.00
p_predicted_default[383] 0.16 0.20 8175 1.00
p_predicted_default[384] 0.16 0.20 8175 1.00
p_predicted_default[385] 0.16 0.20 8175 1.00
p_predicted_default[386] 0.16 0.20 8237 1.00
p_predicted_default[387] 0.16 0.20 8237 1.00
p_predicted_default[388] 0.16 0.20 8237 1.00
p_predicted_default[389] 0.16 0.20 8225 1.00
p_predicted_default[390] 0.16 0.20 8225 1.00
p_predicted_default[391] 0.16 0.20 8225 1.00
p_predicted_default[392] 0.16 0.20 8168 1.00
p_predicted_default[393] 0.16 0.20 8168 1.00
p_predicted_default[394] 0.16 0.20 8168 1.00
p_predicted_default[395] 0.16 0.20 8187 1.00
p_predicted_default[396] 0.16 0.20 8187 1.00
p_predicted_default[397] 0.16 0.20 8187 1.00
p_predicted_default[398] 0.16 0.20 8189 1.00
p_predicted_default[399] 0.16 0.20 8189 1.00
p_predicted_default[400] 0.16 0.20 8189 1.00
p_predicted_default[401] 0.16 0.20 8232 1.00
p_predicted_default[402] 0.16 0.20 8232 1.00
p_predicted_default[403] 0.16 0.20 8232 1.00
p_predicted_default[404] 0.16 0.20 8214 1.00
p_predicted_default[405] 0.16 0.20 8214 1.00
p_predicted_default[406] 0.16 0.20 8214 1.00
p_predicted_default[407] 0.16 0.20 8245 1.00
p_predicted_default[408] 0.16 0.20 8245 1.00
p_predicted_default[409] 0.16 0.20 8245 1.00
p_predicted_default[410] 0.04 0.09 9963 1.00
p_predicted_default[411] 0.04 0.09 9968 1.00
p_predicted_default[412] 0.04 0.09 9969 1.00
p_predicted_default[413] 0.05 0.10 9247 1.00
p_predicted_default[414] 0.38 0.49 1906 1.00
p_predicted_default[415] 0.31 0.37 5226 1.00
p_predicted_default[416] 0.30 0.36 5340 1.00
p_predicted_default[417] 0.29 0.35 5130 1.00
p_predicted_default[418] 0.30 0.36 5344 1.00
p_predicted_default[419] 0.30 0.37 5351 1.00
p_predicted_default[420] 0.26 0.34 2584 1.00
p_predicted_default[421] 0.28 0.35 4851 1.00
p_predicted_default[422] 0.23 0.29 4499 1.00
p_predicted_default[423] 0.27 0.34 4064 1.00
p_predicted_default[424] 0.23 0.29 4326 1.00
p_predicted_default[425] 0.23 0.29 4419 1.00
p_predicted_default[426] 0.17 0.21 4658 1.00
p_predicted_default[427] 0.25 0.31 3987 1.00
p_predicted_default[428] 0.23 0.28 5150 1.00
p_predicted_default[429] 0.22 0.27 5496 1.00
p_predicted_default[430] 0.09 0.13 6078 1.00
p_predicted_default[431] 0.08 0.11 7356 1.00
p_predicted_default[432] 0.06 0.09 6319 1.00
p_predicted_default[433] 0.42 0.52 5223 1.00
p_predicted_default[434] 0.37 0.47 6045 1.00
p_predicted_default[435] 0.30 0.39 5479 1.00
p_predicted_default[436] 0.29 0.38 5602 1.00
p_predicted_default[437] 0.33 0.43 3676 1.00
p_predicted_default[438] 0.21 0.31 1977 1.00
p_predicted_default[439] 0.19 0.30 1625 1.00
p_predicted_default[440] 0.19 0.30 1602 1.00
p_predicted_default[441] 0.17 0.30 1435 1.00
p_predicted_default[442] 0.30 0.41 1647 1.00
p_predicted_default[443] 0.35 0.43 5617 1.00
p_predicted_default[444] 0.33 0.40 7688 1.00
p_predicted_default[445] 0.33 0.41 7221 1.00
p_predicted_default[446] 0.32 0.40 7911 1.00
p_predicted_default[447] 0.29 0.37 6744 1.00
p_predicted_default[448] 0.31 0.38 8175 1.00
p_predicted_default[449] 0.26 0.33 4048 1.00
p_predicted_default[450] 0.25 0.31 5160 1.00
p_predicted_default[451] 0.29 0.36 5859 1.00
p_predicted_default[452] 0.24 0.30 6194 1.00
p_predicted_default[453] 0.24 0.30 6842 1.00
p_predicted_default[454] 0.22 0.28 7865 1.00
p_predicted_default[455] 0.22 0.28 7828 1.00
p_predicted_default[456] 0.21 0.32 1362 1.00
p_predicted_default[457] 0.16 0.24 1845 1.00
p_predicted_default[458] 0.16 0.24 1801 1.00
p_predicted_default[459] 0.15 0.23 1565 1.00
p_predicted_default[460] 0.30 0.40 1643 1.00
p_predicted_default[461] 0.35 0.43 5563 1.00
p_predicted_default[462] 0.31 0.38 8216 1.00
p_predicted_default[463] 0.29 0.36 6239 1.00
p_predicted_default[464] 0.28 0.36 5213 1.00
p_predicted_default[465] 0.27 0.35 4359 1.00
p_predicted_default[466] 0.22 0.28 7832 1.00
p_predicted_default[467] 0.22 0.28 7884 1.00
p_predicted_default[468] 0.22 0.28 7854 1.00
p_predicted_default[469] 0.27 0.37 1629 1.00
p_predicted_default[470] 0.31 0.38 9080 1.00
p_predicted_default[471] 0.28 0.35 4860 1.00
p_predicted_default[472] 0.29 0.36 6360 1.00
p_predicted_default[473] 0.22 0.29 6636 1.00
p_predicted_default[474] 0.23 0.30 6063 1.00
p_predicted_default[475] 0.23 0.29 6468 1.00
p_predicted_default[476] 0.22 0.28 6657 1.00
p_predicted_default[477] 0.12 0.17 3908 1.00
p_predicted_default[478] 0.11 0.16 2617 1.00
p_predicted_default[479] 0.10 0.16 1597 1.00
p_predicted_default[480] 0.09 0.15 1417 1.00
p_predicted_default[481] 0.25 0.34 1615 1.01
p_predicted_default[482] 0.28 0.34 5402 1.00
p_predicted_default[483] 0.29 0.35 5021 1.00
p_predicted_default[484] 0.19 0.23 6276 1.00
p_predicted_default[485] 0.18 0.22 7565 1.00
p_predicted_default[486] 0.18 0.21 7593 1.00
p_predicted_default[487] 0.18 0.22 7717 1.00
p_predicted_default[488] 0.18 0.21 7377 1.00
p_predicted_default[489] 0.17 0.21 6613 1.00
p_predicted_default[490] 0.17 0.21 5649 1.00
p_predicted_default[491] 0.09 0.15 8078 1.00
p_predicted_default[492] 0.09 0.15 8078 1.00
p_predicted_default[493] 0.09 0.15 8123 1.00
p_predicted_default[494] 0.09 0.15 8123 1.00
p_predicted_default[495] 0.10 0.16 7726 1.00
p_predicted_default[496] 0.10 0.16 7726 1.00
p_predicted_default[497] 0.08 0.13 6205 1.00
p_predicted_default[498] 0.08 0.13 6205 1.00
p_predicted_default[499] 0.08 0.13 6204 1.00
p_predicted_default[500] 0.08 0.13 6204 1.00
p_predicted_default[501] 0.27 0.37 1619 1.00
p_predicted_default[502] 0.31 0.39 8873 1.00
p_predicted_default[503] 0.23 0.29 6334 1.00
p_predicted_default[504] 0.23 0.29 6580 1.00
p_predicted_default[505] 0.22 0.28 6658 1.00
p_predicted_default[506] 0.22 0.28 6650 1.00
p_predicted_default[507] 0.22 0.28 6543 1.00
p_predicted_default[508] 0.13 0.17 5583 1.00
p_predicted_default[509] 0.12 0.17 3311 1.00
p_predicted_default[510] 0.10 0.16 1796 1.00
p_predicted_default[511] 0.10 0.15 1526 1.00
p_predicted_default[512] 0.00 0.06 10556 1.00
p_predicted_default[513] 0.00 0.06 10656 1.00
p_predicted_default[514] 0.00 0.06 10683 1.00
p_predicted_default[515] 0.00 0.05 10694 1.00
p_predicted_default[516] 0.00 0.05 10707 1.00
p_predicted_default[517] 0.00 0.05 10532 1.00
p_predicted_default[518] 0.00 0.05 10509 1.00
p_predicted_default[519] 0.00 0.05 10522 1.00
p_predicted_default[520] 0.00 0.05 10515 1.00
p_predicted_default[521] 0.00 0.05 10504 1.00
p_predicted_default[522] 0.00 0.06 10637 1.00
p_predicted_default[523] 0.24 0.37 1594 1.00
p_predicted_default[524] 0.22 0.36 1437 1.00
p_predicted_default[525] 0.53 0.61 8577 1.00
p_predicted_default[526] 0.53 0.61 8577 1.00
p_predicted_default[527] 0.53 0.61 8577 1.00
p_predicted_default[528] 0.51 0.59 5876 1.00
p_predicted_default[529] 0.51 0.59 5876 1.00
p_predicted_default[530] 0.51 0.59 5876 1.00
p_predicted_default[531] 0.33 0.39 6275 1.00
p_predicted_default[532] 0.33 0.39 6275 1.00
p_predicted_default[533] 0.33 0.39 6275 1.00
p_predicted_default[534] 0.32 0.38 5348 1.00
p_predicted_default[535] 0.32 0.38 5348 1.00
p_predicted_default[536] 0.32 0.38 5348 1.00
p_predicted_default[537] 0.27 0.32 4104 1.00
p_predicted_default[538] 0.27 0.32 4104 1.00
p_predicted_default[539] 0.27 0.32 4104 1.00
p_predicted_default[540] 0.41 0.49 7073 1.00
p_predicted_default[541] 0.41 0.49 7073 1.00
p_predicted_default[542] 0.41 0.49 7073 1.00
p_predicted_default[543] 0.39 0.47 6245 1.00
p_predicted_default[544] 0.39 0.47 6245 1.00
p_predicted_default[545] 0.39 0.47 6245 1.00
p_predicted_default[546] 0.23 0.29 4104 1.00
p_predicted_default[547] 0.23 0.29 4104 1.00
p_predicted_default[548] 0.23 0.29 4104 1.00
p_predicted_default[549] 0.24 0.30 3649 1.00
p_predicted_default[550] 0.24 0.30 3649 1.00
p_predicted_default[551] 0.24 0.30 3649 1.00
p_predicted_default[552] 0.03 0.07 7128 1.00
p_predicted_default[553] 0.03 0.07 7133 1.00
p_predicted_default[554] 0.03 0.07 7134 1.00
p_predicted_default[555] 0.03 0.05 8289 1.00
p_predicted_default[556] 0.02 0.05 8211 1.00
p_predicted_default[557] 0.02 0.05 8200 1.00
p_predicted_default[558] 0.05 0.09 10134 1.00
p_predicted_default[559] 0.05 0.09 10122 1.00
p_predicted_default[560] 0.05 0.09 10123 1.00
p_predicted_default[561] 0.05 0.09 10122 1.00
p_predicted_default[562] 0.05 0.09 10122 1.00
p_predicted_default[563] 0.05 0.09 10122 1.00
p_predicted_default[564] 0.05 0.09 10127 1.00
p_predicted_default[565] 0.05 0.09 10120 1.00
p_predicted_default[566] 0.05 0.09 10119 1.00
p_predicted_default[567] 0.05 0.09 10117 1.00
p_predicted_default[568] 0.05 0.09 10133 1.00
p_predicted_default[569] 0.05 0.09 10137 1.00
p_predicted_default[570] 0.05 0.09 10141 1.00
p_predicted_default[571] 0.00 0.04 10673 1.00
p_predicted_default[572] 0.00 0.04 10457 1.00
p_predicted_default[573] 0.00 0.04 10501 1.00
p_predicted_default[574] 0.00 0.04 10515 1.00
p_predicted_default[575] 0.00 0.04 10664 1.00
p_predicted_default[576] 0.19 0.31 9392 1.00
p_predicted_default[577] 0.10 0.17 6912 1.00
p_predicted_default[578] 0.10 0.17 8195 1.00
p_predicted_default[579] 0.11 0.22 8976 1.00
p_predicted_default[580] 0.11 0.22 8975 1.00
p_predicted_default[581] 0.08 0.17 9266 1.00
p_predicted_default[582] 0.30 0.40 1657 1.00
p_predicted_default[583] 0.35 0.43 5690 1.00
p_predicted_default[584] 0.28 0.36 5200 1.00
p_predicted_default[585] 0.23 0.30 7046 1.00
p_predicted_default[586] 0.21 0.27 7266 1.00
p_predicted_default[587] 0.16 0.28 6136 1.00
p_predicted_default[588] 0.15 0.27 5962 1.00
p_predicted_default[589] 0.20 0.31 6971 1.00
p_predicted_default[590] 0.19 0.30 9107 1.00
p_predicted_default[591] 0.20 0.31 7142 1.00
p_predicted_default[592] 0.19 0.30 9279 1.00
p_predicted_default[593] 0.20 0.31 7018 1.00
p_predicted_default[594] 0.19 0.30 9183 1.00
p_predicted_default[595] 0.14 0.25 5875 1.00
p_predicted_default[596] 0.14 0.24 5574 1.00
p_predicted_default[597] 0.35 0.44 1257 1.00
p_predicted_default[598] 0.39 0.45 4584 1.00
p_predicted_default[599] 0.38 0.44 3889 1.00
p_predicted_default[600] 0.37 0.43 3308 1.00
p_predicted_default[601] 0.30 0.36 3336 1.00
p_predicted_default[602] 0.31 0.38 3110 1.00
p_predicted_default[603] 0.15 0.19 4961 1.00
p_predicted_default[604] 0.15 0.19 4132 1.00
p_predicted_default[605] 0.13 0.18 2284 1.00
p_predicted_default[606] 0.11 0.17 1389 1.00
p_predicted_default[607] 0.68 0.85 7444 1.00
p_predicted_default[608] 0.68 0.85 7444 1.00
p_predicted_default[609] 0.64 0.81 6205 1.00
p_predicted_default[610] 0.64 0.81 6205 1.00
p_predicted_default[611] 0.64 0.81 6206 1.00
p_predicted_default[612] 0.64 0.81 6206 1.00
p_predicted_default[613] 0.67 0.84 6644 1.00
p_predicted_default[614] 0.67 0.84 6644 1.00
p_predicted_default[615] 0.77 0.91 5734 1.00
p_predicted_default[616] 0.77 0.91 5734 1.00
p_predicted_default[617] 0.22 0.27 2647 1.00
p_predicted_default[618] 0.22 0.27 2664 1.00
p_predicted_default[619] 0.21 0.26 2072 1.00
p_predicted_default[620] 0.03 0.44 10622 1.00
p_predicted_default[621] 0.03 0.43 10736 1.00
p_predicted_default[622] 0.12 0.23 10061 1.00
p_predicted_default[623] 0.11 0.22 9979 1.00
p_predicted_default[624] 0.11 0.20 10031 1.00
p_predicted_default[625] 0.08 0.15 9652 1.00
p_predicted_default[626] 0.05 0.11 8843 1.00
p_predicted_default[627] 0.06 0.11 8298 1.00
p_predicted_default[628] 0.06 0.11 8216 1.00
p_predicted_default[629] 0.04 0.09 8433 1.00
p_predicted_default[630] 0.36 0.48 1851 1.00
p_predicted_default[631] 0.41 0.49 6748 1.00
p_predicted_default[632] 0.34 0.43 3421 1.00
p_predicted_default[633] 0.34 0.42 3761 1.00
p_predicted_default[634] 0.34 0.42 3941 1.00
p_predicted_default[635] 0.16 0.22 2775 1.00
p_predicted_default[636] 0.26 0.39 6548 1.00
p_predicted_default[637] 0.27 0.43 6580 1.00
p_predicted_default[638] 0.31 0.43 6525 1.00
p_predicted_default[639] 0.32 0.48 8445 1.00
p_predicted_default[640] 0.30 0.42 6039 1.00
p_predicted_default[641] 0.32 0.47 8049 1.00
p_predicted_default[642] 0.31 0.42 6099 1.00
p_predicted_default[643] 0.32 0.47 8112 1.00
p_predicted_default[644] 0.23 0.33 5494 1.00
p_predicted_default[645] 0.24 0.39 6005 1.00
p_predicted_default[646] 0.23 0.34 6352 1.00
p_predicted_default[647] 0.24 0.39 6744 1.00
p_predicted_default[648] 0.23 0.34 6117 1.00
p_predicted_default[649] 0.24 0.39 6549 1.00
p_predicted_default[650] 0.34 0.51 7532 1.00
p_predicted_default[651] 0.34 0.51 7532 1.00
p_predicted_default[652] 0.33 0.50 7807 1.00
p_predicted_default[653] 0.33 0.50 7807 1.00
p_predicted_default[654] 0.25 0.35 8460 1.00
p_predicted_default[655] 0.25 0.35 8460 1.00
p_predicted_default[656] 0.09 0.17 9505 1.00
p_predicted_default[657] 0.09 0.17 9505 1.00
p_predicted_default[658] 0.08 0.16 9397 1.00
p_predicted_default[659] 0.08 0.16 9397 1.00
p_predicted_default[660] 0.04 0.07 7882 1.00
p_predicted_default[661] 0.04 0.07 7882 1.00
p_predicted_default[662] 0.04 0.07 8306 1.00
p_predicted_default[663] 0.04 0.07 8306 1.00
p_predicted_default[664] 0.04 0.07 8513 1.00
p_predicted_default[665] 0.04 0.07 8513 1.00
p_predicted_default[666] 0.04 0.07 8659 1.00
p_predicted_default[667] 0.04 0.07 8659 1.00
p_predicted_default[668] 0.12 0.26 10404 1.00
p_predicted_default[669] 0.12 0.26 10404 1.00
p_predicted_default[670] 0.12 0.25 10453 1.00
p_predicted_default[671] 0.08 0.17 10745 1.00
p_predicted_default[672] 0.08 0.16 10793 1.00
p_predicted_default[673] 0.08 0.16 10771 1.00
p_predicted_default[674] 0.08 0.15 10704 1.00
p_predicted_default[675] 0.07 0.16 10815 1.00
p_predicted_default[676] 0.05 0.13 11648 1.00
p_predicted_default[677] 0.05 0.13 11640 1.00
p_predicted_default[678] 0.05 0.13 11637 1.00
p_predicted_default[679] 0.05 0.13 11636 1.00
p_predicted_default[680] 0.05 0.12 11633 1.00
p_predicted_default[681] 0.05 0.12 11630 1.00
p_predicted_default[682] 0.05 0.12 11607 1.00
p_predicted_default[683] 0.05 0.12 11586 1.00
p_predicted_default[684] 0.05 0.12 11580 1.00
p_predicted_default[685] 0.05 0.12 11565 1.00
p_predicted_default[686] 0.05 0.12 11551 1.00
p_predicted_default[687] 0.05 0.12 11543 1.00
p_predicted_default[688] 0.05 0.12 11529 1.00
p_predicted_default[689] 0.05 0.09 5456 1.00
p_predicted_default[690] 0.03 0.07 6860 1.00
p_predicted_default[691] 0.05 0.09 5456 1.00
p_predicted_default[692] 0.09 0.14 5497 1.00
p_predicted_default[693] 0.07 0.11 6645 1.00
p_predicted_default[694] 0.09 0.14 5497 1.00
p_predicted_default[695] 0.09 0.14 5373 1.00
p_predicted_default[696] 0.07 0.11 6581 1.00
p_predicted_default[697] 0.09 0.14 5373 1.00
p_predicted_default[698] 0.09 0.15 5208 1.00
p_predicted_default[699] 0.07 0.12 6404 1.00
p_predicted_default[700] 0.09 0.15 5208 1.00
p_predicted_default[701] 0.07 0.12 5235 1.00
p_predicted_default[702] 0.05 0.09 6511 1.00
p_predicted_default[703] 0.07 0.12 5235 1.00
p_predicted_default[704] 0.07 0.12 5233 1.00
p_predicted_default[705] 0.05 0.09 6509 1.00
p_predicted_default[706] 0.07 0.12 5233 1.00
p_predicted_default[707] 0.07 0.12 5178 1.00
p_predicted_default[708] 0.05 0.09 6459 1.00
p_predicted_default[709] 0.07 0.12 5178 1.00
p_predicted_default[710] 0.51 0.58 8318 1.00
p_predicted_default[711] 0.43 0.51 4023 1.00
p_predicted_default[712] 0.25 0.33 1538 1.00
p_predicted_default[713] 0.28 0.35 7576 1.00
p_predicted_default[714] 0.28 0.35 7563 1.00
p_predicted_default[715] 0.27 0.34 7991 1.00
p_predicted_default[716] 0.27 0.33 7390 1.00
p_predicted_default[717] 0.22 0.27 4538 1.00
p_predicted_default[718] 0.21 0.26 5606 1.00
p_predicted_default[719] 0.14 0.18 7688 1.00
p_predicted_default[720] 0.11 0.15 3567 1.00
p_predicted_default[721] 0.00 0.02 9861 1.00
p_predicted_default[722] 0.00 0.02 9854 1.00
p_predicted_default[723] 0.00 0.02 9918 1.00
p_predicted_default[724] 0.00 0.02 9918 1.00
p_predicted_default[725] 0.00 0.02 9930 1.00
p_predicted_default[726] 0.00 0.02 9929 1.00
p_predicted_default[727] 0.00 0.02 9935 1.00
p_predicted_default[728] 0.00 0.02 9933 1.00
p_predicted_default[729] 0.00 0.02 10083 1.00
p_predicted_default[730] 0.40 0.57 8339 1.00
p_predicted_default[731] 0.39 0.56 8085 1.00
p_predicted_default[732] 0.31 0.48 7390 1.00
p_predicted_default[733] 0.61 0.76 9868 1.00
p_predicted_default[734] 0.61 0.76 9858 1.00
p_predicted_default[735] 0.59 0.74 9694 1.00
p_predicted_default[736] 0.51 0.67 9747 1.00
p_predicted_default[737] 0.51 0.67 9840 1.00
p_predicted_default[738] 0.50 0.67 10330 1.00
p_predicted_default[739] 0.50 0.68 10488 1.00
p_predicted_default[740] 0.50 0.68 10591 1.00
p_predicted_default[741] 0.10 0.20 8994 1.00
p_predicted_default[742] 0.10 0.20 8994 1.00
p_predicted_default[743] 0.00 0.01 9367 1.00
p_predicted_default[744] 0.00 0.01 9370 1.00
p_predicted_default[745] 0.00 0.01 9317 1.00
p_predicted_default[746] 0.00 0.01 9363 1.00
p_predicted_default[747] 0.00 0.01 9349 1.00
p_predicted_default[748] 0.00 0.01 9579 1.00
p_predicted_default[749] 0.00 0.01 9587 1.00
p_predicted_default[750] 0.00 0.01 9578 1.00
p_predicted_default[751] 0.00 0.01 9724 1.00
p_predicted_default[752] 0.02 0.19 10547 1.00
p_predicted_default[753] 0.02 0.18 10633 1.00
p_predicted_default[754] 0.02 0.18 10635 1.00
p_predicted_default[755] 0.02 0.18 10631 1.00
p_predicted_default[756] 0.01 0.18 10629 1.00
p_predicted_default[757] 0.01 0.19 10339 1.00
p_predicted_default[758] 0.00 0.01 9401 1.00
p_predicted_default[759] 0.00 0.01 9402 1.00
p_predicted_default[760] 0.00 0.01 9381 1.00
p_predicted_default[761] 0.00 0.01 9353 1.00
p_predicted_default[762] 0.00 0.01 9284 1.00
p_predicted_default[763] 0.00 0.01 9315 1.00
p_predicted_default[764] 0.00 0.01 9306 1.00
p_predicted_default[765] 0.00 0.01 9304 1.00
p_predicted_default[766] 0.00 0.01 9297 1.00
p_predicted_default[767] 0.00 0.01 9280 1.00
p_predicted_default[768] 0.00 0.01 9274 1.00
p_predicted_default[769] 0.00 0.01 9348 1.00
p_predicted_default[770] 0.00 0.01 9402 1.00
p_predicted_default[771] 0.00 0.01 9382 1.00
p_predicted_default[772] 0.00 0.01 9268 1.00
p_predicted_default[773] 0.00 0.01 9284 1.00
p_predicted_default[774] 0.00 0.01 9313 1.00
p_predicted_default[775] 0.00 0.01 9302 1.00
p_predicted_default[776] 0.00 0.01 9290 1.00
p_predicted_default[777] 0.00 0.01 9289 1.00
p_predicted_default[778] 0.00 0.01 9370 1.00
p_predicted_default[779] 0.00 0.01 9370 1.00
p_predicted_default[780] 0.00 0.01 9346 1.00
p_predicted_default[781] 0.00 0.01 9401 1.00
p_predicted_default[782] 0.00 0.01 9396 1.00
p_predicted_default[783] 0.00 0.01 9310 1.00
p_predicted_default[784] 0.00 0.01 9287 1.00
p_predicted_default[785] 0.00 0.01 9278 1.00
p_predicted_default[786] 0.00 0.01 9280 1.00
p_predicted_default[787] 0.00 0.01 9361 1.00
p_predicted_default[788] 0.00 0.01 9348 1.00
p_predicted_default[789] 0.00 0.01 9322 1.00
p_predicted_default[790] 0.00 0.01 9320 1.00
p_predicted_default[791] 0.38 0.44 4121 1.00
p_predicted_default[792] 0.37 0.44 3524 1.00
p_predicted_default[793] 0.37 0.43 3396 1.00
p_predicted_default[794] 0.38 0.44 3875 1.00
p_predicted_default[795] 0.33 0.39 2796 1.00
p_predicted_default[796] 0.32 0.38 3037 1.00
p_predicted_default[797] 0.32 0.38 3032 1.00
p_predicted_default[798] 0.31 0.38 3092 1.00
p_predicted_default[799] 0.00 0.04 9340 1.00
p_predicted_default[800] 0.00 0.03 9353 1.00
p_predicted_default[801] 0.00 0.04 9356 1.00
p_predicted_default[802] 0.00 0.03 9379 1.00
p_predicted_default[803] 0.00 0.04 9399 1.00
p_predicted_default[804] 0.00 0.04 9404 1.00
p_predicted_default[805] 0.00 0.02 9903 1.00
p_predicted_default[806] 0.00 0.02 9892 1.00
p_predicted_default[807] 0.03 0.07 7124 1.00
p_predicted_default[808] 0.03 0.05 8280 1.00
p_predicted_default[809] 0.03 0.07 7114 1.00
p_predicted_default[810] 0.03 0.05 8277 1.00
p_predicted_default[811] 0.05 0.09 10135 1.00
p_predicted_default[812] 0.05 0.09 10119 1.00
p_predicted_default[813] 0.06 0.12 7333 1.00
p_predicted_default[814] 0.05 0.09 10119 1.00
p_predicted_default[815] 0.05 0.09 10121 1.00
p_predicted_default[816] 0.05 0.09 10135 1.00
p_predicted_default[817] 0.05 0.09 10143 1.00
p_predicted_default[818] 0.09 0.18 9968 1.00
p_predicted_default[819] 0.09 0.17 9638 1.00
p_predicted_default[820] 0.09 0.17 9664 1.00
p_predicted_default[821] 0.08 0.16 9829 1.00
p_predicted_default[822] 0.07 0.12 10385 1.00
p_predicted_default[823] 0.08 0.15 9722 1.00
p_predicted_default[824] 0.07 0.13 10124 1.00
p_predicted_default[825] 0.06 0.12 10513 1.00
p_predicted_default[826] 0.07 0.12 10415 1.00
p_predicted_default[827] 0.06 0.11 10601 1.00
p_predicted_default[828] 0.00 0.02 9751 1.00
p_predicted_default[829] 0.00 0.02 9811 1.00
p_predicted_default[830] 0.08 0.18 8423 1.00
p_predicted_default[831] 0.10 0.20 9497 1.00
p_predicted_default[832] 0.07 0.14 8586 1.00
p_predicted_default[833] 0.06 0.14 8760 1.00
p_predicted_default[834] 0.06 0.14 8799 1.00
p_predicted_default[835] 0.06 0.15 9279 1.00
p_predicted_default[836] 0.19 0.27 2072 1.00
p_predicted_default[837] 0.19 0.29 2265 1.00
p_predicted_default[838] 0.22 0.28 4812 1.00
p_predicted_default[839] 0.23 0.31 9627 1.00
p_predicted_default[840] 0.22 0.29 4607 1.00
p_predicted_default[841] 0.23 0.31 9401 1.00
p_predicted_default[842] 0.22 0.28 4752 1.00
p_predicted_default[843] 0.23 0.31 9567 1.00
p_predicted_default[844] 0.13 0.18 5184 1.00
p_predicted_default[845] 0.14 0.20 8198 1.00
p_predicted_default[846] 0.13 0.18 5203 1.00
p_predicted_default[847] 0.14 0.20 8341 1.00
p_predicted_default[848] 0.12 0.17 4665 1.00
p_predicted_default[849] 0.13 0.19 6677 1.00
p_predicted_default[850] 0.10 0.13 5668 1.00
p_predicted_default[851] 0.10 0.15 10367 1.00
p_predicted_default[852] 0.07 0.10 4686 1.00
p_predicted_default[853] 0.08 0.12 5450 1.00
p_predicted_default[854] 0.07 0.10 3430 1.00
p_predicted_default[855] 0.07 0.11 3684 1.00
p_predicted_default[856] 0.05 0.08 2187 1.00
p_predicted_default[857] 0.05 0.09 2324 1.00
p_predicted_default[858] 0.21 0.27 5737 1.00
p_predicted_default[859] 0.20 0.26 6608 1.00
p_predicted_default[860] 0.19 0.25 6985 1.00
p_predicted_default[861] 0.19 0.24 6903 1.00
p_predicted_default[862] 0.18 0.23 5802 1.00
p_predicted_default[863] 0.17 0.23 4750 1.00
p_predicted_default[864] 0.14 0.18 5401 1.00
p_predicted_default[865] 0.13 0.17 6423 1.00
p_predicted_default[866] 0.12 0.16 5737 1.00
p_predicted_default[867] 0.12 0.16 6326 1.00
p_predicted_default[868] 0.13 0.17 6416 1.00
p_predicted_default[869] 0.13 0.17 6392 1.00
p_predicted_default[870] 0.00 0.04 10666 1.00
p_predicted_default[871] 0.00 0.04 10451 1.00
p_predicted_default[872] 0.00 0.04 10516 1.00
p_predicted_default[873] 0.00 0.04 10544 1.00
p_predicted_default[874] 0.00 0.04 10652 1.00
p_predicted_default[875] 0.00 0.04 10664 1.00
p_predicted_default[876] 0.34 0.46 7073 1.00
p_predicted_default[877] 0.25 0.35 6012 1.00
p_predicted_default[878] 0.19 0.28 4375 1.00
p_predicted_default[879] 0.18 0.27 3702 1.00
p_predicted_default[880] 0.06 0.12 5760 1.00
p_predicted_default[881] 0.04 0.09 7684 1.00
p_predicted_default[882] 0.03 0.07 7533 1.00
p_predicted_default[883] 0.03 0.06 7745 1.00
p_predicted_default[884] 0.03 0.06 7872 1.00
p_predicted_default[885] 0.00 0.02 9591 1.00
p_predicted_default[886] 0.00 0.02 9752 1.00
p_predicted_default[887] 0.00 0.02 9749 1.00
p_predicted_default[888] 0.00 0.02 9739 1.00
p_predicted_default[889] 0.00 0.02 9730 1.00
p_predicted_default[890] 0.00 0.02 9882 1.00
p_predicted_default[891] 0.00 0.02 10167 1.00
p_predicted_default[892] 0.00 0.02 10165 1.00
p_predicted_default[893] 0.00 0.02 10168 1.00
p_predicted_default[894] 0.00 0.02 10171 1.00
p_predicted_default[895] 0.00 0.02 10128 1.00
p_predicted_default[896] 0.00 0.02 10122 1.00
p_predicted_default[897] 0.00 0.01 9578 1.00
p_predicted_default[898] 0.00 0.01 9566 1.00
p_predicted_default[899] 0.00 0.01 9703 1.00
p_predicted_default[900] 0.00 0.01 9767 1.00
p_predicted_default[901] 0.25 0.33 1537 1.00
p_predicted_default[902] 0.28 0.35 7806 1.00
p_predicted_default[903] 0.14 0.18 7687 1.00
p_predicted_default[904] 0.13 0.17 9222 1.00
p_predicted_default[905] 0.12 0.16 6996 1.00
p_predicted_default[906] 0.10 0.15 2976 1.00
p_predicted_default[907] 0.31 0.38 6813 1.00
p_predicted_default[908] 0.28 0.35 5863 1.00
p_predicted_default[909] 0.23 0.29 4904 1.00
p_predicted_default[910] 0.20 0.26 5942 1.00
p_predicted_default[911] 0.20 0.26 5862 1.00
p_predicted_default[912] 0.02 0.06 5351 1.00
p_predicted_default[913] 0.02 0.04 6308 1.00
p_predicted_default[914] 0.02 0.06 5351 1.00
p_predicted_default[915] 0.03 0.06 4759 1.00
p_predicted_default[916] 0.02 0.05 5664 1.00
p_predicted_default[917] 0.03 0.06 4759 1.00
p_predicted_default[918] 0.02 0.05 4782 1.00
p_predicted_default[919] 0.01 0.04 5557 1.00
p_predicted_default[920] 0.02 0.05 4782 1.00
p_predicted_default[921] 0.02 0.05 4689 1.00
p_predicted_default[922] 0.01 0.04 5484 1.00
p_predicted_default[923] 0.02 0.05 4689 1.00
p_predicted_default[924] 0.41 0.53 1772 1.00
p_predicted_default[925] 0.43 0.52 7042 1.00
p_predicted_default[926] 0.43 0.52 6964 1.00
p_predicted_default[927] 0.44 0.53 7126 1.00
p_predicted_default[928] 0.13 0.21 2275 1.00
p_predicted_default[929] 0.16 0.22 3658 1.00
p_predicted_default[930] 0.09 0.13 4093 1.00
p_predicted_default[931] 0.06 0.09 2634 1.00
p_predicted_default[932] 0.00 0.01 9394 1.00
p_predicted_default[933] 0.00 0.01 9399 1.00
p_predicted_default[934] 0.00 0.02 8922 1.00
p_predicted_default[935] 0.00 0.01 9399 1.00
p_predicted_default[936] 0.00 0.01 9387 1.00
p_predicted_default[937] 0.00 0.01 9340 1.00
p_predicted_default[938] 0.00 0.01 9337 1.00
p_predicted_default[939] 0.00 0.01 9283 1.00
p_predicted_default[940] 0.00 0.01 9303 1.00
p_predicted_default[941] 0.00 0.01 9295 1.00
p_predicted_default[942] 0.00 0.01 9273 1.00
p_predicted_default[943] 0.00 0.01 9268 1.00
p_predicted_default[944] 0.55 0.63 6816 1.00
p_predicted_default[945] 0.53 0.62 6607 1.00
p_predicted_default[946] 0.47 0.56 4147 1.00
p_predicted_default[947] 0.25 0.31 6742 1.00
p_predicted_default[948] 0.25 0.31 6757 1.00
p_predicted_default[949] 0.24 0.30 5999 1.00
p_predicted_default[950] 0.27 0.38 9408 1.00
p_predicted_default[951] 0.27 0.38 9408 1.00
p_predicted_default[952] 0.27 0.38 9408 1.00
p_predicted_default[953] 0.22 0.30 8537 1.00
p_predicted_default[954] 0.22 0.30 8537 1.00
p_predicted_default[955] 0.22 0.30 8537 1.00
p_predicted_default[956] 0.21 0.29 8690 1.00
p_predicted_default[957] 0.21 0.29 8690 1.00
p_predicted_default[958] 0.21 0.29 8690 1.00
p_predicted_default[959] 0.21 0.29 10154 1.00
p_predicted_default[960] 0.21 0.29 10154 1.00
p_predicted_default[961] 0.21 0.29 10154 1.00
p_predicted_default[962] 0.22 0.30 8083 1.00
p_predicted_default[963] 0.22 0.30 8083 1.00
p_predicted_default[964] 0.22 0.30 8083 1.00
p_predicted_default[965] 0.21 0.29 8281 1.00
p_predicted_default[966] 0.21 0.29 8281 1.00
p_predicted_default[967] 0.21 0.29 8281 1.00
p_predicted_default[968] 0.21 0.29 8385 1.00
p_predicted_default[969] 0.21 0.29 8385 1.00
p_predicted_default[970] 0.21 0.29 8385 1.00
p_predicted_default[971] 0.20 0.29 8792 1.00
p_predicted_default[972] 0.20 0.29 8792 1.00
p_predicted_default[973] 0.20 0.29 8792 1.00
p_predicted_default[974] 0.20 0.28 9422 1.00
p_predicted_default[975] 0.20 0.28 9422 1.00
p_predicted_default[976] 0.20 0.28 9422 1.00
p_predicted_default[977] 0.00 0.02 8915 1.00
p_predicted_default[978] 0.00 0.01 9394 1.00
p_predicted_default[979] 0.00 0.01 9394 1.00
p_predicted_default[980] 0.00 0.01 9391 1.00
p_predicted_default[981] 0.00 0.02 8922 1.00
p_predicted_default[982] 0.00 0.01 9392 1.00
p_predicted_default[983] 0.00 0.01 9365 1.00
p_predicted_default[984] 0.00 0.01 9340 1.00
p_predicted_default[985] 0.00 0.01 9268 1.00
p_predicted_default[986] 0.00 0.01 9270 1.00
p_predicted_default[987] 0.00 0.01 9283 1.00
p_predicted_default[988] 0.00 0.01 9303 1.00
p_predicted_default[989] 0.00 0.01 9291 1.00
p_predicted_default[990] 0.00 0.01 9273 1.00
p_predicted_default[991] 0.00 0.01 9268 1.00
p_predicted_default[992] 0.00 0.01 9326 1.00
p_predicted_default[993] 0.00 0.02 9748 1.00
p_predicted_default[994] 0.00 0.02 10268 1.00
p_predicted_default[995] 0.00 0.02 10139 1.00
p_predicted_default[996] 0.99 1.00 10582 1.00
p_predicted_default[997] 0.99 1.00 9718 1.00
p_predicted_default[998] 1.00 1.00 10035 1.00
p_predicted_default[999] 0.00 0.01 8664 1.00
p_predicted_default[1000] 0.00 0.01 8625 1.00
p_predicted_default[1001] 0.00 0.01 9359 1.00
p_predicted_default[1002] 0.00 0.01 9346 1.00
p_predicted_default[1003] 0.00 0.01 9325 1.00
p_predicted_default[1004] 0.19 0.34 7140 1.00
p_predicted_default[1005] 0.21 0.37 5897 1.00
p_predicted_default[1006] 0.21 0.37 5896 1.00
p_predicted_default[1007] 0.17 0.31 5937 1.00
p_predicted_default[1008] 0.18 0.33 6148 1.00
p_predicted_default[1009] 0.37 0.57 5807 1.00
p_predicted_default[1010] 0.41 0.60 4785 1.00
p_predicted_default[1011] 0.46 0.68 4206 1.00
p_predicted_default[1012] 0.09 0.17 9584 1.00
p_predicted_default[1013] 0.06 0.12 10425 1.00
p_predicted_default[1014] 0.06 0.11 10604 1.00
p_predicted_default[1015] 0.09 0.19 9926 1.00
p_predicted_default[1016] 0.08 0.17 9715 1.00
p_predicted_default[1017] 0.06 0.12 10571 1.00
p_predicted_default[1018] 0.06 0.12 10605 1.00
p_predicted_default[1019] 0.06 0.11 10610 1.00
p_predicted_default[1020] 0.05 0.12 10284 1.00
p_predicted_default[1021] 0.05 0.11 10240 1.00
p_predicted_default[1022] 0.05 0.11 10089 1.00
p_predicted_default[1023] 0.05 0.11 10020 1.00
p_predicted_default[1024] 0.05 0.12 10029 1.00
p_predicted_default[1025] 0.05 0.12 10128 1.00
p_predicted_default[1026] 0.05 0.12 10202 1.00
p_predicted_default[1027] 0.09 0.19 9959 1.00
p_predicted_default[1028] 0.09 0.17 9638 1.00
p_predicted_default[1029] 0.08 0.15 9766 1.00
p_predicted_default[1030] 0.06 0.12 10608 1.00
p_predicted_default[1031] 0.06 0.11 10610 1.00
p_predicted_default[1032] 0.05 0.11 10280 1.00
p_predicted_default[1033] 0.05 0.11 10204 1.00
p_predicted_default[1034] 0.05 0.11 10144 1.00
p_predicted_default[1035] 0.05 0.11 10023 1.00
p_predicted_default[1036] 0.05 0.11 10005 1.00
p_predicted_default[1037] 0.05 0.12 10095 1.00
p_predicted_default[1038] 0.05 0.12 7798 1.00
p_predicted_default[1039] 0.06 0.11 7912 1.00
p_predicted_default[1040] 0.05 0.11 7732 1.00
p_predicted_default[1041] 0.06 0.11 7815 1.00
p_predicted_default[1042] 0.05 0.11 7626 1.00
p_predicted_default[1043] 0.05 0.11 7694 1.00
p_predicted_default[1044] 0.05 0.11 7633 1.00
p_predicted_default[1045] 0.05 0.11 7744 1.00
p_predicted_default[1046] 0.05 0.11 7609 1.00
p_predicted_default[1047] 0.05 0.11 7725 1.00
p_predicted_default[1048] 0.05 0.11 7602 1.00
p_predicted_default[1049] 0.05 0.11 7654 1.00
p_predicted_default[1050] 0.04 0.09 8605 1.00
p_predicted_default[1051] 0.04 0.09 8300 1.00
p_predicted_default[1052] 0.13 0.26 10740 1.00
p_predicted_default[1053] 0.13 0.26 10740 1.00
p_predicted_default[1054] 0.12 0.23 10881 1.00
p_predicted_default[1055] 0.12 0.23 10881 1.00
p_predicted_default[1056] 0.12 0.22 10716 1.00
p_predicted_default[1057] 0.12 0.22 10716 1.00
p_predicted_default[1058] 0.12 0.22 10662 1.00
p_predicted_default[1059] 0.12 0.22 10662 1.00
p_predicted_default[1060] 0.11 0.23 11110 1.00
p_predicted_default[1061] 0.11 0.23 11110 1.00
p_predicted_default[1062] 0.13 0.25 10792 1.00
p_predicted_default[1063] 0.12 0.24 10894 1.00
p_predicted_default[1064] 0.12 0.23 10889 1.00
p_predicted_default[1065] 0.12 0.22 10724 1.00
p_predicted_default[1066] 0.12 0.22 10692 1.00
p_predicted_default[1067] 0.12 0.22 10789 1.00
p_predicted_default[1068] 0.12 0.22 10700 1.00
p_predicted_default[1069] 0.11 0.23 11156 1.00
p_predicted_default[1070] 0.11 0.23 11102 1.00
p_predicted_default[1071] 0.00 0.06 10812 1.00
p_predicted_default[1072] 0.00 0.06 10781 1.00
p_predicted_default[1073] 0.00 0.06 10766 1.00
p_predicted_default[1074] 0.16 0.23 7570 1.00
p_predicted_default[1075] 0.16 0.23 7570 1.00
p_predicted_default[1076] 0.20 0.27 7985 1.00
p_predicted_default[1077] 0.20 0.27 7985 1.00
p_predicted_default[1078] 0.16 0.23 8213 1.00
p_predicted_default[1079] 0.16 0.23 8213 1.00
p_predicted_default[1080] 0.16 0.23 8419 1.00
p_predicted_default[1081] 0.16 0.23 8419 1.00
p_predicted_default[1082] 0.16 0.23 8891 1.00
p_predicted_default[1083] 0.16 0.23 8891 1.00
p_predicted_default[1084] 0.17 0.25 1806 1.00
p_predicted_default[1085] 0.20 0.26 7620 1.00
p_predicted_default[1086] 0.15 0.20 5555 1.00
p_predicted_default[1087] 0.14 0.19 6764 1.00
p_predicted_default[1088] 0.13 0.17 9212 1.00
p_predicted_default[1089] 0.11 0.15 5153 1.00
p_predicted_default[1090] 0.11 0.15 4424 1.00
p_predicted_default[1091] 0.22 0.32 9504 1.00
p_predicted_default[1092] 0.22 0.32 9390 1.00
p_predicted_default[1093] 0.22 0.32 9572 1.00
p_predicted_default[1094] 0.22 0.32 9458 1.00
p_predicted_default[1095] 0.21 0.30 8497 1.00
p_predicted_default[1096] 0.22 0.31 9340 1.00
p_predicted_default[1097] 0.21 0.30 8494 1.00
p_predicted_default[1098] 0.22 0.31 9203 1.00
p_predicted_default[1099] 0.21 0.30 8559 1.00
p_predicted_default[1100] 0.21 0.30 8532 1.00
p_predicted_default[1101] 0.21 0.30 8368 1.00
p_predicted_default[1102] 0.20 0.29 8299 1.00
p_predicted_default[1103] 0.15 0.23 8823 1.00
p_predicted_default[1104] 0.15 0.22 8975 1.00
p_predicted_default[1105] 0.14 0.22 9200 1.00
p_predicted_default[1106] 0.48 0.58 5330 1.00
p_predicted_default[1107] 0.48 0.58 5407 1.00
p_predicted_default[1108] 0.46 0.56 5984 1.00
p_predicted_default[1109] 0.41 0.52 3013 1.00
p_predicted_default[1110] 0.25 0.35 3048 1.00
p_predicted_default[1111] 0.28 0.37 6808 1.00
p_predicted_default[1112] 0.27 0.35 6353 1.00
p_predicted_default[1113] 0.27 0.35 6283 1.00
p_predicted_default[1114] 0.21 0.28 6847 1.00
p_predicted_default[1115] 0.23 0.31 4365 1.00
p_predicted_default[1116] 0.25 0.31 6239 1.00
p_predicted_default[1117] 0.24 0.30 6958 1.00
p_predicted_default[1118] 0.22 0.28 6292 1.00
p_predicted_default[1119] 0.17 0.22 5341 1.00
p_predicted_default[1120] 0.17 0.21 5648 1.00
p_predicted_default[1121] 0.16 0.20 6016 1.00
p_predicted_default[1122] 0.16 0.20 5818 1.00
p_predicted_default[1123] 0.16 0.21 6062 1.00
p_predicted_default[1124] 0.16 0.20 5969 1.00
p_predicted_default[1125] 0.17 0.21 6000 1.00
p_predicted_default[1126] 0.15 0.20 5396 1.00
p_predicted_default[1127] 0.35 0.53 3361 1.00
p_predicted_default[1128] 0.28 0.38 9464 1.00
p_predicted_default[1129] 0.27 0.37 9151 1.00
p_predicted_default[1130] 0.21 0.29 8940 1.00
p_predicted_default[1131] 0.21 0.29 9886 1.00
p_predicted_default[1132] 0.09 0.27 8624 1.00
p_predicted_default[1133] 0.00 0.05 10169 1.00
p_predicted_default[1134] 0.00 0.05 10477 1.00
p_predicted_default[1135] 0.02 0.08 5080 1.00
p_predicted_default[1136] 0.01 0.05 4925 1.00
p_predicted_default[1137] 0.02 0.08 5080 1.00
p_predicted_default[1138] 0.01 0.07 4867 1.00
p_predicted_default[1139] 0.01 0.04 4706 1.00
p_predicted_default[1140] 0.01 0.07 4867 1.00
p_predicted_default[1141] 0.02 0.09 4933 1.00
p_predicted_default[1142] 0.01 0.05 4770 1.00
p_predicted_default[1143] 0.02 0.09 4933 1.00
p_predicted_default[1144] 0.01 0.07 4847 1.00
p_predicted_default[1145] 0.01 0.04 4685 1.00
p_predicted_default[1146] 0.01 0.07 4847 1.00
p_predicted_default[1147] 0.09 0.14 5969 1.00
p_predicted_default[1148] 0.06 0.11 6845 1.00
p_predicted_default[1149] 0.09 0.14 5969 1.00
p_predicted_default[1150] 0.07 0.12 5178 1.00
p_predicted_default[1151] 0.05 0.09 6460 1.00
p_predicted_default[1152] 0.07 0.12 5178 1.00
p_predicted_default[1153] 0.07 0.11 5243 1.00
p_predicted_default[1154] 0.05 0.09 6557 1.00
p_predicted_default[1155] 0.07 0.11 5243 1.00
p_predicted_default[1156] 0.12 0.19 5209 1.00
p_predicted_default[1157] 0.08 0.14 6069 1.00
p_predicted_default[1158] 0.12 0.19 5209 1.00
p_predicted_default[1159] 0.04 0.10 6357 1.00
p_predicted_default[1160] 0.04 0.08 6098 1.00
p_predicted_default[1161] 0.04 0.08 6148 1.00
p_predicted_default[1162] 0.03 0.07 6366 1.00
p_predicted_default[1163] 0.03 0.07 6409 1.00
p_predicted_default[1164] 0.03 0.07 6649 1.00
p_predicted_default[1165] 0.07 0.13 6689 1.00
p_predicted_default[1166] 0.05 0.11 8833 1.00
p_predicted_default[1167] 0.07 0.13 6689 1.00
p_predicted_default[1168] 0.05 0.10 7218 1.00
p_predicted_default[1169] 0.04 0.08 8814 1.00
p_predicted_default[1170] 0.05 0.10 7218 1.00
p_predicted_default[1171] 0.06 0.11 6402 1.00
p_predicted_default[1172] 0.04 0.09 7614 1.00
p_predicted_default[1173] 0.06 0.11 6402 1.00
p_predicted_default[1174] 0.90 0.98 9851 1.00
p_predicted_default[1175] 0.89 0.97 9852 1.00
p_predicted_default[1176] 0.88 0.97 9817 1.00
p_predicted_default[1177] 0.88 0.97 9835 1.00
p_predicted_default[1178] 0.88 0.97 10191 1.00
p_predicted_default[1179] 0.10 0.18 2615 1.00
p_predicted_default[1180] 0.13 0.19 7210 1.00
p_predicted_default[1181] 0.07 0.10 10255 1.00
p_predicted_default[1182] 0.08 0.13 5584 1.00
p_predicted_default[1183] 0.06 0.09 10786 1.00
p_predicted_default[1184] 0.05 0.08 5371 1.00
p_predicted_default[1185] 0.05 0.08 3439 1.00
p_predicted_default[1186] 0.67 0.87 9391 1.00
p_predicted_default[1187] 0.64 0.86 10141 1.00
p_predicted_default[1188] 0.64 0.86 10157 1.00
p_predicted_default[1189] 0.09 0.17 9635 1.00
p_predicted_default[1190] 0.06 0.12 10599 1.00
p_predicted_default[1191] 0.06 0.11 10595 1.00
p_predicted_default[1192] 0.06 0.11 10559 1.00
p_predicted_default[1193] 0.05 0.11 9986 1.00
p_predicted_default[1194] 0.09 0.17 9627 1.00
p_predicted_default[1195] 0.06 0.12 10596 1.00
p_predicted_default[1196] 0.06 0.12 10610 1.00
p_predicted_default[1197] 0.06 0.11 10595 1.00
p_predicted_default[1198] 0.05 0.11 10119 1.00
p_predicted_default[1199] 0.67 0.80 9646 1.00
p_predicted_default[1200] 0.67 0.80 9645 1.00
p_predicted_default[1201] 0.74 0.87 5100 1.00
p_predicted_default[1202] 0.64 0.78 9645 1.00
p_predicted_default[1203] 0.64 0.78 9508 1.00
p_predicted_default[1204] 0.64 0.78 9498 1.00
p_predicted_default[1205] 0.64 0.77 9490 1.00
p_predicted_default[1206] 0.64 0.78 9533 1.00
p_predicted_default[1207] 0.58 0.74 9109 1.00
p_predicted_default[1208] 0.57 0.73 10052 1.00
p_predicted_default[1209] 0.00 0.02 9970 1.00
p_predicted_default[1210] 0.00 0.02 10373 1.00
p_predicted_default[1211] 0.08 0.18 10525 1.00
p_predicted_default[1212] 0.09 0.18 10484 1.00
p_predicted_default[1213] 0.08 0.17 10599 1.00
p_predicted_default[1214] 0.06 0.12 10989 1.00
p_predicted_default[1215] 0.06 0.12 10808 1.00
p_predicted_default[1216] 0.05 0.12 10319 1.00
p_predicted_default[1217] 0.32 0.44 9278 1.00
p_predicted_default[1218] 0.31 0.44 9013 1.00
p_predicted_default[1219] 0.25 0.35 7916 1.00
p_predicted_default[1220] 0.25 0.35 7855 1.00
p_predicted_default[1221] 0.25 0.35 8416 1.00
p_predicted_default[1222] 0.11 0.24 10758 1.00
p_predicted_default[1223] 0.11 0.24 10396 1.00
p_predicted_default[1224] 0.11 0.23 10143 1.00
p_predicted_default[1225] 0.07 0.17 10354 1.00
p_predicted_default[1226] 0.07 0.17 10374 1.00
p_predicted_default[1227] 0.07 0.17 10352 1.00
p_predicted_default[1228] 0.07 0.17 10373 1.00
p_predicted_default[1229] 0.73 0.86 12246 1.00
p_predicted_default[1230] 0.73 0.86 11144 1.00
p_predicted_default[1231] 0.67 0.82 10128 1.00
p_predicted_default[1232] 0.67 0.82 10012 1.00
p_predicted_default[1233] 0.33 0.39 3757 1.00
p_predicted_default[1234] 0.33 0.39 3757 1.00
p_predicted_default[1235] 0.31 0.36 3390 1.00
p_predicted_default[1236] 0.31 0.36 3390 1.00
p_predicted_default[1237] 0.30 0.35 2783 1.00
p_predicted_default[1238] 0.30 0.35 2783 1.00
p_predicted_default[1239] 0.25 0.30 2581 1.00
p_predicted_default[1240] 0.25 0.30 2581 1.00
p_predicted_default[1241] 0.23 0.28 2846 1.00
p_predicted_default[1242] 0.23 0.28 2846 1.00
p_predicted_default[1243] 0.22 0.27 2444 1.00
p_predicted_default[1244] 0.22 0.27 2444 1.00
p_predicted_default[1245] 0.22 0.27 2573 1.00
p_predicted_default[1246] 0.22 0.27 2573 1.00
p_predicted_default[1247] 0.22 0.26 2375 1.00
p_predicted_default[1248] 0.22 0.26 2375 1.00
p_predicted_default[1249] 0.18 0.32 9613 1.00
p_predicted_default[1250] 0.12 0.22 9418 1.00
p_predicted_default[1251] 0.18 0.32 9613 1.00
p_predicted_default[1252] 0.20 0.33 9481 1.00
p_predicted_default[1253] 0.14 0.22 9051 1.00
p_predicted_default[1254] 0.20 0.33 9481 1.00
p_predicted_default[1255] 0.20 0.33 9485 1.00
p_predicted_default[1256] 0.14 0.22 9153 1.00
p_predicted_default[1257] 0.20 0.33 9485 1.00
p_predicted_default[1258] 0.20 0.33 9470 1.00
p_predicted_default[1259] 0.14 0.23 9284 1.00
p_predicted_default[1260] 0.20 0.33 9470 1.00
p_predicted_default[1261] 0.24 0.38 10060 1.00
p_predicted_default[1262] 0.16 0.27 9917 1.00
p_predicted_default[1263] 0.24 0.38 10060 1.00
p_predicted_default[1264] 0.19 0.34 7777 1.00
p_predicted_default[1265] 0.13 0.23 7554 1.00
p_predicted_default[1266] 0.19 0.34 7777 1.00
p_predicted_default[1267] 0.05 0.11 10827 1.00
p_predicted_default[1268] 0.05 0.11 10831 1.00
p_predicted_default[1269] 0.04 0.11 10767 1.00
p_predicted_default[1270] 0.04 0.11 10697 1.00
p_predicted_default[1271] 0.04 0.11 10452 1.00
p_predicted_default[1272] 0.10 0.21 6730 1.00
p_predicted_default[1273] 0.07 0.18 6012 1.00
p_predicted_default[1274] 0.03 0.07 6138 1.00
p_predicted_default[1275] 0.03 0.07 6514 1.00
p_predicted_default[1276] 0.03 0.07 6642 1.00
p_predicted_default[1277] 0.03 0.07 6643 1.00
p_predicted_default[1278] 0.09 0.14 8116 1.00
p_predicted_default[1279] 0.09 0.14 8116 1.00
p_predicted_default[1280] 0.06 0.10 8003 1.00
p_predicted_default[1281] 0.06 0.10 8003 1.00
p_predicted_default[1282] 0.01 0.02 4944 1.00
p_predicted_default[1283] 0.01 0.02 5600 1.00
p_predicted_default[1284] 0.01 0.02 5570 1.00
p_predicted_default[1285] 0.01 0.02 5564 1.00
p_predicted_default[1286] 0.01 0.02 4812 1.00
p_predicted_default[1287] 0.01 0.02 4808 1.00
p_predicted_default[1288] 0.01 0.02 4811 1.00
p_predicted_default[1289] 0.01 0.02 4830 1.00
p_predicted_default[1290] 0.01 0.02 4961 1.00
p_predicted_default[1291] 0.01 0.02 4962 1.00
p_predicted_default[1292] 0.01 0.02 4971 1.00
p_predicted_default[1293] 0.04 0.08 6152 1.00
p_predicted_default[1294] 0.03 0.07 6286 1.00
p_predicted_default[1295] 0.03 0.07 6326 1.00
p_predicted_default[1296] 0.03 0.07 6667 1.00
p_predicted_default[1297] 0.14 0.24 8304 1.00
p_predicted_default[1298] 0.16 0.26 6815 1.00
p_predicted_default[1299] 0.24 0.37 6325 1.00
p_predicted_default[1300] 0.25 0.39 6389 1.00
p_predicted_default[1301] 0.20 0.33 5325 1.00
p_predicted_default[1302] 0.43 0.61 2968 1.00
p_predicted_default[1303] 0.43 0.61 2968 1.00
p_predicted_default[1304] 0.33 0.45 7512 1.00
p_predicted_default[1305] 0.33 0.45 7512 1.00
p_predicted_default[1306] 0.34 0.45 8015 1.00
p_predicted_default[1307] 0.34 0.45 8015 1.00
p_predicted_default[1308] 0.27 0.37 7974 1.00
p_predicted_default[1309] 0.27 0.37 7974 1.00
p_predicted_default[1310] 0.26 0.35 7571 1.00
p_predicted_default[1311] 0.26 0.35 7571 1.00
p_predicted_default[1312] 0.24 0.33 8416 1.00
p_predicted_default[1313] 0.24 0.33 8416 1.00
p_predicted_default[1314] 0.24 0.33 8595 1.00
p_predicted_default[1315] 0.24 0.33 8595 1.00
p_predicted_default[1316] 0.10 0.21 11416 1.00
p_predicted_default[1317] 0.09 0.18 11563 1.00
p_predicted_default[1318] 0.09 0.18 11556 1.00
p_predicted_default[1319] 0.08 0.17 11428 1.00
p_predicted_default[1320] 0.08 0.17 11597 1.00
p_predicted_default[1321] 0.08 0.17 11519 1.00
p_predicted_default[1322] 0.08 0.17 11276 1.00
p_predicted_default[1323] 0.08 0.17 11202 1.00
p_predicted_default[1324] 0.10 0.21 11416 1.00
p_predicted_default[1325] 0.09 0.18 11563 1.00
p_predicted_default[1326] 0.09 0.18 11556 1.00
p_predicted_default[1327] 0.08 0.17 11428 1.00
p_predicted_default[1328] 0.08 0.17 11595 1.00
p_predicted_default[1329] 0.08 0.17 11519 1.00
p_predicted_default[1330] 0.08 0.17 11207 1.00
p_predicted_default[1331] 0.32 0.43 8511 1.00
p_predicted_default[1332] 0.26 0.36 6861 1.00
p_predicted_default[1333] 0.25 0.35 8784 1.00
p_predicted_default[1334] 0.04 0.10 6359 1.00
p_predicted_default[1335] 0.04 0.10 6375 1.00
p_predicted_default[1336] 0.04 0.08 6128 1.00
p_predicted_default[1337] 0.03 0.08 6224 1.00
p_predicted_default[1338] 0.03 0.08 6240 1.00
p_predicted_default[1339] 0.03 0.08 6260 1.00
p_predicted_default[1340] 0.03 0.07 6264 1.00
p_predicted_default[1341] 0.03 0.07 6264 1.00
p_predicted_default[1342] 0.03 0.07 6303 1.00
p_predicted_default[1343] 0.03 0.07 6509 1.00
p_predicted_intervention[1] 0.00 0.44 8253 1.00
p_predicted_intervention[2] 0.00 0.43 8238 1.00
p_predicted_intervention[3] 0.00 0.43 8224 1.00
p_predicted_intervention[4] 0.00 0.41 8238 1.00
p_predicted_intervention[5] 0.00 0.41 8235 1.00
p_predicted_intervention[6] 0.00 0.41 8232 1.00
p_predicted_intervention[7] 0.00 0.40 8240 1.00
p_predicted_intervention[8] 0.00 0.40 8229 1.00
p_predicted_intervention[9] 0.00 0.00 9165 1.00
p_predicted_intervention[10] 0.00 0.00 9110 1.00
p_predicted_intervention[11] 1.00 1.00 10009 1.00
p_predicted_intervention[12] 1.00 1.00 10009 1.00
p_predicted_intervention[13] 1.00 1.00 10009 1.00
p_predicted_intervention[14] 1.00 1.00 10009 1.00
p_predicted_intervention[15] 1.00 1.00 10009 1.00
p_predicted_intervention[16] 1.00 1.00 10009 1.00
p_predicted_intervention[17] 1.00 1.00 10009 1.00
p_predicted_intervention[18] 1.00 1.00 10009 1.00
p_predicted_intervention[19] 1.00 1.00 10009 1.00
p_predicted_intervention[20] 1.00 1.00 10009 1.00
p_predicted_intervention[21] 0.00 0.46 8378 1.00
p_predicted_intervention[22] 0.00 0.44 8339 1.00
p_predicted_intervention[23] 0.00 0.00 9046 1.00
p_predicted_intervention[24] 0.00 0.00 9126 1.00
p_predicted_intervention[25] 0.00 0.00 9257 1.00
p_predicted_intervention[26] 0.00 0.00 8845 1.00
p_predicted_intervention[27] 0.00 0.00 8928 1.00
p_predicted_intervention[28] 0.00 0.00 9029 1.00
p_predicted_intervention[29] 0.00 0.00 9085 1.00
p_predicted_intervention[30] 0.00 0.00 9189 1.00
p_predicted_intervention[31] 0.00 0.00 9189 1.00
p_predicted_intervention[32] 0.00 0.00 9242 1.00
p_predicted_intervention[33] 0.00 0.00 9242 1.00
p_predicted_intervention[34] 0.00 0.00 9295 1.00
p_predicted_intervention[35] 0.00 0.00 9295 1.00
p_predicted_intervention[36] 0.00 0.00 9324 1.00
p_predicted_intervention[37] 0.00 0.00 9324 1.00
p_predicted_intervention[38] 0.00 0.00 9324 1.00
p_predicted_intervention[39] 0.00 0.00 9324 1.00
p_predicted_intervention[40] 0.00 0.00 9323 1.00
p_predicted_intervention[41] 0.00 0.00 9323 1.00
p_predicted_intervention[42] 0.00 0.00 9314 1.00
p_predicted_intervention[43] 0.00 0.00 9314 1.00
p_predicted_intervention[44] 0.00 0.00 9322 1.00
p_predicted_intervention[45] 0.00 0.00 9322 1.00
p_predicted_intervention[46] 0.00 0.00 9330 1.00
p_predicted_intervention[47] 0.00 0.00 9330 1.00
p_predicted_intervention[48] 0.00 0.00 9357 1.00
p_predicted_intervention[49] 0.00 0.00 9357 1.00
p_predicted_intervention[50] 0.59 0.99 7410 1.00
p_predicted_intervention[51] 0.66 1.00 7276 1.00
p_predicted_intervention[52] 0.66 1.00 7279 1.00
p_predicted_intervention[53] 0.66 1.00 7282 1.00
p_predicted_intervention[54] 0.54 0.99 7395 1.00
p_predicted_intervention[55] 0.54 0.99 7399 1.00
p_predicted_intervention[56] 0.53 0.99 7399 1.00
p_predicted_intervention[57] 0.50 0.99 7410 1.00
p_predicted_intervention[58] 0.53 1.00 7507 1.00
p_predicted_intervention[59] 0.53 1.00 7507 1.00
p_predicted_intervention[60] 0.53 1.00 7507 1.00
p_predicted_intervention[61] 0.62 1.00 7438 1.00
p_predicted_intervention[62] 0.60 1.00 7442 1.00
p_predicted_intervention[63] 0.51 1.00 7475 1.00
p_predicted_intervention[64] 0.51 1.00 7475 1.00
p_predicted_intervention[65] 0.53 1.00 7478 1.00
p_predicted_intervention[66] 0.49 1.00 7486 1.00
p_predicted_intervention[67] 0.49 1.00 7499 1.00
p_predicted_intervention[68] 0.48 1.00 7503 1.00
p_predicted_intervention[69] 0.42 1.00 7522 1.00
p_predicted_intervention[70] 0.42 1.00 7518 1.00
p_predicted_intervention[71] 0.00 0.02 8002 1.00
p_predicted_intervention[72] 0.00 0.01 8095 1.00
p_predicted_intervention[73] 0.00 0.01 8175 1.00
p_predicted_intervention[74] 0.00 0.01 8187 1.00
p_predicted_intervention[75] 0.00 0.01 8661 1.00
p_predicted_intervention[76] 0.00 1.00 7688 1.00
p_predicted_intervention[77] 0.00 1.00 7682 1.00
p_predicted_intervention[78] 0.00 1.00 7681 1.00
p_predicted_intervention[79] 0.00 1.00 7688 1.00
p_predicted_intervention[80] 0.00 1.00 7682 1.00
p_predicted_intervention[81] 0.00 1.00 7680 1.00
p_predicted_intervention[82] 0.00 1.00 7688 1.00
p_predicted_intervention[83] 0.00 1.00 7682 1.00
p_predicted_intervention[84] 0.00 1.00 7681 1.00
p_predicted_intervention[85] 0.00 0.00 9206 1.00
p_predicted_intervention[86] 0.00 0.00 9289 1.00
p_predicted_intervention[87] 0.00 0.00 9175 1.00
p_predicted_intervention[88] 0.00 0.00 9318 1.00
p_predicted_intervention[89] 0.00 0.00 9357 1.00
p_predicted_intervention[90] 0.99 1.00 9645 1.00
p_predicted_intervention[91] 0.99 1.00 9635 1.00
p_predicted_intervention[92] 0.99 1.00 9630 1.00
p_predicted_intervention[93] 0.99 1.00 9625 1.00
p_predicted_intervention[94] 0.00 1.00 7728 1.00
p_predicted_intervention[95] 0.00 1.00 7706 1.00
p_predicted_intervention[96] 0.00 1.00 7707 1.00
p_predicted_intervention[97] 0.00 1.00 7717 1.00
p_predicted_intervention[98] 0.00 1.00 7700 1.00
p_predicted_intervention[99] 0.00 0.00 8602 1.00
p_predicted_intervention[100] 0.00 0.00 8594 1.00
p_predicted_intervention[101] 0.00 0.00 9255 1.00
p_predicted_intervention[102] 0.00 0.00 9379 1.00
p_predicted_intervention[103] 0.00 0.00 8995 1.00
p_predicted_intervention[104] 1.00 1.00 8336 1.00
p_predicted_intervention[105] 1.00 1.00 8302 1.00
p_predicted_intervention[106] 1.00 1.00 8302 1.00
p_predicted_intervention[107] 1.00 1.00 8296 1.00
p_predicted_intervention[108] 1.00 1.00 8296 1.00
p_predicted_intervention[109] 1.00 1.00 8281 1.00
p_predicted_intervention[110] 1.00 1.00 8280 1.00
p_predicted_intervention[111] 1.00 1.00 8320 1.00
p_predicted_intervention[112] 1.00 1.00 8321 1.00
p_predicted_intervention[113] 1.00 1.00 8318 1.00
p_predicted_intervention[114] 1.00 1.00 8315 1.00
p_predicted_intervention[115] 1.00 1.00 8314 1.00
p_predicted_intervention[116] 1.00 1.00 8312 1.00
p_predicted_intervention[117] 1.00 1.00 10009 1.00
p_predicted_intervention[118] 1.00 1.00 10009 1.00
p_predicted_intervention[119] 1.00 1.00 10009 1.00
p_predicted_intervention[120] 1.00 1.00 10009 1.00
p_predicted_intervention[121] 1.00 1.00 10009 1.00
p_predicted_intervention[122] 1.00 1.00 10009 1.00
p_predicted_intervention[123] 1.00 1.00 10009 1.00
p_predicted_intervention[124] 1.00 1.00 10009 1.00
p_predicted_intervention[125] 1.00 1.00 10009 1.00
p_predicted_intervention[126] 1.00 1.00 10008 1.00
p_predicted_intervention[127] 1.00 1.00 10008 1.00
p_predicted_intervention[128] 0.97 1.00 9605 1.00
p_predicted_intervention[129] 0.99 1.00 9676 1.00
p_predicted_intervention[130] 0.97 1.00 9605 1.00
p_predicted_intervention[131] 0.69 1.00 10276 1.00
p_predicted_intervention[132] 0.65 1.00 10292 1.00
p_predicted_intervention[133] 0.64 1.00 10287 1.00
p_predicted_intervention[134] 0.67 1.00 10284 1.00
p_predicted_intervention[135] 0.67 1.00 10284 1.00
p_predicted_intervention[136] 0.00 0.00 8102 1.00
p_predicted_intervention[137] 0.00 0.00 8824 1.00
p_predicted_intervention[138] 0.00 0.00 8831 1.00
p_predicted_intervention[139] 0.00 0.00 8808 1.00
p_predicted_intervention[140] 0.00 0.00 9028 1.00
p_predicted_intervention[141] 0.00 0.00 9165 1.00
p_predicted_intervention[142] 0.00 0.00 8509 1.00
p_predicted_intervention[143] 0.00 0.00 8574 1.00
p_predicted_intervention[144] 0.00 0.00 8620 1.00
p_predicted_intervention[145] 0.00 0.00 8596 1.00
p_predicted_intervention[146] 0.00 0.00 8638 1.00
p_predicted_intervention[147] 0.00 0.00 8654 1.00
p_predicted_intervention[148] 0.00 0.00 8833 1.00
p_predicted_intervention[149] 0.00 0.00 9050 1.00
p_predicted_intervention[150] 0.00 0.00 9450 1.00
p_predicted_intervention[151] 0.00 0.00 9428 1.00
p_predicted_intervention[152] 0.00 0.00 9427 1.00
p_predicted_intervention[153] 0.00 0.00 9478 1.00
p_predicted_intervention[154] 0.00 0.00 9462 1.00
p_predicted_intervention[155] 0.00 0.00 9490 1.00
p_predicted_intervention[156] 0.00 0.00 9506 1.00
p_predicted_intervention[157] 0.00 0.00 9475 1.00
p_predicted_intervention[158] 0.00 0.00 9520 1.00
p_predicted_intervention[159] 0.00 0.00 9507 1.00
p_predicted_intervention[160] 0.00 0.00 9604 1.00
p_predicted_intervention[161] 0.00 0.00 9632 1.00
p_predicted_intervention[162] 0.00 0.00 9648 1.00
p_predicted_intervention[163] 0.30 0.54 1605 1.00
p_predicted_intervention[164] 0.30 0.54 1605 1.00
p_predicted_intervention[165] 0.29 0.50 2038 1.00
p_predicted_intervention[166] 0.29 0.50 2038 1.00
p_predicted_intervention[167] 0.27 0.49 2082 1.00
p_predicted_intervention[168] 0.27 0.49 2082 1.00
p_predicted_intervention[169] 0.28 0.49 2062 1.00
p_predicted_intervention[170] 0.28 0.49 2062 1.00
p_predicted_intervention[171] 0.00 0.00 9051 1.00
p_predicted_intervention[172] 0.00 0.00 9161 1.00
p_predicted_intervention[173] 0.00 0.00 8937 1.00
p_predicted_intervention[174] 0.00 0.01 8705 1.00
p_predicted_intervention[175] 0.53 1.00 7506 1.00
p_predicted_intervention[176] 0.53 1.00 7506 1.00
p_predicted_intervention[177] 0.62 1.00 7437 1.00
p_predicted_intervention[178] 0.60 1.00 7448 1.00
p_predicted_intervention[179] 0.62 1.00 7439 1.00
p_predicted_intervention[180] 0.60 1.00 7447 1.00
p_predicted_intervention[181] 0.60 1.00 7450 1.00
p_predicted_intervention[182] 0.50 1.00 7490 1.00
p_predicted_intervention[183] 0.50 1.00 7491 1.00
p_predicted_intervention[184] 0.48 1.00 7469 1.00
p_predicted_intervention[185] 0.45 1.00 7464 1.00
p_predicted_intervention[186] 0.45 1.00 7466 1.00
p_predicted_intervention[187] 0.58 0.99 7375 1.00
p_predicted_intervention[188] 0.58 0.99 7375 1.00
p_predicted_intervention[189] 0.65 1.00 7224 1.00
p_predicted_intervention[190] 0.64 0.99 7235 1.00
p_predicted_intervention[191] 0.64 0.99 7240 1.00
p_predicted_intervention[192] 0.64 0.99 7234 1.00
p_predicted_intervention[193] 0.54 0.99 7367 1.00
p_predicted_intervention[194] 0.52 0.99 7371 1.00
p_predicted_intervention[195] 0.52 0.99 7371 1.00
p_predicted_intervention[196] 0.52 0.99 7374 1.00
p_predicted_intervention[197] 0.58 0.99 7375 1.00
p_predicted_intervention[198] 0.58 0.99 7375 1.00
p_predicted_intervention[199] 0.58 0.99 7373 1.00
p_predicted_intervention[200] 0.65 1.00 7224 1.00
p_predicted_intervention[201] 0.64 0.99 7240 1.00
p_predicted_intervention[202] 0.52 0.99 7370 1.00
p_predicted_intervention[203] 0.52 0.99 7375 1.00
p_predicted_intervention[204] 0.52 0.99 7371 1.00
p_predicted_intervention[205] 0.09 1.00 9778 1.00
p_predicted_intervention[206] 0.09 1.00 9750 1.00
p_predicted_intervention[207] 0.09 1.00 9748 1.00
p_predicted_intervention[208] 0.00 0.00 9374 1.00
p_predicted_intervention[209] 0.00 0.00 9210 1.00
p_predicted_intervention[210] 0.00 0.00 9484 1.00
p_predicted_intervention[211] 0.00 0.00 9485 1.00
p_predicted_intervention[212] 0.00 0.00 9498 1.00
p_predicted_intervention[213] 0.00 0.00 9346 1.00
p_predicted_intervention[214] 0.00 0.00 9378 1.00
p_predicted_intervention[215] 0.00 0.00 9369 1.00
p_predicted_intervention[216] 0.00 1.00 7729 1.00
p_predicted_intervention[217] 0.00 1.00 7687 1.00
p_predicted_intervention[218] 0.00 1.00 7678 1.00
p_predicted_intervention[219] 0.00 1.00 7709 1.00
p_predicted_intervention[220] 0.00 1.00 7699 1.00
p_predicted_intervention[221] 0.00 1.00 7687 1.00
p_predicted_intervention[222] 0.00 1.00 7674 1.00
p_predicted_intervention[223] 0.00 1.00 7707 1.00
p_predicted_intervention[224] 0.00 1.00 7698 1.00
p_predicted_intervention[225] 0.00 1.00 7698 1.00
p_predicted_intervention[226] 0.98 1.00 9620 1.00
p_predicted_intervention[227] 0.98 1.00 9574 1.00
p_predicted_intervention[228] 0.98 1.00 9578 1.00
p_predicted_intervention[229] 0.98 1.00 9574 1.00
p_predicted_intervention[230] 0.98 1.00 9571 1.00
p_predicted_intervention[231] 0.54 1.00 7527 1.00
p_predicted_intervention[232] 0.62 1.00 7457 1.00
p_predicted_intervention[233] 0.51 1.00 7472 1.00
p_predicted_intervention[234] 0.48 1.00 7474 1.00
p_predicted_intervention[235] 0.48 1.00 7475 1.00
p_predicted_intervention[236] 0.09 1.00 9761 1.00
p_predicted_intervention[237] 0.09 1.00 9761 1.00
p_predicted_intervention[238] 0.09 1.00 9759 1.00
p_predicted_intervention[239] 0.09 1.00 9759 1.00
p_predicted_intervention[240] 1.00 1.00 8361 1.00
p_predicted_intervention[241] 1.00 1.00 8282 1.00
p_predicted_intervention[242] 1.00 1.00 8284 1.00
p_predicted_intervention[243] 1.00 1.00 8271 1.00
p_predicted_intervention[244] 0.00 0.00 8885 1.00
p_predicted_intervention[245] 0.00 0.00 8885 1.00
p_predicted_intervention[246] 0.00 0.00 8885 1.00
p_predicted_intervention[247] 0.00 0.00 8887 1.00
p_predicted_intervention[248] 0.00 0.00 8887 1.00
p_predicted_intervention[249] 0.00 0.00 8887 1.00
p_predicted_intervention[250] 0.00 0.00 9096 1.00
p_predicted_intervention[251] 0.00 0.00 9096 1.00
p_predicted_intervention[252] 0.00 0.00 9096 1.00
p_predicted_intervention[253] 0.00 0.00 9130 1.00
p_predicted_intervention[254] 0.00 0.00 9130 1.00
p_predicted_intervention[255] 0.00 0.00 9130 1.00
p_predicted_intervention[256] 0.00 0.00 9161 1.00
p_predicted_intervention[257] 0.00 0.00 9161 1.00
p_predicted_intervention[258] 0.00 0.00 9161 1.00
p_predicted_intervention[259] 0.00 0.00 9180 1.00
p_predicted_intervention[260] 0.00 0.00 9180 1.00
p_predicted_intervention[261] 0.00 0.00 9180 1.00
p_predicted_intervention[262] 0.00 0.00 8912 1.00
p_predicted_intervention[263] 0.00 0.00 8912 1.00
p_predicted_intervention[264] 0.00 0.00 8912 1.00
p_predicted_intervention[265] 0.00 0.00 9063 1.00
p_predicted_intervention[266] 0.00 0.00 9063 1.00
p_predicted_intervention[267] 0.00 0.00 9063 1.00
p_predicted_intervention[268] 0.00 0.00 9082 1.00
p_predicted_intervention[269] 0.00 0.00 9082 1.00
p_predicted_intervention[270] 0.00 0.00 9082 1.00
p_predicted_intervention[271] 0.00 0.00 9209 1.00
p_predicted_intervention[272] 0.00 0.00 9209 1.00
p_predicted_intervention[273] 0.00 0.00 9209 1.00
p_predicted_intervention[274] 0.00 0.00 9241 1.00
p_predicted_intervention[275] 0.00 0.00 9241 1.00
p_predicted_intervention[276] 0.00 0.00 9241 1.00
p_predicted_intervention[277] 1.00 1.00 10008 1.00
p_predicted_intervention[278] 1.00 1.00 10008 1.00
p_predicted_intervention[279] 1.00 1.00 10008 1.00
p_predicted_intervention[280] 1.00 1.00 10008 1.00
p_predicted_intervention[281] 1.00 1.00 10008 1.00
p_predicted_intervention[282] 1.00 1.00 10008 1.00
p_predicted_intervention[283] 1.00 1.00 10008 1.00
p_predicted_intervention[284] 1.00 1.00 10008 1.00
p_predicted_intervention[285] 1.00 1.00 10007 1.00
p_predicted_intervention[286] 1.00 1.00 10007 1.00
p_predicted_intervention[287] 1.00 1.00 10008 1.00
p_predicted_intervention[288] 1.00 1.00 10008 1.00
p_predicted_intervention[289] 1.00 1.00 10007 1.00
p_predicted_intervention[290] 1.00 1.00 10007 1.00
p_predicted_intervention[291] 1.00 1.00 10007 1.00
p_predicted_intervention[292] 1.00 1.00 10007 1.00
p_predicted_intervention[293] 1.00 1.00 10007 1.00
p_predicted_intervention[294] 1.00 1.00 10007 1.00
p_predicted_intervention[295] 1.00 1.00 10007 1.00
p_predicted_intervention[296] 1.00 1.00 10007 1.00
p_predicted_intervention[297] 1.00 1.00 10007 1.00
p_predicted_intervention[298] 1.00 1.00 10007 1.00
p_predicted_intervention[299] 1.00 1.00 10007 1.00
p_predicted_intervention[300] 1.00 1.00 10007 1.00
p_predicted_intervention[301] 1.00 1.00 10007 1.00
p_predicted_intervention[302] 1.00 1.00 10007 1.00
p_predicted_intervention[303] 1.00 1.00 10007 1.00
p_predicted_intervention[304] 1.00 1.00 10007 1.00
p_predicted_intervention[305] 1.00 1.00 10007 1.00
p_predicted_intervention[306] 1.00 1.00 10007 1.00
p_predicted_intervention[307] 1.00 1.00 10007 1.00
p_predicted_intervention[308] 1.00 1.00 10007 1.00
p_predicted_intervention[309] 1.00 1.00 10007 1.00
p_predicted_intervention[310] 1.00 1.00 10007 1.00
p_predicted_intervention[311] 1.00 1.00 10007 1.00
p_predicted_intervention[312] 1.00 1.00 10007 1.00
p_predicted_intervention[313] 1.00 1.00 10007 1.00
p_predicted_intervention[314] 1.00 1.00 10007 1.00
p_predicted_intervention[315] 1.00 1.00 10007 1.00
p_predicted_intervention[316] 1.00 1.00 10007 1.00
p_predicted_intervention[317] 1.00 1.00 10007 1.00
p_predicted_intervention[318] 1.00 1.00 10007 1.00
p_predicted_intervention[319] 1.00 1.00 10007 1.00
p_predicted_intervention[320] 1.00 1.00 10007 1.00
p_predicted_intervention[321] 1.00 1.00 10007 1.00
p_predicted_intervention[322] 1.00 1.00 10007 1.00
p_predicted_intervention[323] 1.00 1.00 10007 1.00
p_predicted_intervention[324] 1.00 1.00 10007 1.00
p_predicted_intervention[325] 0.00 0.00 9463 1.00
p_predicted_intervention[326] 0.00 0.00 9296 1.00
p_predicted_intervention[327] 0.00 0.00 9366 1.00
p_predicted_intervention[328] 0.00 0.00 9405 1.00
p_predicted_intervention[329] 0.00 0.00 9433 1.00
p_predicted_intervention[330] 0.00 0.00 9448 1.00
p_predicted_intervention[331] 0.00 0.00 9461 1.00
p_predicted_intervention[332] 0.00 0.00 9406 1.00
p_predicted_intervention[333] 0.00 0.00 9414 1.00
p_predicted_intervention[334] 0.00 0.00 9431 1.00
p_predicted_intervention[335] 0.00 0.00 9441 1.00
p_predicted_intervention[336] 0.00 0.00 9442 1.00
p_predicted_intervention[337] 0.00 0.00 9452 1.00
p_predicted_intervention[338] 1.00 1.00 10008 1.00
p_predicted_intervention[339] 1.00 1.00 10010 1.00
p_predicted_intervention[340] 1.00 1.00 10010 1.00
p_predicted_intervention[341] 1.00 1.00 10010 1.00
p_predicted_intervention[342] 1.00 1.00 10010 1.00
p_predicted_intervention[343] 1.00 1.00 10010 1.00
p_predicted_intervention[344] 1.00 1.00 10010 1.00
p_predicted_intervention[345] 1.00 1.00 10009 1.00
p_predicted_intervention[346] 0.00 0.01 8741 1.00
p_predicted_intervention[347] 0.00 0.01 8741 1.00
p_predicted_intervention[348] 0.00 0.00 9093 1.00
p_predicted_intervention[349] 0.00 0.00 9093 1.00
p_predicted_intervention[350] 0.35 0.52 1326 1.00
p_predicted_intervention[351] 0.35 0.52 1326 1.00
p_predicted_intervention[352] 0.37 0.53 2001 1.00
p_predicted_intervention[353] 0.37 0.53 2001 1.00
p_predicted_intervention[354] 0.00 0.46 8267 1.00
p_predicted_intervention[355] 0.00 0.46 8241 1.00
p_predicted_intervention[356] 0.00 0.45 8250 1.00
p_predicted_intervention[357] 0.00 0.47 8222 1.00
p_predicted_intervention[358] 0.00 0.43 8240 1.00
p_predicted_intervention[359] 0.00 0.44 8209 1.00
p_predicted_intervention[360] 0.00 0.42 8234 1.00
p_predicted_intervention[361] 0.00 0.42 8202 1.00
p_predicted_intervention[362] 0.00 0.45 8297 1.00
p_predicted_intervention[363] 0.00 0.44 8264 1.00
p_predicted_intervention[364] 0.00 0.44 8293 1.00
p_predicted_intervention[365] 0.00 0.44 8260 1.00
p_predicted_intervention[366] 0.00 0.44 8273 1.00
p_predicted_intervention[367] 0.00 0.43 8241 1.00
p_predicted_intervention[368] 0.00 0.00 8917 1.00
p_predicted_intervention[369] 0.00 0.00 8917 1.00
p_predicted_intervention[370] 0.00 0.00 8917 1.00
p_predicted_intervention[371] 0.00 0.00 9095 1.00
p_predicted_intervention[372] 0.00 0.00 9095 1.00
p_predicted_intervention[373] 0.00 0.00 9095 1.00
p_predicted_intervention[374] 0.00 0.00 9124 1.00
p_predicted_intervention[375] 0.00 0.00 9124 1.00
p_predicted_intervention[376] 0.00 0.00 9124 1.00
p_predicted_intervention[377] 0.00 0.00 8887 1.00
p_predicted_intervention[378] 0.00 0.00 8887 1.00
p_predicted_intervention[379] 0.00 0.00 8887 1.00
p_predicted_intervention[380] 0.00 0.00 8916 1.00
p_predicted_intervention[381] 0.00 0.00 8916 1.00
p_predicted_intervention[382] 0.00 0.00 8916 1.00
p_predicted_intervention[383] 0.00 0.00 8921 1.00
p_predicted_intervention[384] 0.00 0.00 8921 1.00
p_predicted_intervention[385] 0.00 0.00 8921 1.00
p_predicted_intervention[386] 0.00 0.00 8932 1.00
p_predicted_intervention[387] 0.00 0.00 8932 1.00
p_predicted_intervention[388] 0.00 0.00 8932 1.00
p_predicted_intervention[389] 0.00 0.00 8930 1.00
p_predicted_intervention[390] 0.00 0.00 8930 1.00
p_predicted_intervention[391] 0.00 0.00 8930 1.00
p_predicted_intervention[392] 0.00 0.00 8920 1.00
p_predicted_intervention[393] 0.00 0.00 8920 1.00
p_predicted_intervention[394] 0.00 0.00 8920 1.00
p_predicted_intervention[395] 0.00 0.00 8924 1.00
p_predicted_intervention[396] 0.00 0.00 8924 1.00
p_predicted_intervention[397] 0.00 0.00 8924 1.00
p_predicted_intervention[398] 0.00 0.00 8924 1.00
p_predicted_intervention[399] 0.00 0.00 8924 1.00
p_predicted_intervention[400] 0.00 0.00 8924 1.00
p_predicted_intervention[401] 0.00 0.00 8933 1.00
p_predicted_intervention[402] 0.00 0.00 8933 1.00
p_predicted_intervention[403] 0.00 0.00 8933 1.00
p_predicted_intervention[404] 0.00 0.00 8929 1.00
p_predicted_intervention[405] 0.00 0.00 8929 1.00
p_predicted_intervention[406] 0.00 0.00 8929 1.00
p_predicted_intervention[407] 0.00 0.00 8936 1.00
p_predicted_intervention[408] 0.00 0.00 8936 1.00
p_predicted_intervention[409] 0.00 0.00 8936 1.00
p_predicted_intervention[410] 0.99 1.00 9643 1.00
p_predicted_intervention[411] 0.99 1.00 9643 1.00
p_predicted_intervention[412] 0.99 1.00 9643 1.00
p_predicted_intervention[413] 0.99 1.00 9666 1.00
p_predicted_intervention[414] 0.00 0.00 9215 1.00
p_predicted_intervention[415] 0.00 0.00 9269 1.00
p_predicted_intervention[416] 0.00 0.00 9290 1.00
p_predicted_intervention[417] 0.00 0.00 9313 1.00
p_predicted_intervention[418] 0.00 0.00 9288 1.00
p_predicted_intervention[419] 0.00 0.00 9268 1.00
p_predicted_intervention[420] 0.00 0.00 9136 1.00
p_predicted_intervention[421] 0.00 0.00 9323 1.00
p_predicted_intervention[422] 0.00 0.00 9255 1.00
p_predicted_intervention[423] 0.00 0.00 9357 1.00
p_predicted_intervention[424] 0.00 0.00 9245 1.00
p_predicted_intervention[425] 0.00 0.00 9249 1.00
p_predicted_intervention[426] 0.00 0.00 9312 1.00
p_predicted_intervention[427] 0.00 0.00 9179 1.00
p_predicted_intervention[428] 0.00 0.00 9237 1.00
p_predicted_intervention[429] 0.00 0.00 9288 1.00
p_predicted_intervention[430] 0.00 0.00 9299 1.00
p_predicted_intervention[431] 0.00 0.00 9399 1.00
p_predicted_intervention[432] 0.00 0.00 9469 1.00
p_predicted_intervention[433] 0.00 0.00 9237 1.00
p_predicted_intervention[434] 0.00 0.00 9386 1.00
p_predicted_intervention[435] 0.00 0.00 8844 1.00
p_predicted_intervention[436] 0.00 0.00 8861 1.00
p_predicted_intervention[437] 0.00 0.00 8654 1.00
p_predicted_intervention[438] 0.00 0.00 9211 1.00
p_predicted_intervention[439] 0.00 0.00 9250 1.00
p_predicted_intervention[440] 0.00 0.00 9251 1.00
p_predicted_intervention[441] 0.00 0.00 9271 1.00
p_predicted_intervention[442] 0.00 0.00 8103 1.00
p_predicted_intervention[443] 0.00 0.00 8791 1.00
p_predicted_intervention[444] 0.00 0.00 8900 1.00
p_predicted_intervention[445] 0.00 0.00 8871 1.00
p_predicted_intervention[446] 0.00 0.00 8918 1.00
p_predicted_intervention[447] 0.00 0.00 9067 1.00
p_predicted_intervention[448] 0.00 0.00 8979 1.00
p_predicted_intervention[449] 0.00 0.00 8289 1.00
p_predicted_intervention[450] 0.00 0.00 8371 1.00
p_predicted_intervention[451] 0.00 0.00 9103 1.00
p_predicted_intervention[452] 0.00 0.00 8430 1.00
p_predicted_intervention[453] 0.00 0.00 8470 1.00
p_predicted_intervention[454] 0.00 0.00 8557 1.00
p_predicted_intervention[455] 0.00 0.00 8604 1.00
p_predicted_intervention[456] 0.00 0.00 9561 1.00
p_predicted_intervention[457] 0.00 0.00 9138 1.00
p_predicted_intervention[458] 0.00 0.00 9150 1.00
p_predicted_intervention[459] 0.00 0.00 9228 1.00
p_predicted_intervention[460] 0.00 0.00 8114 1.00
p_predicted_intervention[461] 0.00 0.00 8787 1.00
p_predicted_intervention[462] 0.00 0.00 8966 1.00
p_predicted_intervention[463] 0.00 0.00 9086 1.00
p_predicted_intervention[464] 0.00 0.00 9134 1.00
p_predicted_intervention[465] 0.00 0.00 9180 1.00
p_predicted_intervention[466] 0.00 0.00 8565 1.00
p_predicted_intervention[467] 0.00 0.00 8562 1.00
p_predicted_intervention[468] 0.00 0.00 8599 1.00
p_predicted_intervention[469] 0.39 0.57 1664 1.00
p_predicted_intervention[470] 0.32 0.52 1469 1.00
p_predicted_intervention[471] 0.28 0.48 1475 1.00
p_predicted_intervention[472] 0.29 0.49 1473 1.00
p_predicted_intervention[473] 0.34 0.53 1979 1.00
p_predicted_intervention[474] 0.36 0.55 1942 1.00
p_predicted_intervention[475] 0.35 0.54 1963 1.00
p_predicted_intervention[476] 0.34 0.53 1985 1.00
p_predicted_intervention[477] 0.27 0.47 2106 1.00
p_predicted_intervention[478] 0.25 0.45 2139 1.00
p_predicted_intervention[479] 0.21 0.41 2143 1.00
p_predicted_intervention[480] 0.19 0.40 2106 1.00
p_predicted_intervention[481] 0.00 0.00 8765 1.00
p_predicted_intervention[482] 0.00 0.00 9127 1.00
p_predicted_intervention[483] 0.00 0.00 9108 1.00
p_predicted_intervention[484] 0.00 0.00 9030 1.00
p_predicted_intervention[485] 0.00 0.00 9081 1.00
p_predicted_intervention[486] 0.00 0.00 9121 1.00
p_predicted_intervention[487] 0.00 0.00 9102 1.00
p_predicted_intervention[488] 0.00 0.00 9131 1.00
p_predicted_intervention[489] 0.00 0.00 9156 1.00
p_predicted_intervention[490] 0.00 0.00 9183 1.00
p_predicted_intervention[491] 0.98 1.00 9648 1.00
p_predicted_intervention[492] 0.98 1.00 9648 1.00
p_predicted_intervention[493] 0.98 1.00 9640 1.00
p_predicted_intervention[494] 0.98 1.00 9640 1.00
p_predicted_intervention[495] 0.98 1.00 9662 1.00
p_predicted_intervention[496] 0.98 1.00 9662 1.00
p_predicted_intervention[497] 0.99 1.00 9591 1.00
p_predicted_intervention[498] 0.99 1.00 9591 1.00
p_predicted_intervention[499] 0.99 1.00 9592 1.00
p_predicted_intervention[500] 0.99 1.00 9592 1.00
p_predicted_intervention[501] 0.39 0.57 1668 1.00
p_predicted_intervention[502] 0.33 0.53 1469 1.00
p_predicted_intervention[503] 0.35 0.54 1955 1.00
p_predicted_intervention[504] 0.35 0.54 1971 1.00
p_predicted_intervention[505] 0.34 0.53 1985 1.00
p_predicted_intervention[506] 0.34 0.53 1991 1.00
p_predicted_intervention[507] 0.33 0.52 2004 1.00
p_predicted_intervention[508] 0.28 0.48 2082 1.00
p_predicted_intervention[509] 0.26 0.46 2119 1.00
p_predicted_intervention[510] 0.22 0.42 2151 1.00
p_predicted_intervention[511] 0.21 0.41 2131 1.00
p_predicted_intervention[512] 0.54 1.00 10177 1.00
p_predicted_intervention[513] 0.56 1.00 10136 1.00
p_predicted_intervention[514] 0.55 1.00 10130 1.00
p_predicted_intervention[515] 0.55 1.00 10124 1.00
p_predicted_intervention[516] 0.54 1.00 10114 1.00
p_predicted_intervention[517] 0.56 1.00 10107 1.00
p_predicted_intervention[518] 0.56 1.00 10098 1.00
p_predicted_intervention[519] 0.56 1.00 10102 1.00
p_predicted_intervention[520] 0.56 1.00 10100 1.00
p_predicted_intervention[521] 0.56 1.00 10095 1.00
p_predicted_intervention[522] 0.55 1.00 10097 1.00
p_predicted_intervention[523] 0.00 0.00 9343 1.00
p_predicted_intervention[524] 0.00 0.00 9363 1.00
p_predicted_intervention[525] 0.00 0.00 9126 1.00
p_predicted_intervention[526] 0.00 0.00 9126 1.00
p_predicted_intervention[527] 0.00 0.00 9126 1.00
p_predicted_intervention[528] 0.00 0.00 9206 1.00
p_predicted_intervention[529] 0.00 0.00 9206 1.00
p_predicted_intervention[530] 0.00 0.00 9206 1.00
p_predicted_intervention[531] 0.00 0.00 9248 1.00
p_predicted_intervention[532] 0.00 0.00 9248 1.00
p_predicted_intervention[533] 0.00 0.00 9248 1.00
p_predicted_intervention[534] 0.00 0.00 9278 1.00
p_predicted_intervention[535] 0.00 0.00 9278 1.00
p_predicted_intervention[536] 0.00 0.00 9278 1.00
p_predicted_intervention[537] 0.00 0.00 9108 1.00
p_predicted_intervention[538] 0.00 0.00 9108 1.00
p_predicted_intervention[539] 0.00 0.00 9108 1.00
p_predicted_intervention[540] 0.00 0.00 9218 1.00
p_predicted_intervention[541] 0.00 0.00 9218 1.00
p_predicted_intervention[542] 0.00 0.00 9218 1.00
p_predicted_intervention[543] 0.00 0.00 9272 1.00
p_predicted_intervention[544] 0.00 0.00 9272 1.00
p_predicted_intervention[545] 0.00 0.00 9272 1.00
p_predicted_intervention[546] 0.00 0.00 9246 1.00
p_predicted_intervention[547] 0.00 0.00 9246 1.00
p_predicted_intervention[548] 0.00 0.00 9246 1.00
p_predicted_intervention[549] 0.00 0.00 9228 1.00
p_predicted_intervention[550] 0.00 0.00 9228 1.00
p_predicted_intervention[551] 0.00 0.00 9228 1.00
p_predicted_intervention[552] 1.00 1.00 10008 1.00
p_predicted_intervention[553] 1.00 1.00 10008 1.00
p_predicted_intervention[554] 1.00 1.00 10008 1.00
p_predicted_intervention[555] 1.00 1.00 10007 1.00
p_predicted_intervention[556] 1.00 1.00 10007 1.00
p_predicted_intervention[557] 1.00 1.00 10007 1.00
p_predicted_intervention[558] 1.00 1.00 10007 1.00
p_predicted_intervention[559] 1.00 1.00 10007 1.00
p_predicted_intervention[560] 1.00 1.00 10007 1.00
p_predicted_intervention[561] 1.00 1.00 10007 1.00
p_predicted_intervention[562] 1.00 1.00 10007 1.00
p_predicted_intervention[563] 1.00 1.00 10007 1.00
p_predicted_intervention[564] 1.00 1.00 10007 1.00
p_predicted_intervention[565] 1.00 1.00 10007 1.00
p_predicted_intervention[566] 1.00 1.00 10007 1.00
p_predicted_intervention[567] 1.00 1.00 10007 1.00
p_predicted_intervention[568] 1.00 1.00 10007 1.00
p_predicted_intervention[569] 1.00 1.00 10007 1.00
p_predicted_intervention[570] 1.00 1.00 10007 1.00
p_predicted_intervention[571] 0.21 1.00 9379 1.00
p_predicted_intervention[572] 0.22 1.00 9498 1.00
p_predicted_intervention[573] 0.22 1.00 9504 1.00
p_predicted_intervention[574] 0.22 1.00 9506 1.00
p_predicted_intervention[575] 0.22 1.00 9484 1.00
p_predicted_intervention[576] 0.00 0.47 8257 1.00
p_predicted_intervention[577] 0.00 0.46 8313 1.00
p_predicted_intervention[578] 0.00 0.44 8299 1.00
p_predicted_intervention[579] 0.98 1.00 9690 1.00
p_predicted_intervention[580] 0.99 1.00 9688 1.00
p_predicted_intervention[581] 0.99 1.00 9612 1.00
p_predicted_intervention[582] 0.00 0.00 8103 1.00
p_predicted_intervention[583] 0.00 0.00 8785 1.00
p_predicted_intervention[584] 0.00 0.00 9126 1.00
p_predicted_intervention[585] 0.00 0.00 8472 1.00
p_predicted_intervention[586] 0.00 0.00 8655 1.00
p_predicted_intervention[587] 0.00 0.50 8270 1.00
p_predicted_intervention[588] 0.00 0.48 8249 1.00
p_predicted_intervention[589] 0.00 0.48 8249 1.00
p_predicted_intervention[590] 0.00 0.49 8223 1.00
p_predicted_intervention[591] 0.00 0.49 8274 1.00
p_predicted_intervention[592] 0.00 0.48 8248 1.00
p_predicted_intervention[593] 0.00 0.49 8264 1.00
p_predicted_intervention[594] 0.00 0.49 8237 1.00
p_predicted_intervention[595] 0.00 0.51 8307 1.00
p_predicted_intervention[596] 0.00 0.48 8282 1.00
p_predicted_intervention[597] 0.41 0.55 1606 1.00
p_predicted_intervention[598] 0.34 0.50 1323 1.00
p_predicted_intervention[599] 0.33 0.49 1319 1.00
p_predicted_intervention[600] 0.32 0.48 1316 1.00
p_predicted_intervention[601] 0.37 0.52 2020 1.00
p_predicted_intervention[602] 0.39 0.54 1964 1.00
p_predicted_intervention[603] 0.31 0.48 2043 1.00
p_predicted_intervention[604] 0.30 0.47 2059 1.00
p_predicted_intervention[605] 0.27 0.45 2096 1.00
p_predicted_intervention[606] 0.23 0.41 2006 1.00
p_predicted_intervention[607] 1.00 1.00 9982 1.00
p_predicted_intervention[608] 1.00 1.00 9982 1.00
p_predicted_intervention[609] 1.00 1.00 9987 1.00
p_predicted_intervention[610] 1.00 1.00 9987 1.00
p_predicted_intervention[611] 1.00 1.00 9987 1.00
p_predicted_intervention[612] 1.00 1.00 9987 1.00
p_predicted_intervention[613] 1.00 1.00 9988 1.00
p_predicted_intervention[614] 1.00 1.00 9988 1.00
p_predicted_intervention[615] 1.00 1.00 9988 1.00
p_predicted_intervention[616] 1.00 1.00 9988 1.00
p_predicted_intervention[617] 0.34 0.48 2172 1.00
p_predicted_intervention[618] 0.34 0.48 2170 1.00
p_predicted_intervention[619] 0.32 0.47 2196 1.00
p_predicted_intervention[620] 0.95 1.00 10924 1.00
p_predicted_intervention[621] 0.95 1.00 10911 1.00
p_predicted_intervention[622] 0.49 1.00 10033 1.00
p_predicted_intervention[623] 0.50 1.00 10055 1.00
p_predicted_intervention[624] 0.49 1.00 10030 1.00
p_predicted_intervention[625] 0.45 1.00 9940 1.00
p_predicted_intervention[626] 0.99 1.00 9693 1.00
p_predicted_intervention[627] 0.99 1.00 9667 1.00
p_predicted_intervention[628] 0.99 1.00 9668 1.00
p_predicted_intervention[629] 0.99 1.00 9621 1.00
p_predicted_intervention[630] 0.00 0.00 9186 1.00
p_predicted_intervention[631] 0.00 0.00 9201 1.00
p_predicted_intervention[632] 0.00 0.00 9085 1.00
p_predicted_intervention[633] 0.00 0.00 9101 1.00
p_predicted_intervention[634] 0.00 0.00 9111 1.00
p_predicted_intervention[635] 0.00 0.00 9487 1.00
p_predicted_intervention[636] 0.00 0.42 8294 1.00
p_predicted_intervention[637] 0.00 0.42 8263 1.00
p_predicted_intervention[638] 0.00 0.41 8265 1.00
p_predicted_intervention[639] 0.00 0.42 8232 1.00
p_predicted_intervention[640] 0.00 0.41 8237 1.00
p_predicted_intervention[641] 0.00 0.41 8205 1.00
p_predicted_intervention[642] 0.00 0.41 8243 1.00
p_predicted_intervention[643] 0.00 0.42 8210 1.00
p_predicted_intervention[644] 0.00 0.44 8292 1.00
p_predicted_intervention[645] 0.00 0.43 8257 1.00
p_predicted_intervention[646] 0.00 0.44 8251 1.00
p_predicted_intervention[647] 0.00 0.42 8217 1.00
p_predicted_intervention[648] 0.00 0.44 8259 1.00
p_predicted_intervention[649] 0.00 0.42 8226 1.00
p_predicted_intervention[650] 0.54 0.98 7323 1.00
p_predicted_intervention[651] 0.54 0.98 7323 1.00
p_predicted_intervention[652] 0.53 0.98 7322 1.00
p_predicted_intervention[653] 0.53 0.98 7322 1.00
p_predicted_intervention[654] 0.50 0.98 7226 1.00
p_predicted_intervention[655] 0.50 0.98 7226 1.00
p_predicted_intervention[656] 0.66 1.00 7657 1.00
p_predicted_intervention[657] 0.66 1.00 7657 1.00
p_predicted_intervention[658] 0.66 1.00 7664 1.00
p_predicted_intervention[659] 0.66 1.00 7664 1.00
p_predicted_intervention[660] 0.54 1.00 7560 1.00
p_predicted_intervention[661] 0.54 1.00 7560 1.00
p_predicted_intervention[662] 0.52 1.00 7572 1.00
p_predicted_intervention[663] 0.52 1.00 7572 1.00
p_predicted_intervention[664] 0.50 1.00 7577 1.00
p_predicted_intervention[665] 0.50 1.00 7577 1.00
p_predicted_intervention[666] 0.50 1.00 7577 1.00
p_predicted_intervention[667] 0.50 1.00 7577 1.00
p_predicted_intervention[668] 0.49 1.00 10091 1.00
p_predicted_intervention[669] 0.49 1.00 10091 1.00
p_predicted_intervention[670] 0.51 1.00 10117 1.00
p_predicted_intervention[671] 0.49 1.00 10088 1.00
p_predicted_intervention[672] 0.48 1.00 10065 1.00
p_predicted_intervention[673] 0.48 1.00 10062 1.00
p_predicted_intervention[674] 0.47 1.00 10047 1.00
p_predicted_intervention[675] 0.46 1.00 10036 1.00
p_predicted_intervention[676] 0.45 1.00 9993 1.00
p_predicted_intervention[677] 0.44 1.00 9984 1.00
p_predicted_intervention[678] 0.44 1.00 9983 1.00
p_predicted_intervention[679] 0.44 1.00 9982 1.00
p_predicted_intervention[680] 0.44 1.00 9981 1.00
p_predicted_intervention[681] 0.44 1.00 9980 1.00
p_predicted_intervention[682] 0.44 1.00 9974 1.00
p_predicted_intervention[683] 0.44 1.00 9969 1.00
p_predicted_intervention[684] 0.44 1.00 9968 1.00
p_predicted_intervention[685] 0.44 1.00 9965 1.00
p_predicted_intervention[686] 0.44 1.00 9963 1.00
p_predicted_intervention[687] 0.44 1.00 9962 1.00
p_predicted_intervention[688] 0.44 1.00 9960 1.00
p_predicted_intervention[689] 0.97 1.00 9641 1.00
p_predicted_intervention[690] 0.99 1.00 9710 1.00
p_predicted_intervention[691] 0.97 1.00 9641 1.00
p_predicted_intervention[692] 0.97 1.00 9594 1.00
p_predicted_intervention[693] 0.99 1.00 9665 1.00
p_predicted_intervention[694] 0.97 1.00 9594 1.00
p_predicted_intervention[695] 0.97 1.00 9588 1.00
p_predicted_intervention[696] 0.99 1.00 9657 1.00
p_predicted_intervention[697] 0.97 1.00 9588 1.00
p_predicted_intervention[698] 0.97 1.00 9581 1.00
p_predicted_intervention[699] 0.99 1.00 9651 1.00
p_predicted_intervention[700] 0.97 1.00 9581 1.00
p_predicted_intervention[701] 0.97 1.00 9546 1.00
p_predicted_intervention[702] 0.99 1.00 9604 1.00
p_predicted_intervention[703] 0.97 1.00 9546 1.00
p_predicted_intervention[704] 0.97 1.00 9545 1.00
p_predicted_intervention[705] 0.99 1.00 9604 1.00
p_predicted_intervention[706] 0.97 1.00 9545 1.00
p_predicted_intervention[707] 0.97 1.00 9541 1.00
p_predicted_intervention[708] 0.99 1.00 9605 1.00
p_predicted_intervention[709] 0.97 1.00 9541 1.00
p_predicted_intervention[710] 0.00 0.00 9090 1.00
p_predicted_intervention[711] 0.00 0.00 8880 1.00
p_predicted_intervention[712] 0.39 0.56 1631 1.00
p_predicted_intervention[713] 0.33 0.52 1432 1.00
p_predicted_intervention[714] 0.33 0.52 1432 1.00
p_predicted_intervention[715] 0.31 0.51 1431 1.00
p_predicted_intervention[716] 0.31 0.49 1431 1.00
p_predicted_intervention[717] 0.37 0.55 1895 1.00
p_predicted_intervention[718] 0.36 0.53 1937 1.00
p_predicted_intervention[719] 0.33 0.52 1942 1.00
p_predicted_intervention[720] 0.27 0.46 2095 1.00
p_predicted_intervention[721] 0.14 1.00 9417 1.00
p_predicted_intervention[722] 0.16 1.00 9496 1.00
p_predicted_intervention[723] 0.15 1.00 9460 1.00
p_predicted_intervention[724] 0.15 1.00 9462 1.00
p_predicted_intervention[725] 0.15 1.00 9477 1.00
p_predicted_intervention[726] 0.15 1.00 9478 1.00
p_predicted_intervention[727] 0.15 1.00 9487 1.00
p_predicted_intervention[728] 0.15 1.00 9504 1.00
p_predicted_intervention[729] 0.15 1.00 9470 1.00
p_predicted_intervention[730] 0.00 0.41 8278 1.00
p_predicted_intervention[731] 0.00 0.41 8256 1.00
p_predicted_intervention[732] 0.00 0.44 8253 1.00
p_predicted_intervention[733] 0.96 1.00 6780 1.00
p_predicted_intervention[734] 0.96 1.00 6780 1.00
p_predicted_intervention[735] 0.96 1.00 6788 1.00
p_predicted_intervention[736] 0.94 1.00 6922 1.00
p_predicted_intervention[737] 0.94 1.00 6921 1.00
p_predicted_intervention[738] 0.94 1.00 6928 1.00
p_predicted_intervention[739] 0.94 1.00 6940 1.00
p_predicted_intervention[740] 0.94 1.00 6943 1.00
p_predicted_intervention[741] 1.00 1.00 8090 1.00
p_predicted_intervention[742] 1.00 1.00 8090 1.00
p_predicted_intervention[743] 0.56 1.00 10698 1.00
p_predicted_intervention[744] 0.56 1.00 10697 1.00
p_predicted_intervention[745] 0.56 1.00 10692 1.00
p_predicted_intervention[746] 0.56 1.00 10699 1.00
p_predicted_intervention[747] 0.56 1.00 10695 1.00
p_predicted_intervention[748] 0.47 1.00 10587 1.00
p_predicted_intervention[749] 0.48 1.00 10576 1.00
p_predicted_intervention[750] 0.48 1.00 10578 1.00
p_predicted_intervention[751] 0.47 1.00 10594 1.00
p_predicted_intervention[752] 0.74 1.00 10537 1.00
p_predicted_intervention[753] 0.74 1.00 10543 1.00
p_predicted_intervention[754] 0.74 1.00 10541 1.00
p_predicted_intervention[755] 0.74 1.00 10544 1.00
p_predicted_intervention[756] 0.74 1.00 10546 1.00
p_predicted_intervention[757] 0.75 1.00 10515 1.00
p_predicted_intervention[758] 0.58 1.00 10687 1.00
p_predicted_intervention[759] 0.58 1.00 10687 1.00
p_predicted_intervention[760] 0.58 1.00 10686 1.00
p_predicted_intervention[761] 0.57 1.00 10685 1.00
p_predicted_intervention[762] 0.57 1.00 10679 1.00
p_predicted_intervention[763] 0.57 1.00 10681 1.00
p_predicted_intervention[764] 0.57 1.00 10681 1.00
p_predicted_intervention[765] 0.57 1.00 10680 1.00
p_predicted_intervention[766] 0.57 1.00 10680 1.00
p_predicted_intervention[767] 0.56 1.00 10678 1.00
p_predicted_intervention[768] 0.57 1.00 10677 1.00
p_predicted_intervention[769] 0.56 1.00 10695 1.00
p_predicted_intervention[770] 0.58 1.00 10687 1.00
p_predicted_intervention[771] 0.58 1.00 10686 1.00
p_predicted_intervention[772] 0.57 1.00 10679 1.00
p_predicted_intervention[773] 0.57 1.00 10680 1.00
p_predicted_intervention[774] 0.57 1.00 10682 1.00
p_predicted_intervention[775] 0.57 1.00 10681 1.00
p_predicted_intervention[776] 0.57 1.00 10680 1.00
p_predicted_intervention[777] 0.57 1.00 10679 1.00
p_predicted_intervention[778] 0.56 1.00 10696 1.00
p_predicted_intervention[779] 0.56 1.00 10696 1.00
p_predicted_intervention[780] 0.56 1.00 10695 1.00
p_predicted_intervention[781] 0.58 1.00 10687 1.00
p_predicted_intervention[782] 0.58 1.00 10687 1.00
p_predicted_intervention[783] 0.57 1.00 10682 1.00
p_predicted_intervention[784] 0.57 1.00 10679 1.00
p_predicted_intervention[785] 0.57 1.00 10678 1.00
p_predicted_intervention[786] 0.56 1.00 10678 1.00
p_predicted_intervention[787] 0.56 1.00 10695 1.00
p_predicted_intervention[788] 0.56 1.00 10695 1.00
p_predicted_intervention[789] 0.56 1.00 10693 1.00
p_predicted_intervention[790] 0.56 1.00 10693 1.00
p_predicted_intervention[791] 0.33 0.50 1320 1.00
p_predicted_intervention[792] 0.32 0.48 1317 1.00
p_predicted_intervention[793] 0.32 0.48 1316 1.00
p_predicted_intervention[794] 0.33 0.49 1319 1.00
p_predicted_intervention[795] 0.40 0.55 1921 1.00
p_predicted_intervention[796] 0.39 0.54 1953 1.00
p_predicted_intervention[797] 0.39 0.54 1953 1.00
p_predicted_intervention[798] 0.39 0.54 1961 1.00
p_predicted_intervention[799] 0.15 1.00 9582 1.00
p_predicted_intervention[800] 0.15 1.00 9599 1.00
p_predicted_intervention[801] 0.15 1.00 9602 1.00
p_predicted_intervention[802] 0.16 1.00 9614 1.00
p_predicted_intervention[803] 0.16 1.00 9630 1.00
p_predicted_intervention[804] 0.15 1.00 9636 1.00
p_predicted_intervention[805] 0.16 1.00 9561 1.00
p_predicted_intervention[806] 0.16 1.00 9562 1.00
p_predicted_intervention[807] 1.00 1.00 10008 1.00
p_predicted_intervention[808] 1.00 1.00 10007 1.00
p_predicted_intervention[809] 1.00 1.00 10008 1.00
p_predicted_intervention[810] 1.00 1.00 10007 1.00
p_predicted_intervention[811] 1.00 1.00 10007 1.00
p_predicted_intervention[812] 1.00 1.00 10007 1.00
p_predicted_intervention[813] 1.00 1.00 10007 1.00
p_predicted_intervention[814] 1.00 1.00 10007 1.00
p_predicted_intervention[815] 1.00 1.00 10007 1.00
p_predicted_intervention[816] 1.00 1.00 10007 1.00
p_predicted_intervention[817] 1.00 1.00 10007 1.00
p_predicted_intervention[818] 0.48 1.00 10031 1.00
p_predicted_intervention[819] 0.49 1.00 10062 1.00
p_predicted_intervention[820] 0.49 1.00 10060 1.00
p_predicted_intervention[821] 0.49 1.00 10025 1.00
p_predicted_intervention[822] 0.47 1.00 9988 1.00
p_predicted_intervention[823] 0.48 1.00 10005 1.00
p_predicted_intervention[824] 0.47 1.00 10004 1.00
p_predicted_intervention[825] 0.46 1.00 9976 1.00
p_predicted_intervention[826] 0.46 1.00 9986 1.00
p_predicted_intervention[827] 0.44 1.00 9942 1.00
p_predicted_intervention[828] 0.57 1.00 10669 1.00
p_predicted_intervention[829] 0.56 1.00 10672 1.00
p_predicted_intervention[830] 0.00 0.49 8361 1.00
p_predicted_intervention[831] 0.00 0.48 8332 1.00
p_predicted_intervention[832] 0.00 0.48 8356 1.00
p_predicted_intervention[833] 0.00 0.47 8338 1.00
p_predicted_intervention[834] 0.00 0.46 8334 1.00
p_predicted_intervention[835] 0.00 0.47 8355 1.00
p_predicted_intervention[836] 0.00 0.00 8831 1.00
p_predicted_intervention[837] 0.37 0.59 1761 1.00
p_predicted_intervention[838] 0.00 0.00 8904 1.00
p_predicted_intervention[839] 0.30 0.55 1646 1.00
p_predicted_intervention[840] 0.00 0.00 8893 1.00
p_predicted_intervention[841] 0.30 0.55 1643 1.00
p_predicted_intervention[842] 0.00 0.00 8901 1.00
p_predicted_intervention[843] 0.30 0.55 1645 1.00
p_predicted_intervention[844] 0.00 0.00 9064 1.00
p_predicted_intervention[845] 0.25 0.50 1731 1.00
p_predicted_intervention[846] 0.00 0.00 9058 1.00
p_predicted_intervention[847] 0.25 0.51 1729 1.00
p_predicted_intervention[848] 0.00 0.00 9120 1.00
p_predicted_intervention[849] 0.24 0.48 1745 1.00
p_predicted_intervention[850] 0.00 0.00 9043 1.00
p_predicted_intervention[851] 0.30 0.54 2055 1.00
p_predicted_intervention[852] 0.00 0.00 9291 1.00
p_predicted_intervention[853] 0.24 0.47 2232 1.00
p_predicted_intervention[854] 0.00 0.00 9361 1.00
p_predicted_intervention[855] 0.22 0.46 2271 1.00
p_predicted_intervention[856] 0.00 0.00 9509 1.00
p_predicted_intervention[857] 0.18 0.42 2348 1.00
p_predicted_intervention[858] 0.33 0.49 1334 1.00
p_predicted_intervention[859] 0.32 0.48 1330 1.00
p_predicted_intervention[860] 0.31 0.46 1324 1.00
p_predicted_intervention[861] 0.30 0.46 1323 1.00
p_predicted_intervention[862] 0.29 0.44 1318 1.00
p_predicted_intervention[863] 0.28 0.43 1316 1.00
p_predicted_intervention[864] 0.35 0.50 1990 1.00
p_predicted_intervention[865] 0.32 0.48 2067 1.00
p_predicted_intervention[866] 0.31 0.46 2104 1.00
p_predicted_intervention[867] 0.32 0.47 2080 1.00
p_predicted_intervention[868] 0.32 0.48 2069 1.00
p_predicted_intervention[869] 0.33 0.48 2051 1.00
p_predicted_intervention[870] 0.21 1.00 9378 1.00
p_predicted_intervention[871] 0.22 1.00 9497 1.00
p_predicted_intervention[872] 0.22 1.00 9505 1.00
p_predicted_intervention[873] 0.21 1.00 9507 1.00
p_predicted_intervention[874] 0.22 1.00 9481 1.00
p_predicted_intervention[875] 0.21 1.00 9484 1.00
p_predicted_intervention[876] 0.00 0.01 8401 1.00
p_predicted_intervention[877] 0.00 0.02 8126 1.00
p_predicted_intervention[878] 0.00 0.01 8528 1.00
p_predicted_intervention[879] 0.00 0.01 8570 1.00
p_predicted_intervention[880] 0.56 1.00 7521 1.00
p_predicted_intervention[881] 0.64 1.00 7509 1.00
p_predicted_intervention[882] 0.54 1.00 7529 1.00
p_predicted_intervention[883] 0.52 1.00 7543 1.00
p_predicted_intervention[884] 0.51 1.00 7542 1.00
p_predicted_intervention[885] 0.52 1.00 10594 1.00
p_predicted_intervention[886] 0.57 1.00 10669 1.00
p_predicted_intervention[887] 0.57 1.00 10668 1.00
p_predicted_intervention[888] 0.57 1.00 10667 1.00
p_predicted_intervention[889] 0.57 1.00 10663 1.00
p_predicted_intervention[890] 0.56 1.00 10654 1.00
p_predicted_intervention[891] 0.55 1.00 10651 1.00
p_predicted_intervention[892] 0.55 1.00 10652 1.00
p_predicted_intervention[893] 0.55 1.00 10650 1.00
p_predicted_intervention[894] 0.55 1.00 10649 1.00
p_predicted_intervention[895] 0.55 1.00 10646 1.00
p_predicted_intervention[896] 0.54 1.00 10670 1.00
p_predicted_intervention[897] 0.47 1.00 10587 1.00
p_predicted_intervention[898] 0.47 1.00 10582 1.00
p_predicted_intervention[899] 0.47 1.00 10597 1.00
p_predicted_intervention[900] 0.47 1.00 10590 1.00
p_predicted_intervention[901] 0.39 0.56 1631 1.00
p_predicted_intervention[902] 0.32 0.52 1432 1.00
p_predicted_intervention[903] 0.33 0.52 1942 1.00
p_predicted_intervention[904] 0.31 0.50 1990 1.00
p_predicted_intervention[905] 0.29 0.48 2046 1.00
p_predicted_intervention[906] 0.26 0.45 2108 1.00
p_predicted_intervention[907] 0.00 0.01 8384 1.00
p_predicted_intervention[908] 0.00 0.01 8534 1.00
p_predicted_intervention[909] 0.00 0.01 7989 1.00
p_predicted_intervention[910] 0.00 0.01 8170 1.00
p_predicted_intervention[911] 0.00 0.01 8179 1.00
p_predicted_intervention[912] 0.97 1.00 9635 1.00
p_predicted_intervention[913] 0.99 1.00 9693 1.00
p_predicted_intervention[914] 0.97 1.00 9635 1.00
p_predicted_intervention[915] 0.97 1.00 9638 1.00
p_predicted_intervention[916] 0.99 1.00 9706 1.00
p_predicted_intervention[917] 0.97 1.00 9638 1.00
p_predicted_intervention[918] 0.98 1.00 9591 1.00
p_predicted_intervention[919] 0.99 1.00 9647 1.00
p_predicted_intervention[920] 0.98 1.00 9591 1.00
p_predicted_intervention[921] 0.98 1.00 9573 1.00
p_predicted_intervention[922] 0.99 1.00 9640 1.00
p_predicted_intervention[923] 0.98 1.00 9573 1.00
p_predicted_intervention[924] 0.00 0.00 8318 1.00
p_predicted_intervention[925] 0.00 0.00 9192 1.00
p_predicted_intervention[926] 0.00 0.00 9197 1.00
p_predicted_intervention[927] 0.00 0.00 9180 1.00
p_predicted_intervention[928] 0.00 0.00 9324 1.00
p_predicted_intervention[929] 0.00 0.00 9274 1.00
p_predicted_intervention[930] 0.00 0.00 9366 1.00
p_predicted_intervention[931] 0.00 0.00 9611 1.00
p_predicted_intervention[932] 0.58 1.00 10687 1.00
p_predicted_intervention[933] 0.58 1.00 10687 1.00
p_predicted_intervention[934] 0.53 1.00 10613 1.00
p_predicted_intervention[935] 0.58 1.00 10686 1.00
p_predicted_intervention[936] 0.58 1.00 10686 1.00
p_predicted_intervention[937] 0.57 1.00 10685 1.00
p_predicted_intervention[938] 0.57 1.00 10684 1.00
p_predicted_intervention[939] 0.57 1.00 10679 1.00
p_predicted_intervention[940] 0.57 1.00 10681 1.00
p_predicted_intervention[941] 0.57 1.00 10679 1.00
p_predicted_intervention[942] 0.57 1.00 10677 1.00
p_predicted_intervention[943] 0.57 1.00 10675 1.00
p_predicted_intervention[944] 0.00 0.00 9290 1.00
p_predicted_intervention[945] 0.00 0.00 9319 1.00
p_predicted_intervention[946] 0.00 0.00 9080 1.00
p_predicted_intervention[947] 0.00 0.00 9313 1.00
p_predicted_intervention[948] 0.00 0.00 9311 1.00
p_predicted_intervention[949] 0.00 0.00 9341 1.00
p_predicted_intervention[950] 0.57 0.99 6691 1.00
p_predicted_intervention[951] 0.57 0.99 6691 1.00
p_predicted_intervention[952] 0.57 0.99 6691 1.00
p_predicted_intervention[953] 0.47 0.98 6929 1.00
p_predicted_intervention[954] 0.47 0.98 6929 1.00
p_predicted_intervention[955] 0.47 0.98 6929 1.00
p_predicted_intervention[956] 0.46 0.98 6933 1.00
p_predicted_intervention[957] 0.46 0.98 6933 1.00
p_predicted_intervention[958] 0.46 0.98 6933 1.00
p_predicted_intervention[959] 0.44 0.98 6940 1.00
p_predicted_intervention[960] 0.44 0.98 6940 1.00
p_predicted_intervention[961] 0.44 0.98 6940 1.00
p_predicted_intervention[962] 0.46 0.98 6938 1.00
p_predicted_intervention[963] 0.46 0.98 6938 1.00
p_predicted_intervention[964] 0.46 0.98 6938 1.00
p_predicted_intervention[965] 0.45 0.98 6947 1.00
p_predicted_intervention[966] 0.45 0.98 6947 1.00
p_predicted_intervention[967] 0.45 0.98 6947 1.00
p_predicted_intervention[968] 0.45 0.98 6947 1.00
p_predicted_intervention[969] 0.45 0.98 6947 1.00
p_predicted_intervention[970] 0.45 0.98 6947 1.00
p_predicted_intervention[971] 0.44 0.98 6929 1.00
p_predicted_intervention[972] 0.44 0.98 6929 1.00
p_predicted_intervention[973] 0.44 0.98 6929 1.00
p_predicted_intervention[974] 0.43 0.98 6936 1.00
p_predicted_intervention[975] 0.43 0.98 6936 1.00
p_predicted_intervention[976] 0.43 0.98 6936 1.00
p_predicted_intervention[977] 0.53 1.00 10614 1.00
p_predicted_intervention[978] 0.58 1.00 10687 1.00
p_predicted_intervention[979] 0.58 1.00 10687 1.00
p_predicted_intervention[980] 0.58 1.00 10687 1.00
p_predicted_intervention[981] 0.53 1.00 10613 1.00
p_predicted_intervention[982] 0.58 1.00 10686 1.00
p_predicted_intervention[983] 0.58 1.00 10685 1.00
p_predicted_intervention[984] 0.57 1.00 10685 1.00
p_predicted_intervention[985] 0.57 1.00 10679 1.00
p_predicted_intervention[986] 0.57 1.00 10678 1.00
p_predicted_intervention[987] 0.57 1.00 10679 1.00
p_predicted_intervention[988] 0.57 1.00 10681 1.00
p_predicted_intervention[989] 0.57 1.00 10679 1.00
p_predicted_intervention[990] 0.57 1.00 10677 1.00
p_predicted_intervention[991] 0.57 1.00 10675 1.00
p_predicted_intervention[992] 0.56 1.00 10693 1.00
p_predicted_intervention[993] 0.22 1.00 9658 1.00
p_predicted_intervention[994] 0.22 1.00 9614 1.00
p_predicted_intervention[995] 0.21 1.00 9625 1.00
p_predicted_intervention[996] 0.00 0.98 8598 1.00
p_predicted_intervention[997] 0.00 0.97 8683 1.00
p_predicted_intervention[998] 0.00 0.98 8621 1.00
p_predicted_intervention[999] 0.54 1.00 10641 1.00
p_predicted_intervention[1000] 0.54 1.00 10639 1.00
p_predicted_intervention[1001] 0.56 1.00 10696 1.00
p_predicted_intervention[1002] 0.56 1.00 10695 1.00
p_predicted_intervention[1003] 0.56 1.00 10694 1.00
p_predicted_intervention[1004] 0.98 1.00 9623 1.00
p_predicted_intervention[1005] 0.98 1.00 9655 1.00
p_predicted_intervention[1006] 0.98 1.00 9656 1.00
p_predicted_intervention[1007] 0.99 1.00 9583 1.00
p_predicted_intervention[1008] 0.99 1.00 9570 1.00
p_predicted_intervention[1009] 0.97 1.00 9530 1.00
p_predicted_intervention[1010] 0.96 1.00 9561 1.00
p_predicted_intervention[1011] 0.97 1.00 9483 1.00
p_predicted_intervention[1012] 0.49 1.00 10066 1.00
p_predicted_intervention[1013] 0.46 1.00 9985 1.00
p_predicted_intervention[1014] 0.44 1.00 9943 1.00
p_predicted_intervention[1015] 0.48 1.00 10034 1.00
p_predicted_intervention[1016] 0.49 1.00 10055 1.00
p_predicted_intervention[1017] 0.46 1.00 9967 1.00
p_predicted_intervention[1018] 0.45 1.00 9957 1.00
p_predicted_intervention[1019] 0.45 1.00 9949 1.00
p_predicted_intervention[1020] 0.37 1.00 9801 1.00
p_predicted_intervention[1021] 0.41 1.00 9867 1.00
p_predicted_intervention[1022] 0.40 1.00 9844 1.00
p_predicted_intervention[1023] 0.40 1.00 9834 1.00
p_predicted_intervention[1024] 0.39 1.00 9820 1.00
p_predicted_intervention[1025] 0.37 1.00 9808 1.00
p_predicted_intervention[1026] 0.37 1.00 9804 1.00
p_predicted_intervention[1027] 0.48 1.00 10031 1.00
p_predicted_intervention[1028] 0.49 1.00 10062 1.00
p_predicted_intervention[1029] 0.48 1.00 10011 1.00
p_predicted_intervention[1030] 0.45 1.00 9954 1.00
p_predicted_intervention[1031] 0.45 1.00 9951 1.00
p_predicted_intervention[1032] 0.42 1.00 9876 1.00
p_predicted_intervention[1033] 0.41 1.00 9856 1.00
p_predicted_intervention[1034] 0.41 1.00 9851 1.00
p_predicted_intervention[1035] 0.40 1.00 9836 1.00
p_predicted_intervention[1036] 0.39 1.00 9824 1.00
p_predicted_intervention[1037] 0.37 1.00 9811 1.00
p_predicted_intervention[1038] 0.63 1.00 7624 1.00
p_predicted_intervention[1039] 0.60 1.00 7787 1.00
p_predicted_intervention[1040] 0.62 1.00 7630 1.00
p_predicted_intervention[1041] 0.59 1.00 7790 1.00
p_predicted_intervention[1042] 0.62 1.00 7634 1.00
p_predicted_intervention[1043] 0.59 1.00 7795 1.00
p_predicted_intervention[1044] 0.62 1.00 7634 1.00
p_predicted_intervention[1045] 0.58 1.00 7796 1.00
p_predicted_intervention[1046] 0.62 1.00 7635 1.00
p_predicted_intervention[1047] 0.58 1.00 7797 1.00
p_predicted_intervention[1048] 0.62 1.00 7643 1.00
p_predicted_intervention[1049] 0.58 1.00 7804 1.00
p_predicted_intervention[1050] 0.53 1.00 7537 1.00
p_predicted_intervention[1051] 0.49 0.99 7788 1.00
p_predicted_intervention[1052] 1.00 1.00 8233 1.00
p_predicted_intervention[1053] 1.00 1.00 8233 1.00
p_predicted_intervention[1054] 1.00 1.00 8187 1.00
p_predicted_intervention[1055] 1.00 1.00 8187 1.00
p_predicted_intervention[1056] 1.00 1.00 8156 1.00
p_predicted_intervention[1057] 1.00 1.00 8156 1.00
p_predicted_intervention[1058] 1.00 1.00 8144 1.00
p_predicted_intervention[1059] 1.00 1.00 8144 1.00
p_predicted_intervention[1060] 1.00 1.00 8195 1.00
p_predicted_intervention[1061] 1.00 1.00 8195 1.00
p_predicted_intervention[1062] 1.00 1.00 8227 1.00
p_predicted_intervention[1063] 1.00 1.00 8199 1.00
p_predicted_intervention[1064] 1.00 1.00 8192 1.00
p_predicted_intervention[1065] 1.00 1.00 8157 1.00
p_predicted_intervention[1066] 1.00 1.00 8150 1.00
p_predicted_intervention[1067] 1.00 1.00 8173 1.00
p_predicted_intervention[1068] 1.00 1.00 8160 1.00
p_predicted_intervention[1069] 1.00 1.00 8212 1.00
p_predicted_intervention[1070] 1.00 1.00 8191 1.00
p_predicted_intervention[1071] 0.65 1.00 10329 1.00
p_predicted_intervention[1072] 0.65 1.00 10290 1.00
p_predicted_intervention[1073] 0.66 1.00 10286 1.00
p_predicted_intervention[1074] 0.52 0.98 7242 1.00
p_predicted_intervention[1075] 0.52 0.98 7242 1.00
p_predicted_intervention[1076] 0.60 0.99 7086 1.00
p_predicted_intervention[1077] 0.60 0.99 7086 1.00
p_predicted_intervention[1078] 0.51 0.98 7241 1.00
p_predicted_intervention[1079] 0.51 0.98 7241 1.00
p_predicted_intervention[1080] 0.51 0.98 7240 1.00
p_predicted_intervention[1081] 0.51 0.98 7240 1.00
p_predicted_intervention[1082] 0.50 0.98 7239 1.00
p_predicted_intervention[1083] 0.50 0.98 7239 1.00
p_predicted_intervention[1084] 0.38 0.56 1635 1.00
p_predicted_intervention[1085] 0.31 0.52 1477 1.00
p_predicted_intervention[1086] 0.35 0.54 1891 1.00
p_predicted_intervention[1087] 0.34 0.53 1921 1.00
p_predicted_intervention[1088] 0.32 0.50 1987 1.00
p_predicted_intervention[1089] 0.28 0.47 2069 1.00
p_predicted_intervention[1090] 0.27 0.47 2080 1.00
p_predicted_intervention[1091] 0.61 0.99 7027 1.00
p_predicted_intervention[1092] 0.60 0.99 7029 1.00
p_predicted_intervention[1093] 0.61 0.99 7026 1.00
p_predicted_intervention[1094] 0.60 0.99 7030 1.00
p_predicted_intervention[1095] 0.59 0.99 7047 1.00
p_predicted_intervention[1096] 0.60 0.99 7034 1.00
p_predicted_intervention[1097] 0.59 0.99 7047 1.00
p_predicted_intervention[1098] 0.59 0.99 7036 1.00
p_predicted_intervention[1099] 0.58 0.99 7049 1.00
p_predicted_intervention[1100] 0.58 0.99 7049 1.00
p_predicted_intervention[1101] 0.58 0.99 7052 1.00
p_predicted_intervention[1102] 0.57 0.99 7019 1.00
p_predicted_intervention[1103] 0.48 0.99 7151 1.00
p_predicted_intervention[1104] 0.47 0.99 7136 1.00
p_predicted_intervention[1105] 0.47 0.99 7107 1.00
p_predicted_intervention[1106] 0.00 0.00 9352 1.00
p_predicted_intervention[1107] 0.00 0.00 9355 1.00
p_predicted_intervention[1108] 0.00 0.00 9384 1.00
p_predicted_intervention[1109] 0.00 0.00 8855 1.00
p_predicted_intervention[1110] 0.00 0.00 8999 1.00
p_predicted_intervention[1111] 0.00 0.00 9486 1.00
p_predicted_intervention[1112] 0.00 0.00 9526 1.00
p_predicted_intervention[1113] 0.00 0.00 9528 1.00
p_predicted_intervention[1114] 0.00 0.00 9237 1.00
p_predicted_intervention[1115] 0.00 0.00 9131 1.00
p_predicted_intervention[1116] 0.33 0.51 1387 1.00
p_predicted_intervention[1117] 0.32 0.50 1385 1.00
p_predicted_intervention[1118] 0.30 0.47 1381 1.00
p_predicted_intervention[1119] 0.35 0.52 1944 1.00
p_predicted_intervention[1120] 0.35 0.51 1958 1.00
p_predicted_intervention[1121] 0.33 0.49 2009 1.00
p_predicted_intervention[1122] 0.32 0.49 2022 1.00
p_predicted_intervention[1123] 0.34 0.50 1988 1.00
p_predicted_intervention[1124] 0.33 0.49 2013 1.00
p_predicted_intervention[1125] 0.34 0.50 1980 1.00
p_predicted_intervention[1126] 0.32 0.48 2034 1.00
p_predicted_intervention[1127] 0.49 0.98 6932 1.00
p_predicted_intervention[1128] 0.57 0.99 6690 1.00
p_predicted_intervention[1129] 0.56 0.99 6699 1.00
p_predicted_intervention[1130] 0.45 0.98 6933 1.00
p_predicted_intervention[1131] 0.44 0.98 6943 1.00
p_predicted_intervention[1132] 0.00 0.51 8395 1.00
p_predicted_intervention[1133] 0.22 1.00 9558 1.00
p_predicted_intervention[1134] 0.20 1.00 9550 1.00
p_predicted_intervention[1135] 0.97 1.00 9495 1.00
p_predicted_intervention[1136] 0.99 1.00 9610 1.00
p_predicted_intervention[1137] 0.97 1.00 9495 1.00
p_predicted_intervention[1138] 0.98 1.00 9478 1.00
p_predicted_intervention[1139] 0.99 1.00 9574 1.00
p_predicted_intervention[1140] 0.98 1.00 9478 1.00
p_predicted_intervention[1141] 0.97 1.00 9472 1.00
p_predicted_intervention[1142] 0.99 1.00 9587 1.00
p_predicted_intervention[1143] 0.97 1.00 9472 1.00
p_predicted_intervention[1144] 0.98 1.00 9470 1.00
p_predicted_intervention[1145] 0.99 1.00 9573 1.00
p_predicted_intervention[1146] 0.98 1.00 9470 1.00
p_predicted_intervention[1147] 0.97 1.00 9606 1.00
p_predicted_intervention[1148] 0.99 1.00 9675 1.00
p_predicted_intervention[1149] 0.97 1.00 9606 1.00
p_predicted_intervention[1150] 0.97 1.00 9541 1.00
p_predicted_intervention[1151] 0.99 1.00 9605 1.00
p_predicted_intervention[1152] 0.97 1.00 9541 1.00
p_predicted_intervention[1153] 0.97 1.00 9545 1.00
p_predicted_intervention[1154] 0.99 1.00 9614 1.00
p_predicted_intervention[1155] 0.97 1.00 9545 1.00
p_predicted_intervention[1156] 0.97 1.00 9514 1.00
p_predicted_intervention[1157] 0.99 1.00 9587 1.00
p_predicted_intervention[1158] 0.97 1.00 9514 1.00
p_predicted_intervention[1159] 1.00 1.00 8242 1.00
p_predicted_intervention[1160] 1.00 1.00 8169 1.00
p_predicted_intervention[1161] 1.00 1.00 8166 1.00
p_predicted_intervention[1162] 1.00 1.00 8136 1.00
p_predicted_intervention[1163] 1.00 1.00 8170 1.00
p_predicted_intervention[1164] 1.00 1.00 8134 1.00
p_predicted_intervention[1165] 0.97 1.00 9629 1.00
p_predicted_intervention[1166] 0.99 1.00 9692 1.00
p_predicted_intervention[1167] 0.97 1.00 9629 1.00
p_predicted_intervention[1168] 0.97 1.00 9569 1.00
p_predicted_intervention[1169] 0.99 1.00 9627 1.00
p_predicted_intervention[1170] 0.97 1.00 9569 1.00
p_predicted_intervention[1171] 0.97 1.00 9550 1.00
p_predicted_intervention[1172] 0.99 1.00 9613 1.00
p_predicted_intervention[1173] 0.97 1.00 9550 1.00
p_predicted_intervention[1174] 0.00 0.98 8464 1.00
p_predicted_intervention[1175] 0.00 0.98 8492 1.00
p_predicted_intervention[1176] 0.00 0.98 8492 1.00
p_predicted_intervention[1177] 0.00 0.98 8491 1.00
p_predicted_intervention[1178] 0.00 0.97 8430 1.00
p_predicted_intervention[1179] 0.35 0.59 1770 1.00
p_predicted_intervention[1180] 0.28 0.54 1701 1.00
p_predicted_intervention[1181] 0.28 0.52 2075 1.00
p_predicted_intervention[1182] 0.21 0.45 1788 1.00
p_predicted_intervention[1183] 0.26 0.49 2144 1.00
p_predicted_intervention[1184] 0.22 0.45 2256 1.00
p_predicted_intervention[1185] 0.20 0.43 2304 1.00
p_predicted_intervention[1186] 0.00 0.97 8532 1.00
p_predicted_intervention[1187] 0.00 0.96 8540 1.00
p_predicted_intervention[1188] 0.00 0.96 8541 1.00
p_predicted_intervention[1189] 0.49 1.00 10062 1.00
p_predicted_intervention[1190] 0.45 1.00 9960 1.00
p_predicted_intervention[1191] 0.44 1.00 9939 1.00
p_predicted_intervention[1192] 0.44 1.00 9929 1.00
p_predicted_intervention[1193] 0.40 1.00 9828 1.00
p_predicted_intervention[1194] 0.49 1.00 10063 1.00
p_predicted_intervention[1195] 0.45 1.00 9961 1.00
p_predicted_intervention[1196] 0.45 1.00 9951 1.00
p_predicted_intervention[1197] 0.44 1.00 9939 1.00
p_predicted_intervention[1198] 0.40 1.00 9848 1.00
p_predicted_intervention[1199] 0.97 1.00 6847 1.00
p_predicted_intervention[1200] 0.97 1.00 6846 1.00
p_predicted_intervention[1201] 0.96 1.00 6976 1.00
p_predicted_intervention[1202] 0.96 1.00 6849 1.00
p_predicted_intervention[1203] 0.96 1.00 6847 1.00
p_predicted_intervention[1204] 0.96 1.00 6847 1.00
p_predicted_intervention[1205] 0.96 1.00 6847 1.00
p_predicted_intervention[1206] 0.96 1.00 6847 1.00
p_predicted_intervention[1207] 0.95 1.00 6983 1.00
p_predicted_intervention[1208] 0.95 1.00 6983 1.00
p_predicted_intervention[1209] 0.21 1.00 9697 1.00
p_predicted_intervention[1210] 0.21 1.00 9634 1.00
p_predicted_intervention[1211] 0.48 1.00 10062 1.00
p_predicted_intervention[1212] 0.48 1.00 10065 1.00
p_predicted_intervention[1213] 0.50 1.00 10097 1.00
p_predicted_intervention[1214] 0.45 1.00 9990 1.00
p_predicted_intervention[1215] 0.45 1.00 9964 1.00
p_predicted_intervention[1216] 0.39 1.00 9872 1.00
p_predicted_intervention[1217] 0.54 0.98 7218 1.00
p_predicted_intervention[1218] 0.53 0.98 7219 1.00
p_predicted_intervention[1219] 0.50 0.98 7225 1.00
p_predicted_intervention[1220] 0.50 0.98 7232 1.00
p_predicted_intervention[1221] 0.49 0.98 7233 1.00
p_predicted_intervention[1222] 0.00 0.46 8347 1.00
p_predicted_intervention[1223] 0.00 0.45 8326 1.00
p_predicted_intervention[1224] 0.00 0.44 8314 1.00
p_predicted_intervention[1225] 0.00 0.47 8358 1.00
p_predicted_intervention[1226] 0.00 0.47 8356 1.00
p_predicted_intervention[1227] 0.00 0.47 8358 1.00
p_predicted_intervention[1228] 0.00 0.47 8356 1.00
p_predicted_intervention[1229] 0.00 1.00 7690 1.00
p_predicted_intervention[1230] 0.00 1.00 7648 1.00
p_predicted_intervention[1231] 0.00 1.00 7653 1.00
p_predicted_intervention[1232] 0.00 1.00 7643 1.00
p_predicted_intervention[1233] 0.35 0.50 1268 1.00
p_predicted_intervention[1234] 0.35 0.50 1268 1.00
p_predicted_intervention[1235] 0.33 0.47 1255 1.00
p_predicted_intervention[1236] 0.33 0.47 1255 1.00
p_predicted_intervention[1237] 0.31 0.45 1247 1.00
p_predicted_intervention[1238] 0.31 0.45 1247 1.00
p_predicted_intervention[1239] 0.38 0.52 2042 1.00
p_predicted_intervention[1240] 0.38 0.52 2042 1.00
p_predicted_intervention[1241] 0.36 0.49 2143 1.00
p_predicted_intervention[1242] 0.36 0.49 2143 1.00
p_predicted_intervention[1243] 0.34 0.48 2185 1.00
p_predicted_intervention[1244] 0.34 0.48 2185 1.00
p_predicted_intervention[1245] 0.34 0.48 2178 1.00
p_predicted_intervention[1246] 0.34 0.48 2178 1.00
p_predicted_intervention[1247] 0.33 0.47 2188 1.00
p_predicted_intervention[1248] 0.33 0.47 2188 1.00
p_predicted_intervention[1249] 0.96 1.00 9502 1.00
p_predicted_intervention[1250] 0.98 1.00 9583 1.00
p_predicted_intervention[1251] 0.96 1.00 9502 1.00
p_predicted_intervention[1252] 0.96 1.00 9523 1.00
p_predicted_intervention[1253] 0.98 1.00 9611 1.00
p_predicted_intervention[1254] 0.96 1.00 9523 1.00
p_predicted_intervention[1255] 0.96 1.00 9518 1.00
p_predicted_intervention[1256] 0.98 1.00 9607 1.00
p_predicted_intervention[1257] 0.96 1.00 9518 1.00
p_predicted_intervention[1258] 0.97 1.00 9512 1.00
p_predicted_intervention[1259] 0.98 1.00 9601 1.00
p_predicted_intervention[1260] 0.97 1.00 9512 1.00
p_predicted_intervention[1261] 0.96 1.00 9500 1.00
p_predicted_intervention[1262] 0.98 1.00 9592 1.00
p_predicted_intervention[1263] 0.96 1.00 9500 1.00
p_predicted_intervention[1264] 0.97 1.00 9470 1.00
p_predicted_intervention[1265] 0.98 1.00 9550 1.00
p_predicted_intervention[1266] 0.97 1.00 9470 1.00
p_predicted_intervention[1267] 1.00 1.00 8002 1.00
p_predicted_intervention[1268] 1.00 1.00 7998 1.00
p_predicted_intervention[1269] 1.00 1.00 7986 1.00
p_predicted_intervention[1270] 1.00 1.00 7979 1.00
p_predicted_intervention[1271] 1.00 1.00 7971 1.00
p_predicted_intervention[1272] 0.97 1.00 9455 1.00
p_predicted_intervention[1273] 0.97 1.00 9415 1.00
p_predicted_intervention[1274] 1.00 1.00 8212 1.00
p_predicted_intervention[1275] 1.00 1.00 8169 1.00
p_predicted_intervention[1276] 1.00 1.00 8154 1.00
p_predicted_intervention[1277] 1.00 1.00 8186 1.00
p_predicted_intervention[1278] 0.66 1.00 7337 1.00
p_predicted_intervention[1279] 0.66 1.00 7337 1.00
p_predicted_intervention[1280] 0.54 1.00 7395 1.00
p_predicted_intervention[1281] 0.54 1.00 7395 1.00
p_predicted_intervention[1282] 1.00 1.00 10009 1.00
p_predicted_intervention[1283] 1.00 1.00 10009 1.00
p_predicted_intervention[1284] 1.00 1.00 10009 1.00
p_predicted_intervention[1285] 1.00 1.00 10009 1.00
p_predicted_intervention[1286] 1.00 1.00 10009 1.00
p_predicted_intervention[1287] 1.00 1.00 10009 1.00
p_predicted_intervention[1288] 1.00 1.00 10009 1.00
p_predicted_intervention[1289] 1.00 1.00 10009 1.00
p_predicted_intervention[1290] 1.00 1.00 10009 1.00
p_predicted_intervention[1291] 1.00 1.00 10009 1.00
p_predicted_intervention[1292] 1.00 1.00 10009 1.00
p_predicted_intervention[1293] 1.00 1.00 8165 1.00
p_predicted_intervention[1294] 1.00 1.00 8146 1.00
p_predicted_intervention[1295] 1.00 1.00 8182 1.00
p_predicted_intervention[1296] 1.00 1.00 8131 1.00
p_predicted_intervention[1297] 0.98 1.00 9616 1.00
p_predicted_intervention[1298] 0.98 1.00 9647 1.00
p_predicted_intervention[1299] 0.98 1.00 9612 1.00
p_predicted_intervention[1300] 0.98 1.00 9607 1.00
p_predicted_intervention[1301] 0.99 1.00 9559 1.00
p_predicted_intervention[1302] 0.50 0.98 7063 1.00
p_predicted_intervention[1303] 0.50 0.98 7063 1.00
p_predicted_intervention[1304] 0.56 0.99 6843 1.00
p_predicted_intervention[1305] 0.56 0.99 6843 1.00
p_predicted_intervention[1306] 0.57 0.99 6819 1.00
p_predicted_intervention[1307] 0.57 0.99 6819 1.00
p_predicted_intervention[1308] 0.46 0.98 7056 1.00
p_predicted_intervention[1309] 0.46 0.98 7056 1.00
p_predicted_intervention[1310] 0.47 0.98 7017 1.00
p_predicted_intervention[1311] 0.47 0.98 7017 1.00
p_predicted_intervention[1312] 0.45 0.98 7001 1.00
p_predicted_intervention[1313] 0.45 0.98 7001 1.00
p_predicted_intervention[1314] 0.45 0.98 7006 1.00
p_predicted_intervention[1315] 0.45 0.98 7006 1.00
p_predicted_intervention[1316] 1.00 1.00 8137 1.00
p_predicted_intervention[1317] 1.00 1.00 8076 1.00
p_predicted_intervention[1318] 1.00 1.00 8069 1.00
p_predicted_intervention[1319] 1.00 1.00 8049 1.00
p_predicted_intervention[1320] 1.00 1.00 8072 1.00
p_predicted_intervention[1321] 1.00 1.00 8064 1.00
p_predicted_intervention[1322] 1.00 1.00 8041 1.00
p_predicted_intervention[1323] 1.00 1.00 8035 1.00
p_predicted_intervention[1324] 1.00 1.00 8137 1.00
p_predicted_intervention[1325] 1.00 1.00 8076 1.00
p_predicted_intervention[1326] 1.00 1.00 8069 1.00
p_predicted_intervention[1327] 1.00 1.00 8049 1.00
p_predicted_intervention[1328] 1.00 1.00 8072 1.00
p_predicted_intervention[1329] 1.00 1.00 8064 1.00
p_predicted_intervention[1330] 1.00 1.00 8035 1.00
p_predicted_intervention[1331] 0.61 0.99 7008 1.00
p_predicted_intervention[1332] 0.51 0.98 7225 1.00
p_predicted_intervention[1333] 0.49 0.98 7234 1.00
p_predicted_intervention[1334] 1.00 1.00 8243 1.00
p_predicted_intervention[1335] 1.00 1.00 8242 1.00
p_predicted_intervention[1336] 1.00 1.00 8168 1.00
p_predicted_intervention[1337] 1.00 1.00 8154 1.00
p_predicted_intervention[1338] 1.00 1.00 8152 1.00
p_predicted_intervention[1339] 1.00 1.00 8150 1.00
p_predicted_intervention[1340] 1.00 1.00 8148 1.00
p_predicted_intervention[1341] 1.00 1.00 8148 1.00
p_predicted_intervention[1342] 1.00 1.00 8143 1.00
p_predicted_intervention[1343] 1.00 1.00 8168 1.00
predicted_difference[1] -0.16 0.25 8521 1.00
predicted_difference[2] -0.16 0.26 8476 1.00
predicted_difference[3] -0.16 0.26 8451 1.00
predicted_difference[4] -0.14 0.25 7870 1.00
predicted_difference[5] -0.14 0.25 7872 1.00
predicted_difference[6] -0.14 0.25 7868 1.00
predicted_difference[7] -0.12 0.23 7719 1.00
predicted_difference[8] -0.12 0.23 7723 1.00
predicted_difference[9] -0.32 -0.23 5223 1.00
predicted_difference[10] -0.34 -0.25 5954 1.00
predicted_difference[11] 1.00 1.00 6134 1.00
predicted_difference[12] 1.00 1.00 5872 1.00
predicted_difference[13] 1.00 1.00 6170 1.00
predicted_difference[14] 1.00 1.00 6196 1.00
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predicted_difference[1182] 0.15 0.39 1816 1.00
predicted_difference[1183] 0.21 0.45 2159 1.00
predicted_difference[1184] 0.18 0.41 2245 1.00
predicted_difference[1185] 0.16 0.39 2294 1.00
predicted_difference[1186] -0.33 0.18 8608 1.00
predicted_difference[1187] -0.29 0.17 8208 1.00
predicted_difference[1188] -0.30 0.17 8262 1.00
predicted_difference[1189] 0.43 0.98 10180 1.00
predicted_difference[1190] 0.41 0.98 9992 1.00
predicted_difference[1191] 0.41 0.98 9977 1.00
predicted_difference[1192] 0.40 0.98 9969 1.00
predicted_difference[1193] 0.36 0.99 9876 1.00
predicted_difference[1194] 0.43 0.98 10181 1.00
predicted_difference[1195] 0.41 0.98 9993 1.00
predicted_difference[1196] 0.41 0.98 9986 1.00
predicted_difference[1197] 0.41 0.98 9977 1.00
predicted_difference[1198] 0.37 0.99 9897 1.00
predicted_difference[1199] 0.36 0.61 7543 1.00
predicted_difference[1200] 0.36 0.61 7543 1.00
predicted_difference[1201] 0.28 0.57 7581 1.00
predicted_difference[1202] 0.37 0.62 7566 1.00
predicted_difference[1203] 0.37 0.62 7614 1.00
predicted_difference[1204] 0.37 0.62 7617 1.00
predicted_difference[1205] 0.38 0.62 7619 1.00
predicted_difference[1206] 0.37 0.62 7607 1.00
predicted_difference[1207] 0.41 0.67 8086 1.00
predicted_difference[1208] 0.41 0.68 8176 1.00
predicted_difference[1209] 0.20 1.00 9654 1.00
predicted_difference[1210] 0.21 1.00 9602 1.00
predicted_difference[1211] 0.40 0.95 9731 1.00
predicted_difference[1212] 0.40 0.96 9731 1.00
predicted_difference[1213] 0.42 0.95 9850 1.00
predicted_difference[1214] 0.40 0.96 9741 1.00
predicted_difference[1215] 0.39 0.96 9729 1.00
predicted_difference[1216] 0.35 0.97 9616 1.00
predicted_difference[1217] 0.27 0.76 7243 1.00
predicted_difference[1218] 0.27 0.76 7231 1.00
predicted_difference[1219] 0.29 0.78 7523 1.00
predicted_difference[1220] 0.28 0.78 7527 1.00
predicted_difference[1221] 0.28 0.79 7517 1.00
predicted_difference[1222] -0.04 0.29 7817 1.00
predicted_difference[1223] -0.03 0.26 7864 1.00
predicted_difference[1224] -0.03 0.26 7866 1.00
predicted_difference[1225] -0.02 0.33 7862 1.00
predicted_difference[1226] -0.02 0.33 7862 1.00
predicted_difference[1227] -0.02 0.33 7862 1.00
predicted_difference[1228] -0.02 0.33 7862 1.00
predicted_difference[1229] -0.45 0.40 7659 1.00
predicted_difference[1230] -0.48 0.36 7457 1.00
predicted_difference[1231] -0.39 0.47 7071 1.00
predicted_difference[1232] -0.38 0.49 7104 1.00
predicted_difference[1233] 0.04 0.18 1393 1.00
predicted_difference[1234] 0.04 0.18 1393 1.00
predicted_difference[1235] 0.04 0.17 1390 1.00
predicted_difference[1236] 0.04 0.17 1390 1.00
predicted_difference[1237] 0.04 0.17 1388 1.00
predicted_difference[1238] 0.04 0.17 1388 1.00
predicted_difference[1239] 0.14 0.26 2608 1.00
predicted_difference[1240] 0.14 0.26 2608 1.00
predicted_difference[1241] 0.13 0.26 2627 1.00
predicted_difference[1242] 0.13 0.26 2627 1.00
predicted_difference[1243] 0.13 0.25 2640 1.00
predicted_difference[1244] 0.13 0.25 2640 1.00
predicted_difference[1245] 0.13 0.25 2637 1.00
predicted_difference[1246] 0.13 0.25 2637 1.00
predicted_difference[1247] 0.13 0.25 2641 1.00
predicted_difference[1248] 0.13 0.25 2641 1.00
predicted_difference[1249] 0.74 0.89 9495 1.00
predicted_difference[1250] 0.83 0.94 9600 1.00
predicted_difference[1251] 0.74 0.89 9495 1.00
predicted_difference[1252] 0.72 0.85 9465 1.00
predicted_difference[1253] 0.82 0.92 9585 1.00
predicted_difference[1254] 0.72 0.85 9465 1.00
predicted_difference[1255] 0.72 0.85 9474 1.00
predicted_difference[1256] 0.82 0.92 9589 1.00
predicted_difference[1257] 0.72 0.85 9474 1.00
predicted_difference[1258] 0.71 0.85 9486 1.00
predicted_difference[1259] 0.82 0.92 9594 1.00
predicted_difference[1260] 0.71 0.85 9486 1.00
predicted_difference[1261] 0.67 0.82 9385 1.00
predicted_difference[1262] 0.78 0.90 9521 1.00
predicted_difference[1263] 0.67 0.82 9385 1.00
predicted_difference[1264] 0.72 0.86 9571 1.00
predicted_difference[1265] 0.82 0.92 9653 1.00
predicted_difference[1266] 0.72 0.86 9571 1.00
predicted_difference[1267] 0.98 1.00 8413 1.00
predicted_difference[1268] 0.98 1.00 8410 1.00
predicted_difference[1269] 0.99 1.00 8395 1.00
predicted_difference[1270] 0.99 1.00 8388 1.00
predicted_difference[1271] 0.99 1.00 8234 1.00
predicted_difference[1272] 0.83 0.94 9682 1.00
predicted_difference[1273] 0.85 0.95 9701 1.00
predicted_difference[1274] 0.99 1.00 7970 1.00
predicted_difference[1275] 0.99 1.00 8003 1.00
predicted_difference[1276] 0.99 1.00 8004 1.00
predicted_difference[1277] 0.99 1.00 7962 1.00
predicted_difference[1278] 0.58 0.95 7146 1.00
predicted_difference[1279] 0.58 0.95 7146 1.00
predicted_difference[1280] 0.49 0.96 7331 1.00
predicted_difference[1281] 0.49 0.96 7331 1.00
predicted_difference[1282] 1.00 1.00 5491 1.00
predicted_difference[1283] 1.00 1.00 6282 1.00
predicted_difference[1284] 1.00 1.00 6364 1.00
predicted_difference[1285] 1.00 1.00 6382 1.00
predicted_difference[1286] 1.00 1.00 6130 1.00
predicted_difference[1287] 1.00 1.00 6123 1.00
predicted_difference[1288] 1.00 1.00 6127 1.00
predicted_difference[1289] 1.00 1.00 6163 1.00
predicted_difference[1290] 1.00 1.00 6406 1.00
predicted_difference[1291] 1.00 1.00 6408 1.00
predicted_difference[1292] 1.00 1.00 6418 1.00
predicted_difference[1293] 0.99 1.00 7914 1.00
predicted_difference[1294] 0.99 1.00 7923 1.00
predicted_difference[1295] 0.99 1.00 7862 1.00
predicted_difference[1296] 0.99 1.00 7878 1.00
predicted_difference[1297] 0.86 0.96 9510 1.00
predicted_difference[1298] 0.84 0.94 9444 1.00
predicted_difference[1299] 0.77 0.91 9152 1.00
predicted_difference[1300] 0.76 0.90 9162 1.00
predicted_difference[1301] 0.80 0.93 9379 1.00
predicted_difference[1302] 0.13 0.64 6030 1.00
predicted_difference[1303] 0.13 0.64 6030 1.00
predicted_difference[1304] 0.27 0.69 6343 1.00
predicted_difference[1305] 0.27 0.69 6343 1.00
predicted_difference[1306] 0.27 0.68 6343 1.00
predicted_difference[1307] 0.27 0.68 6343 1.00
predicted_difference[1308] 0.22 0.72 7116 1.00
predicted_difference[1309] 0.22 0.72 7116 1.00
predicted_difference[1310] 0.23 0.72 7022 1.00
predicted_difference[1311] 0.23 0.72 7022 1.00
predicted_difference[1312] 0.23 0.74 6925 1.00
predicted_difference[1313] 0.23 0.74 6925 1.00
predicted_difference[1314] 0.22 0.74 6922 1.00
predicted_difference[1315] 0.22 0.74 6922 1.00
predicted_difference[1316] 0.96 0.99 8915 1.00
predicted_difference[1317] 0.96 0.99 8761 1.00
predicted_difference[1318] 0.96 0.99 8742 1.00
predicted_difference[1319] 0.97 0.99 8708 1.00
predicted_difference[1320] 0.97 0.99 8641 1.00
predicted_difference[1321] 0.97 0.99 8627 1.00
predicted_difference[1322] 0.97 0.99 8612 1.00
predicted_difference[1323] 0.97 0.99 8609 1.00
predicted_difference[1324] 0.96 0.99 8915 1.00
predicted_difference[1325] 0.96 0.99 8761 1.00
predicted_difference[1326] 0.96 0.99 8742 1.00
predicted_difference[1327] 0.97 0.99 8708 1.00
predicted_difference[1328] 0.97 0.99 8640 1.00
predicted_difference[1329] 0.97 0.99 8627 1.00
predicted_difference[1330] 0.97 0.99 8609 1.00
predicted_difference[1331] 0.33 0.75 6936 1.00
predicted_difference[1332] 0.29 0.78 7544 1.00
predicted_difference[1333] 0.28 0.79 7511 1.00
predicted_difference[1334] 0.99 1.00 7939 1.00
predicted_difference[1335] 0.99 1.00 7942 1.00
predicted_difference[1336] 0.99 1.00 7913 1.00
predicted_difference[1337] 0.99 1.00 7915 1.00
predicted_difference[1338] 0.99 1.00 7917 1.00
predicted_difference[1339] 0.99 1.00 7920 1.00
predicted_difference[1340] 0.99 1.00 7921 1.00
predicted_difference[1341] 0.99 1.00 7921 1.00
predicted_difference[1342] 0.99 1.00 7925 1.00
predicted_difference[1343] 0.99 1.00 7896 1.00
lp__ -308.95 -262.21 562 1.00
Samples were drawn using NUTS(diag_e) at Sun Apr 21 04:22:05 2024.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).</code></pre>
</div>
</div>
<section id="investigating-parameter-distributions" class="level3">
<h3 class="anchored" data-anchor-id="investigating-parameter-distributions">Investigating parameter distributions</h3>
<div class="cell">
<div class="sourceCode cell-code" id="cb49"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a><span class="co">#g1 &lt;- group_mcmc_areas("beta",beta_list,fit,1)</span></span>
<span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-3"><a href="#cb49-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-4"><a href="#cb49-4" aria-hidden="true" tabindex="-1"></a>gx <span class="ot">&lt;-</span> <span class="fu">c</span>()</span>
<span id="cb49-5"><a href="#cb49-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-6"><a href="#cb49-6" aria-hidden="true" tabindex="-1"></a><span class="co">#grab parameters for every category with more than 8 observations</span></span>
<span id="cb49-7"><a href="#cb49-7" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> category_count<span class="sc">$</span>category_id[category_count<span class="sc">$</span>n <span class="sc">&gt;=</span> <span class="dv">8</span>]) {</span>
<span id="cb49-8"><a href="#cb49-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(i)</span>
<span id="cb49-9"><a href="#cb49-9" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb49-10"><a href="#cb49-10" aria-hidden="true" tabindex="-1"></a> <span class="co">#Print parameter distributions</span></span>
<span id="cb49-11"><a href="#cb49-11" aria-hidden="true" tabindex="-1"></a> gi <span class="ot">&lt;-</span> <span class="fu">group_mcmc_areas</span>(<span class="st">"beta"</span>,beta_list,fit,i) <span class="co">#add way to filter groups</span></span>
<span id="cb49-12"><a href="#cb49-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(</span>
<span id="cb49-13"><a href="#cb49-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"./Images/DirectEffects/Parameters/group_"</span>,i,<span class="st">"_"</span>,gi<span class="sc">$</span>name,<span class="st">".png"</span>)</span>
<span id="cb49-14"><a href="#cb49-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">plot=</span>gi<span class="sc">$</span>plot</span>
<span id="cb49-15"><a href="#cb49-15" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb49-16"><a href="#cb49-16" aria-hidden="true" tabindex="-1"></a> gx <span class="ot">&lt;-</span> <span class="fu">c</span>(gx,gi)</span>
<span id="cb49-17"><a href="#cb49-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-18"><a href="#cb49-18" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get Quantiles and means for parameters</span></span>
<span id="cb49-19"><a href="#cb49-19" aria-hidden="true" tabindex="-1"></a> table <span class="ot">&lt;-</span> <span class="fu">xtable</span>(gi<span class="sc">$</span>quantiles,</span>
<span id="cb49-20"><a href="#cb49-20" aria-hidden="true" tabindex="-1"></a> <span class="at">floating=</span><span class="cn">FALSE</span></span>
<span id="cb49-21"><a href="#cb49-21" aria-hidden="true" tabindex="-1"></a> ,<span class="at">latex.environments =</span> <span class="cn">NULL</span></span>
<span id="cb49-22"><a href="#cb49-22" aria-hidden="true" tabindex="-1"></a> ,<span class="at">booktabs =</span> <span class="cn">TRUE</span></span>
<span id="cb49-23"><a href="#cb49-23" aria-hidden="true" tabindex="-1"></a> ,<span class="at">zap=</span><span class="fu">getOption</span>(<span class="st">"digits"</span>)</span>
<span id="cb49-24"><a href="#cb49-24" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb49-25"><a href="#cb49-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">write_lines</span>(table,<span class="fu">paste0</span>(<span class="st">"./latex_output/DirectEffects/group_"</span>,gi<span class="sc">$</span>name,<span class="st">".tex"</span>))</span>
<span id="cb49-26"><a href="#cb49-26" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 1</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 2</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 4</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 5</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 6</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 7</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 11</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 12</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 13</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb68"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb68-1"><a href="#cb68-1" aria-hidden="true" tabindex="-1"></a>px <span class="ot">&lt;-</span> <span class="fu">c</span>()</span>
<span id="cb68-2"><a href="#cb68-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb68-3"><a href="#cb68-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb68-4"><a href="#cb68-4" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">9</span>,<span class="dv">10</span>,<span class="dv">11</span>,<span class="dv">12</span>)) {</span>
<span id="cb68-5"><a href="#cb68-5" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb68-6"><a href="#cb68-6" aria-hidden="true" tabindex="-1"></a> <span class="co">#Print parameter distributions</span></span>
<span id="cb68-7"><a href="#cb68-7" aria-hidden="true" tabindex="-1"></a> pi <span class="ot">&lt;-</span> <span class="fu">parameter_mcmc_areas</span>(<span class="st">"beta"</span>,beta_list,fit,i) <span class="co">#add way to filter groups</span></span>
<span id="cb68-8"><a href="#cb68-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(</span>
<span id="cb68-9"><a href="#cb68-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"./Images/DirectEffects/Parameters/parameters_"</span>,i,<span class="st">"_"</span>,pi<span class="sc">$</span>name,<span class="st">".png"</span>)</span>
<span id="cb68-10"><a href="#cb68-10" aria-hidden="true" tabindex="-1"></a> ,<span class="at">plot=</span>pi<span class="sc">$</span>plot</span>
<span id="cb68-11"><a href="#cb68-11" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb68-12"><a href="#cb68-12" aria-hidden="true" tabindex="-1"></a> px <span class="ot">&lt;-</span> <span class="fu">c</span>(px,pi)</span>
<span id="cb68-13"><a href="#cb68-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb68-14"><a href="#cb68-14" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get Quantiles and means for parameters</span></span>
<span id="cb68-15"><a href="#cb68-15" aria-hidden="true" tabindex="-1"></a> table <span class="ot">&lt;-</span> <span class="fu">xtable</span>(pi<span class="sc">$</span>quantiles,</span>
<span id="cb68-16"><a href="#cb68-16" aria-hidden="true" tabindex="-1"></a> <span class="at">floating=</span><span class="cn">FALSE</span></span>
<span id="cb68-17"><a href="#cb68-17" aria-hidden="true" tabindex="-1"></a> ,<span class="at">latex.environments =</span> <span class="cn">NULL</span></span>
<span id="cb68-18"><a href="#cb68-18" aria-hidden="true" tabindex="-1"></a> ,<span class="at">booktabs =</span> <span class="cn">TRUE</span></span>
<span id="cb68-19"><a href="#cb68-19" aria-hidden="true" tabindex="-1"></a> ,<span class="at">zap=</span><span class="fu">getOption</span>(<span class="st">"digits"</span>)</span>
<span id="cb68-20"><a href="#cb68-20" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb68-21"><a href="#cb68-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">write_lines</span>(table,<span class="fu">paste0</span>(<span class="st">"./latex_output/DirectEffects/parameters_"</span>,i,<span class="st">"_"</span>,pi<span class="sc">$</span>name,<span class="st">".tex"</span>))</span>
<span id="cb68-22"><a href="#cb68-22" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb68-23"><a href="#cb68-23" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image</code></pre>
</div>
</div>
<p>Note these have 95% outer CI and 80% inner (shaded)</p>
<pre><code>1) "Elapsed Duration",
2) "asinh(Generic Brands)",
3) "asinh(Competitors USPDC)",
4) "asinh(High SDI)",
5) "asinh(High-Medium SDI)",
6) "asinh(Medium SDI)",
7) "asinh(Low-Medium SDI)",
8) "asinh(Low SDI)",
9) "status_NYR",
10) "status_EBI",
11) "status_Rec",
12) "status_ANR",</code></pre>
<div class="cell">
<div class="sourceCode cell-code" id="cb71"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(px[<span class="dv">4</span>]<span class="sc">$</span>plot <span class="sc">+</span> px[<span class="dv">7</span>]<span class="sc">$</span>plot)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-13-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb72"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb72-1"><a href="#cb72-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
</section>
</section>
<section id="counterfactuals" class="level1">
<h1>Counterfactuals</h1>
<div class="cell">
<div class="sourceCode cell-code" id="cb74"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb74-1"><a href="#cb74-1" aria-hidden="true" tabindex="-1"></a>generated_ib <span class="ot">&lt;-</span> <span class="fu">gqs</span>(</span>
<span id="cb74-2"><a href="#cb74-2" aria-hidden="true" tabindex="-1"></a> fit<span class="sc">@</span>stanmodel,</span>
<span id="cb74-3"><a href="#cb74-3" aria-hidden="true" tabindex="-1"></a> <span class="at">data=</span>counterfact_marketing_ib,</span>
<span id="cb74-4"><a href="#cb74-4" aria-hidden="true" tabindex="-1"></a> <span class="at">draws=</span><span class="fu">as.matrix</span>(fit),</span>
<span id="cb74-5"><a href="#cb74-5" aria-hidden="true" tabindex="-1"></a> <span class="at">seed=</span><span class="dv">11021585</span></span>
<span id="cb74-6"><a href="#cb74-6" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb75"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" aria-hidden="true" tabindex="-1"></a>df_ib_p <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb75-2"><a href="#cb75-2" aria-hidden="true" tabindex="-1"></a> <span class="at">p_prior=</span><span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"p_prior"</span>)<span class="sc">$</span>p_prior)</span>
<span id="cb75-3"><a href="#cb75-3" aria-hidden="true" tabindex="-1"></a> ,<span class="at">p_predicted =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"p_predicted"</span>)<span class="sc">$</span>p_predicted)</span>
<span id="cb75-4"><a href="#cb75-4" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb75-5"><a href="#cb75-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb75-6"><a href="#cb75-6" aria-hidden="true" tabindex="-1"></a>df_ib_prior <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb75-7"><a href="#cb75-7" aria-hidden="true" tabindex="-1"></a> <span class="at">mu_prior =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"mu_prior"</span>)<span class="sc">$</span>mu_prior)</span>
<span id="cb75-8"><a href="#cb75-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">sigma_prior =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"sigma_prior"</span>)<span class="sc">$</span>sigma_prior)</span>
<span id="cb75-9"><a href="#cb75-9" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb75-10"><a href="#cb75-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb75-11"><a href="#cb75-11" aria-hidden="true" tabindex="-1"></a><span class="co">#p_prior</span></span>
<span id="cb75-12"><a href="#cb75-12" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df_ib_p, <span class="fu">aes</span>(<span class="at">x=</span>p_prior)) <span class="sc">+</span></span>
<span id="cb75-13"><a href="#cb75-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb75-14"><a href="#cb75-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb75-15"><a href="#cb75-15" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Implied Prior Distribution P"</span></span>
<span id="cb75-16"><a href="#cb75-16" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">""</span></span>
<span id="cb75-17"><a href="#cb75-17" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb75-18"><a href="#cb75-18" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb75-19"><a href="#cb75-19" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-15-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb76"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb76-1"><a href="#cb76-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/prior_p.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb78"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb78-1"><a href="#cb78-1" aria-hidden="true" tabindex="-1"></a><span class="co">#p_posterior</span></span>
<span id="cb78-2"><a href="#cb78-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df_ib_p, <span class="fu">aes</span>(<span class="at">x=</span>p_predicted)) <span class="sc">+</span></span>
<span id="cb78-3"><a href="#cb78-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb78-4"><a href="#cb78-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb78-5"><a href="#cb78-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Implied Posterior Distribution P"</span></span>
<span id="cb78-6"><a href="#cb78-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">""</span></span>
<span id="cb78-7"><a href="#cb78-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb78-8"><a href="#cb78-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb78-9"><a href="#cb78-9" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-15-2.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb79"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb79-1"><a href="#cb79-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/posterior_p.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb81"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb81-1"><a href="#cb81-1" aria-hidden="true" tabindex="-1"></a><span class="co">#mu_prior</span></span>
<span id="cb81-2"><a href="#cb81-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df_ib_prior) <span class="sc">+</span></span>
<span id="cb81-3"><a href="#cb81-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="fu">aes</span>(<span class="at">x=</span>mu_prior)) <span class="sc">+</span> </span>
<span id="cb81-4"><a href="#cb81-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb81-5"><a href="#cb81-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Prior - Mu"</span></span>
<span id="cb81-6"><a href="#cb81-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"same prior for all Mu values"</span></span>
<span id="cb81-7"><a href="#cb81-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Mu"</span></span>
<span id="cb81-8"><a href="#cb81-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability"</span></span>
<span id="cb81-9"><a href="#cb81-9" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-15-3.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb82"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb82-1"><a href="#cb82-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/prior_mu.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb84"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb84-1"><a href="#cb84-1" aria-hidden="true" tabindex="-1"></a><span class="co">#sigma_posterior</span></span>
<span id="cb84-2"><a href="#cb84-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df_ib_prior) <span class="sc">+</span></span>
<span id="cb84-3"><a href="#cb84-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="fu">aes</span>(<span class="at">x=</span>sigma_prior)) <span class="sc">+</span> </span>
<span id="cb84-4"><a href="#cb84-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb84-5"><a href="#cb84-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Prior - Sigma"</span></span>
<span id="cb84-6"><a href="#cb84-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"same prior for all Sigma values"</span></span>
<span id="cb84-7"><a href="#cb84-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Sigma"</span></span>
<span id="cb84-8"><a href="#cb84-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability"</span></span>
<span id="cb84-9"><a href="#cb84-9" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-15-4.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb85"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb85-1"><a href="#cb85-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/prior_sigma.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb87"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb87-1"><a href="#cb87-1" aria-hidden="true" tabindex="-1"></a><span class="fu">check_hmc_diagnostics</span>(fit)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Divergences:</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>0 of 10000 iterations ended with a divergence.</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>
Tree depth:</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>0 of 10000 iterations saturated the maximum tree depth of 10.</code></pre>
</div>
<div class="cell-output cell-output-stdout">
<pre><code>
Energy:</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>E-BFMI indicated possible pathological behavior:
Chain 2: E-BFMI = 0.184
Chain 4: E-BFMI = 0.192
E-BFMI below 0.2 indicates you may need to reparameterize your model.</code></pre>
</div>
</div>
<section id="intervention-alternatives" class="level3">
<h3 class="anchored" data-anchor-id="intervention-alternatives">Intervention: Alternatives</h3>
<section id="generic-alternative" class="level4">
<h4 class="anchored" data-anchor-id="generic-alternative">Generic Alternative</h4>
<div class="cell">
<div class="sourceCode cell-code" id="cb94"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb94-1"><a href="#cb94-1" aria-hidden="true" tabindex="-1"></a>counterfact_predicted_ib <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb94-2"><a href="#cb94-2" aria-hidden="true" tabindex="-1"></a> <span class="at">p_predicted_default =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"p_predicted_default"</span>)<span class="sc">$</span>p_predicted_default)</span>
<span id="cb94-3"><a href="#cb94-3" aria-hidden="true" tabindex="-1"></a> ,<span class="at">p_predicted_intervention =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"p_predicted_intervention"</span>)<span class="sc">$</span>p_predicted_intervention)</span>
<span id="cb94-4"><a href="#cb94-4" aria-hidden="true" tabindex="-1"></a> ,<span class="at">predicted_difference =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"predicted_difference"</span>)<span class="sc">$</span>predicted_difference)</span>
<span id="cb94-5"><a href="#cb94-5" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb94-6"><a href="#cb94-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb94-7"><a href="#cb94-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb94-8"><a href="#cb94-8" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(counterfact_predicted_ib, <span class="fu">aes</span>(<span class="at">x=</span>p_predicted_default)) <span class="sc">+</span></span>
<span id="cb94-9"><a href="#cb94-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb94-10"><a href="#cb94-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb94-11"><a href="#cb94-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of 'p'"</span></span>
<span id="cb94-12"><a href="#cb94-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: None"</span></span>
<span id="cb94-13"><a href="#cb94-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb94-14"><a href="#cb94-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb94-15"><a href="#cb94-15" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-17-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb95"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb95-1"><a href="#cb95-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/default_p_generic_intervention_base.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb97"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb97-1"><a href="#cb97-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(counterfact_predicted_ib, <span class="fu">aes</span>(<span class="at">x=</span>p_predicted_intervention)) <span class="sc">+</span></span>
<span id="cb97-2"><a href="#cb97-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb97-3"><a href="#cb97-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb97-4"><a href="#cb97-4" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of 'p'"</span></span>
<span id="cb97-5"><a href="#cb97-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: Add a single generic competitor"</span></span>
<span id="cb97-6"><a href="#cb97-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb97-7"><a href="#cb97-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb97-8"><a href="#cb97-8" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-17-2.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb98"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb98-1"><a href="#cb98-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/default_p_generic_intervention_interv.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb100"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb100-1"><a href="#cb100-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(counterfact_predicted_ib, <span class="fu">aes</span>(<span class="at">x=</span>predicted_difference)) <span class="sc">+</span></span>
<span id="cb100-2"><a href="#cb100-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb100-3"><a href="#cb100-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb100-4"><a href="#cb100-4" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of differences 'p'"</span></span>
<span id="cb100-5"><a href="#cb100-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: Add a single generic competitor"</span></span>
<span id="cb100-6"><a href="#cb100-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Difference in 'p' under treatment"</span></span>
<span id="cb100-7"><a href="#cb100-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb100-8"><a href="#cb100-8" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-17-3.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb101"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb101-1"><a href="#cb101-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/default_p_generic_intervention_distdiff.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb103"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb103-1"><a href="#cb103-1" aria-hidden="true" tabindex="-1"></a>pddf_ib <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"predicted_difference"</span>)<span class="sc">$</span>predicted_difference) <span class="sc">|&gt;</span></span>
<span id="cb103-2"><a href="#cb103-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(X1<span class="sc">:</span>X1343)</span>
<span id="cb103-3"><a href="#cb103-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb103-4"><a href="#cb103-4" aria-hidden="true" tabindex="-1"></a><span class="co">#TODO: Fix Category names</span></span>
<span id="cb103-5"><a href="#cb103-5" aria-hidden="true" tabindex="-1"></a>pddf_ib[<span class="st">"entry_idx"</span>] <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">gsub</span>(<span class="st">"</span><span class="sc">\\</span><span class="st">D"</span>,<span class="st">""</span>,pddf_ib<span class="sc">$</span>name))</span>
<span id="cb103-6"><a href="#cb103-6" aria-hidden="true" tabindex="-1"></a>pddf_ib[<span class="st">"category"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(pddf_ib<span class="sc">$</span>entry_idx, <span class="cf">function</span>(i) df<span class="sc">$</span>category_id[i])</span>
<span id="cb103-7"><a href="#cb103-7" aria-hidden="true" tabindex="-1"></a>pddf_ib[<span class="st">"category_name"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(pddf_ib<span class="sc">$</span>category, <span class="cf">function</span>(i) beta_list<span class="sc">$</span>groups[i])</span>
<span id="cb103-8"><a href="#cb103-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb103-9"><a href="#cb103-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb103-10"><a href="#cb103-10" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf_ib, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb103-11"><a href="#cb103-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb103-12"><a href="#cb103-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb103-13"><a href="#cb103-13" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences"</span></span>
<span id="cb103-14"><a href="#cb103-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: add a single generic competitor"</span></span>
<span id="cb103-15"><a href="#cb103-15" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb103-16"><a href="#cb103-16" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Probability Density"</span></span>
<span id="cb103-17"><a href="#cb103-17" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb103-18"><a href="#cb103-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`</code></pre>
</div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-18-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb105"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb105-1"><a href="#cb105-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/p_generic_intervention_distdiff_styled.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb107"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb107-1"><a href="#cb107-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf_ib, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb107-2"><a href="#cb107-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb107-3"><a href="#cb107-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb107-4"><a href="#cb107-4" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span><span class="fu">factor</span>(</span>
<span id="cb107-5"><a href="#cb107-5" aria-hidden="true" tabindex="-1"></a> category_name, </span>
<span id="cb107-6"><a href="#cb107-6" aria-hidden="true" tabindex="-1"></a> <span class="at">levels=</span>beta_list<span class="sc">$</span>groups</span>
<span id="cb107-7"><a href="#cb107-7" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb107-8"><a href="#cb107-8" aria-hidden="true" tabindex="-1"></a> , <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">TRUE</span>)</span>
<span id="cb107-9"><a href="#cb107-9" aria-hidden="true" tabindex="-1"></a> , <span class="at">ncol=</span><span class="dv">5</span>) <span class="sc">+</span></span>
<span id="cb107-10"><a href="#cb107-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb107-11"><a href="#cb107-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences | By Group"</span></span>
<span id="cb107-12"><a href="#cb107-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: add a single generic competitor"</span></span>
<span id="cb107-13"><a href="#cb107-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb107-14"><a href="#cb107-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Probability Density"</span></span>
<span id="cb107-15"><a href="#cb107-15" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb107-16"><a href="#cb107-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) <span class="sc">+</span></span>
<span id="cb107-17"><a href="#cb107-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`</code></pre>
</div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-18-2.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb109"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb109-1"><a href="#cb109-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/p_generic_intervention_distdiff_by_group.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb111"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb111-1"><a href="#cb111-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf_ib, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb111-2"><a href="#cb111-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb111-3"><a href="#cb111-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb111-4"><a href="#cb111-4" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span><span class="fu">factor</span>(</span>
<span id="cb111-5"><a href="#cb111-5" aria-hidden="true" tabindex="-1"></a> category_name, </span>
<span id="cb111-6"><a href="#cb111-6" aria-hidden="true" tabindex="-1"></a> <span class="at">levels=</span>beta_list<span class="sc">$</span>groups</span>
<span id="cb111-7"><a href="#cb111-7" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb111-8"><a href="#cb111-8" aria-hidden="true" tabindex="-1"></a> , <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">TRUE</span>)</span>
<span id="cb111-9"><a href="#cb111-9" aria-hidden="true" tabindex="-1"></a> , <span class="at">ncol=</span><span class="dv">5</span>) <span class="sc">+</span></span>
<span id="cb111-10"><a href="#cb111-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb111-11"><a href="#cb111-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Histogram of predicted differences | By Group"</span></span>
<span id="cb111-12"><a href="#cb111-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: add a single generic competitor"</span></span>
<span id="cb111-13"><a href="#cb111-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb111-14"><a href="#cb111-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Predicted counts"</span></span>
<span id="cb111-15"><a href="#cb111-15" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb111-16"><a href="#cb111-16" aria-hidden="true" tabindex="-1"></a> <span class="co">#xlim(-0.25,0.1) +</span></span>
<span id="cb111-17"><a href="#cb111-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) <span class="sc">+</span></span>
<span id="cb111-18"><a href="#cb111-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-18-3.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb112"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb112-1"><a href="#cb112-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/p_generic_intervention_histdiff_by_group.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<p>Get the probability of increase over probability of a decrease</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb114"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb114-1"><a href="#cb114-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mean</span>(counterfact_predicted_ib<span class="sc">$</span>predicted_difference)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 0.1657709</code></pre>
</div>
</div>
<p>Thus adding a single generic competitor increases the probability of termination by 16.55% on average for the snapshots investigated.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb116"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb116-1"><a href="#cb116-1" aria-hidden="true" tabindex="-1"></a>n <span class="ot">=</span> <span class="fu">length</span>(counterfact_predicted_ib<span class="sc">$</span>p_predicted_intervention)</span>
<span id="cb116-2"><a href="#cb116-2" aria-hidden="true" tabindex="-1"></a><span class="fu">mean</span>(<span class="fu">rbinom</span>(n,<span class="dv">1</span>,<span class="fu">as.vector</span>(counterfact_predicted_ib<span class="sc">$</span>p_predicted_intervention)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 0.3118685</code></pre>
</div>
<div class="sourceCode cell-code" id="cb118"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb118-1"><a href="#cb118-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mean</span>(<span class="fu">rbinom</span>(n,<span class="dv">1</span>,<span class="fu">as.vector</span>(counterfact_predicted_ib<span class="sc">$</span>p_predicted_default)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 0.1460717</code></pre>
</div>
</div>
</section>
<section id="usp-dc-alternative" class="level4">
<h4 class="anchored" data-anchor-id="usp-dc-alternative">USP DC Alternative</h4>
<div class="cell">
<div class="sourceCode cell-code" id="cb120"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb120-1"><a href="#cb120-1" aria-hidden="true" tabindex="-1"></a><span class="co">#formulary intervention</span></span>
<span id="cb120-2"><a href="#cb120-2" aria-hidden="true" tabindex="-1"></a>brand_intervention_bnc <span class="ot">&lt;-</span> x[<span class="fu">c</span>(inherited_cols,<span class="st">"identical_brands"</span>)]</span>
<span id="cb120-3"><a href="#cb120-3" aria-hidden="true" tabindex="-1"></a>brand_intervention_bnc[<span class="st">"brand_name_counts"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(<span class="fu">sinh</span>(x<span class="sc">$</span>brand_name_counts)<span class="sc">+</span><span class="dv">1</span>) <span class="co">#add a single formulary competitor brand</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb121"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb121-1"><a href="#cb121-1" aria-hidden="true" tabindex="-1"></a>counterfact_marketing_bnc <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb121-2"><a href="#cb121-2" aria-hidden="true" tabindex="-1"></a> <span class="at">D =</span> <span class="fu">ncol</span>(x),<span class="co">#</span></span>
<span id="cb121-3"><a href="#cb121-3" aria-hidden="true" tabindex="-1"></a> <span class="at">N =</span> <span class="fu">nrow</span>(x),</span>
<span id="cb121-4"><a href="#cb121-4" aria-hidden="true" tabindex="-1"></a> <span class="at">L =</span> n_categories<span class="sc">$</span>count,</span>
<span id="cb121-5"><a href="#cb121-5" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="fu">as.vector</span>(y),</span>
<span id="cb121-6"><a href="#cb121-6" aria-hidden="true" tabindex="-1"></a> <span class="at">ll =</span> <span class="fu">as.vector</span>(categories),</span>
<span id="cb121-7"><a href="#cb121-7" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">as.matrix</span>(x),</span>
<span id="cb121-8"><a href="#cb121-8" aria-hidden="true" tabindex="-1"></a> <span class="at">mu_mean =</span> <span class="dv">0</span>,</span>
<span id="cb121-9"><a href="#cb121-9" aria-hidden="true" tabindex="-1"></a> <span class="at">mu_stdev =</span> <span class="fl">0.05</span>,</span>
<span id="cb121-10"><a href="#cb121-10" aria-hidden="true" tabindex="-1"></a> <span class="at">sigma_shape =</span> <span class="dv">4</span>,</span>
<span id="cb121-11"><a href="#cb121-11" aria-hidden="true" tabindex="-1"></a> <span class="at">sigma_rate =</span> <span class="dv">20</span>,</span>
<span id="cb121-12"><a href="#cb121-12" aria-hidden="true" tabindex="-1"></a> <span class="at">Nx =</span> <span class="fu">nrow</span>(x),</span>
<span id="cb121-13"><a href="#cb121-13" aria-hidden="true" tabindex="-1"></a> <span class="at">llx =</span> <span class="fu">as.vector</span>(categories),</span>
<span id="cb121-14"><a href="#cb121-14" aria-hidden="true" tabindex="-1"></a> <span class="at">counterfact_x_tilde =</span> <span class="fu">as.matrix</span>(brand_intervention_bnc),</span>
<span id="cb121-15"><a href="#cb121-15" aria-hidden="true" tabindex="-1"></a> <span class="at">counterfact_x =</span> <span class="fu">as.matrix</span>(x)</span>
<span id="cb121-16"><a href="#cb121-16" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb122"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb122-1"><a href="#cb122-1" aria-hidden="true" tabindex="-1"></a>generated_bnc <span class="ot">&lt;-</span> <span class="fu">gqs</span>(</span>
<span id="cb122-2"><a href="#cb122-2" aria-hidden="true" tabindex="-1"></a> fit<span class="sc">@</span>stanmodel,</span>
<span id="cb122-3"><a href="#cb122-3" aria-hidden="true" tabindex="-1"></a> <span class="at">data=</span>counterfact_marketing_bnc,</span>
<span id="cb122-4"><a href="#cb122-4" aria-hidden="true" tabindex="-1"></a> <span class="at">draws=</span><span class="fu">as.matrix</span>(fit),</span>
<span id="cb122-5"><a href="#cb122-5" aria-hidden="true" tabindex="-1"></a> <span class="at">seed=</span><span class="dv">11021585</span></span>
<span id="cb122-6"><a href="#cb122-6" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb123"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb123-1"><a href="#cb123-1" aria-hidden="true" tabindex="-1"></a>counterfact_predicted_bnc <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb123-2"><a href="#cb123-2" aria-hidden="true" tabindex="-1"></a> <span class="at">p_predicted_default =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_bnc, <span class="at">pars=</span><span class="st">"p_predicted_default"</span>)<span class="sc">$</span>p_predicted_default)</span>
<span id="cb123-3"><a href="#cb123-3" aria-hidden="true" tabindex="-1"></a> ,<span class="at">p_predicted_intervention =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_bnc, <span class="at">pars=</span><span class="st">"p_predicted_intervention"</span>)<span class="sc">$</span>p_predicted_intervention)</span>
<span id="cb123-4"><a href="#cb123-4" aria-hidden="true" tabindex="-1"></a> ,<span class="at">predicted_difference =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_bnc, <span class="at">pars=</span><span class="st">"predicted_difference"</span>)<span class="sc">$</span>predicted_difference)</span>
<span id="cb123-5"><a href="#cb123-5" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb123-6"><a href="#cb123-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb123-7"><a href="#cb123-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb123-8"><a href="#cb123-8" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(counterfact_predicted_bnc, <span class="fu">aes</span>(<span class="at">x=</span>p_predicted_default)) <span class="sc">+</span></span>
<span id="cb123-9"><a href="#cb123-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb123-10"><a href="#cb123-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb123-11"><a href="#cb123-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of 'p'"</span></span>
<span id="cb123-12"><a href="#cb123-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: None"</span></span>
<span id="cb123-13"><a href="#cb123-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb123-14"><a href="#cb123-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb123-15"><a href="#cb123-15" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-24-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb124"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb124-1"><a href="#cb124-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/default_p_uspdc_intervention_base.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb126"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb126-1"><a href="#cb126-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(counterfact_predicted_bnc, <span class="fu">aes</span>(<span class="at">x=</span>p_predicted_intervention)) <span class="sc">+</span></span>
<span id="cb126-2"><a href="#cb126-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb126-3"><a href="#cb126-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb126-4"><a href="#cb126-4" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of 'p'"</span></span>
<span id="cb126-5"><a href="#cb126-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: Add a single USP DC competitor"</span></span>
<span id="cb126-6"><a href="#cb126-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb126-7"><a href="#cb126-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb126-8"><a href="#cb126-8" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-24-2.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb127"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb127-1"><a href="#cb127-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/default_p_uspdc_intervention_interv.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb129"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb129-1"><a href="#cb129-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(counterfact_predicted_bnc, <span class="fu">aes</span>(<span class="at">x=</span>predicted_difference)) <span class="sc">+</span></span>
<span id="cb129-2"><a href="#cb129-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb129-3"><a href="#cb129-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb129-4"><a href="#cb129-4" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of differences 'p'"</span></span>
<span id="cb129-5"><a href="#cb129-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: Add a single USP DC competitor"</span></span>
<span id="cb129-6"><a href="#cb129-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Difference in 'p' under treatment"</span></span>
<span id="cb129-7"><a href="#cb129-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb129-8"><a href="#cb129-8" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-24-3.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb130"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb130-1"><a href="#cb130-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/default_p_uspdc_intervention_distdiff.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb132"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb132-1"><a href="#cb132-1" aria-hidden="true" tabindex="-1"></a>pddf_bnc <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">extract</span>(generated_bnc, <span class="at">pars=</span><span class="st">"predicted_difference"</span>)<span class="sc">$</span>predicted_difference) <span class="sc">|&gt;</span></span>
<span id="cb132-2"><a href="#cb132-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(X1<span class="sc">:</span>X1343)</span>
<span id="cb132-3"><a href="#cb132-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb132-4"><a href="#cb132-4" aria-hidden="true" tabindex="-1"></a><span class="co">#Add Category names</span></span>
<span id="cb132-5"><a href="#cb132-5" aria-hidden="true" tabindex="-1"></a>pddf_bnc[<span class="st">"entry_idx"</span>] <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">gsub</span>(<span class="st">"</span><span class="sc">\\</span><span class="st">D"</span>,<span class="st">""</span>,pddf_bnc<span class="sc">$</span>name))</span>
<span id="cb132-6"><a href="#cb132-6" aria-hidden="true" tabindex="-1"></a>pddf_bnc[<span class="st">"category"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(pddf_bnc<span class="sc">$</span>entry_idx, <span class="cf">function</span>(i) df<span class="sc">$</span>category_id[i])</span>
<span id="cb132-7"><a href="#cb132-7" aria-hidden="true" tabindex="-1"></a>pddf_bnc[<span class="st">"category_name"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(pddf_bnc<span class="sc">$</span>category, <span class="cf">function</span>(i) beta_list<span class="sc">$</span>groups[i])</span>
<span id="cb132-8"><a href="#cb132-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb132-9"><a href="#cb132-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb132-10"><a href="#cb132-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb132-11"><a href="#cb132-11" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf_bnc, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb132-12"><a href="#cb132-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb132-13"><a href="#cb132-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb132-14"><a href="#cb132-14" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences"</span></span>
<span id="cb132-15"><a href="#cb132-15" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: add a single USP DC competitor"</span></span>
<span id="cb132-16"><a href="#cb132-16" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb132-17"><a href="#cb132-17" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Probability Density"</span></span>
<span id="cb132-18"><a href="#cb132-18" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb132-19"><a href="#cb132-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`</code></pre>
</div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-25-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb134"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb134-1"><a href="#cb134-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/p_uspdc_intervention_distdiff_styled.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb136"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb136-1"><a href="#cb136-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf_bnc, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb136-2"><a href="#cb136-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb136-3"><a href="#cb136-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb136-4"><a href="#cb136-4" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span><span class="fu">factor</span>(</span>
<span id="cb136-5"><a href="#cb136-5" aria-hidden="true" tabindex="-1"></a> category_name, </span>
<span id="cb136-6"><a href="#cb136-6" aria-hidden="true" tabindex="-1"></a> <span class="at">levels=</span>beta_list<span class="sc">$</span>groups</span>
<span id="cb136-7"><a href="#cb136-7" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb136-8"><a href="#cb136-8" aria-hidden="true" tabindex="-1"></a> , <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">TRUE</span>)</span>
<span id="cb136-9"><a href="#cb136-9" aria-hidden="true" tabindex="-1"></a> , <span class="at">ncol=</span><span class="dv">5</span>) <span class="sc">+</span></span>
<span id="cb136-10"><a href="#cb136-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb136-11"><a href="#cb136-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences in 'p' | By Group"</span></span>
<span id="cb136-12"><a href="#cb136-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: add a single USP DC competitor"</span></span>
<span id="cb136-13"><a href="#cb136-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb136-14"><a href="#cb136-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Probability Density"</span></span>
<span id="cb136-15"><a href="#cb136-15" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb136-16"><a href="#cb136-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) <span class="sc">+</span></span>
<span id="cb136-17"><a href="#cb136-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`</code></pre>
</div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-25-2.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb138"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb138-1"><a href="#cb138-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/p_uspdc_intervention_distdiff_by_group.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb140"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb140-1"><a href="#cb140-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf_bnc, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb140-2"><a href="#cb140-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb140-3"><a href="#cb140-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb140-4"><a href="#cb140-4" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span><span class="fu">factor</span>(</span>
<span id="cb140-5"><a href="#cb140-5" aria-hidden="true" tabindex="-1"></a> category_name, </span>
<span id="cb140-6"><a href="#cb140-6" aria-hidden="true" tabindex="-1"></a> <span class="at">levels=</span>beta_list<span class="sc">$</span>groups</span>
<span id="cb140-7"><a href="#cb140-7" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb140-8"><a href="#cb140-8" aria-hidden="true" tabindex="-1"></a> , <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">TRUE</span>)</span>
<span id="cb140-9"><a href="#cb140-9" aria-hidden="true" tabindex="-1"></a> , <span class="at">ncol=</span><span class="dv">5</span>) <span class="sc">+</span></span>
<span id="cb140-10"><a href="#cb140-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb140-11"><a href="#cb140-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Histogram of predicted differences in 'p' | By Group"</span></span>
<span id="cb140-12"><a href="#cb140-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: add a single USP DC competitor"</span></span>
<span id="cb140-13"><a href="#cb140-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb140-14"><a href="#cb140-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Predicted counts"</span></span>
<span id="cb140-15"><a href="#cb140-15" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb140-16"><a href="#cb140-16" aria-hidden="true" tabindex="-1"></a> <span class="co">#xlim(-0.25,0.1) +</span></span>
<span id="cb140-17"><a href="#cb140-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) <span class="sc">+</span></span>
<span id="cb140-18"><a href="#cb140-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="EffectsOfMarketConditions_files/figure-html/unnamed-chunk-25-3.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb141"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb141-1"><a href="#cb141-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./Images/DirectEffects/p_uspdc_intervention_histdiff_by_group.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
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</div>
<p>TODO: add density plot of (x,y,z) (date,value,counts) - with and without faceting</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb143"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb143-1"><a href="#cb143-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mean</span>(counterfact_predicted_bnc<span class="sc">$</span>predicted_difference)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 0.1649445</code></pre>
</div>
</div>
<p>Addin a single USP DC competitor increases/reduces the probability of completion by 16.47% on average for the snapshots of trials that we have.</p>
</section>
</section>
<section id="intervention-marginal-increase-in-time-to-finish-enrollment" class="level3">
<h3 class="anchored" data-anchor-id="intervention-marginal-increase-in-time-to-finish-enrollment">Intervention: Marginal increase in time to finish enrollment</h3>
<div class="cell">
<div class="sourceCode cell-code" id="cb145"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb145-1"><a href="#cb145-1" aria-hidden="true" tabindex="-1"></a>pddf <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">extract</span>(generated, <span class="at">pars=</span><span class="st">"predicted_difference"</span>)<span class="sc">$</span>predicted_difference) <span class="sc">|&gt;</span> <span class="fu">pivot_longer</span>(X1<span class="sc">:</span>X189)</span>
<span id="cb145-2"><a href="#cb145-2" aria-hidden="true" tabindex="-1"></a>pddf[<span class="st">"entry_idx"</span>] <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">gsub</span>(<span class="st">"</span><span class="sc">\\</span><span class="st">D"</span>,<span class="st">""</span>,pddf<span class="sc">$</span>name))</span>
<span id="cb145-3"><a href="#cb145-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb145-4"><a href="#cb145-4" aria-hidden="true" tabindex="-1"></a>pddf[<span class="st">"category"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(pddf<span class="sc">$</span>entry_idx, <span class="cf">function</span>(i) counterfact_categories[i])</span>
<span id="cb145-5"><a href="#cb145-5" aria-hidden="true" tabindex="-1"></a>pddf[<span class="st">"category_name"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(pddf<span class="sc">$</span>category, <span class="cf">function</span>(i) beta_list<span class="sc">$</span>groups[i])</span>
<span id="cb145-6"><a href="#cb145-6" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb145-7"><a href="#cb145-7" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb145-8"><a href="#cb145-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb145-9"><a href="#cb145-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb145-10"><a href="#cb145-10" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences"</span></span>
<span id="cb145-11"><a href="#cb145-11" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb145-12"><a href="#cb145-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Predicted counts"</span></span>
<span id="cb145-13"><a href="#cb145-13" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb145-14"><a href="#cb145-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlim</span>(<span class="sc">-</span><span class="fl">0.3</span>,<span class="fl">0.1</span>) <span class="sc">+</span></span>
<span id="cb145-15"><a href="#cb145-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) </span>
<span id="cb145-16"><a href="#cb145-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb145-17"><a href="#cb145-17" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(pddf, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb145-18"><a href="#cb145-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">bins=</span><span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb145-19"><a href="#cb145-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb145-20"><a href="#cb145-20" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span><span class="fu">factor</span>(</span>
<span id="cb145-21"><a href="#cb145-21" aria-hidden="true" tabindex="-1"></a> category_name, </span>
<span id="cb145-22"><a href="#cb145-22" aria-hidden="true" tabindex="-1"></a> <span class="at">levels=</span>beta_list<span class="sc">$</span>groups</span>
<span id="cb145-23"><a href="#cb145-23" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb145-24"><a href="#cb145-24" aria-hidden="true" tabindex="-1"></a> , <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">TRUE</span>)</span>
<span id="cb145-25"><a href="#cb145-25" aria-hidden="true" tabindex="-1"></a> , <span class="at">ncol=</span><span class="dv">5</span>) <span class="sc">+</span></span>
<span id="cb145-26"><a href="#cb145-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb145-27"><a href="#cb145-27" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences"</span>,</span>
<span id="cb145-28"><a href="#cb145-28" aria-hidden="true" tabindex="-1"></a> <span class="at">subtitle =</span> <span class="st">"By group"</span></span>
<span id="cb145-29"><a href="#cb145-29" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb145-30"><a href="#cb145-30" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Predicted counts"</span></span>
<span id="cb145-31"><a href="#cb145-31" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb145-32"><a href="#cb145-32" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlim</span>(<span class="sc">-</span><span class="fl">0.25</span>,<span class="fl">0.1</span>) <span class="sc">+</span></span>
<span id="cb145-33"><a href="#cb145-33" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="fu">aes</span>(<span class="at">xintercept =</span> <span class="dv">0</span>), <span class="at">color =</span> <span class="st">"skyblue"</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>) <span class="sc">+</span></span>
<span id="cb145-34"><a href="#cb145-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">strip.text.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Recall that we had really tight zero priors.</p>
</section>
</section>
<section id="diagnostics" class="level1">
<h1>Diagnostics</h1>
<div class="cell">
<div class="sourceCode cell-code" id="cb146"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb146-1"><a href="#cb146-1" aria-hidden="true" tabindex="-1"></a><span class="co">#trace plots</span></span>
<span id="cb146-2"><a href="#cb146-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(fit, <span class="at">pars=</span><span class="fu">c</span>(<span class="st">"mu"</span>), <span class="at">plotfun=</span><span class="st">"trace"</span>)</span>
<span id="cb146-3"><a href="#cb146-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb146-4"><a href="#cb146-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb146-5"><a href="#cb146-5" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>) {</span>
<span id="cb146-6"><a href="#cb146-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb146-7"><a href="#cb146-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_rank_overlay</span>(</span>
<span id="cb146-8"><a href="#cb146-8" aria-hidden="true" tabindex="-1"></a> fit, </span>
<span id="cb146-9"><a href="#cb146-9" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb146-10"><a href="#cb146-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"mu["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-3</span>,<span class="st">"]"</span>),</span>
<span id="cb146-11"><a href="#cb146-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"mu["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-2</span>,<span class="st">"]"</span>),</span>
<span id="cb146-12"><a href="#cb146-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"mu["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-1</span>,<span class="st">"]"</span>),</span>
<span id="cb146-13"><a href="#cb146-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"mu["</span>,<span class="dv">4</span><span class="sc">*</span>i,<span class="st">"]"</span>)</span>
<span id="cb146-14"><a href="#cb146-14" aria-hidden="true" tabindex="-1"></a> ), </span>
<span id="cb146-15"><a href="#cb146-15" aria-hidden="true" tabindex="-1"></a> <span class="at">n_bins=</span><span class="dv">100</span></span>
<span id="cb146-16"><a href="#cb146-16" aria-hidden="true" tabindex="-1"></a> )<span class="sc">+</span> <span class="fu">legend_move</span>(<span class="st">"top"</span>) <span class="sc">+</span></span>
<span id="cb146-17"><a href="#cb146-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_colour_ghibli_d</span>(<span class="st">"KikiMedium"</span>)</span>
<span id="cb146-18"><a href="#cb146-18" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb146-19"><a href="#cb146-19" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb147"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb147-1"><a href="#cb147-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(fit, <span class="at">pars=</span><span class="fu">c</span>(<span class="st">"sigma"</span>), <span class="at">plotfun=</span><span class="st">"trace"</span>)</span>
<span id="cb147-2"><a href="#cb147-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb147-3"><a href="#cb147-3" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>) {</span>
<span id="cb147-4"><a href="#cb147-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb147-5"><a href="#cb147-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_rank_overlay</span>(</span>
<span id="cb147-6"><a href="#cb147-6" aria-hidden="true" tabindex="-1"></a> fit, </span>
<span id="cb147-7"><a href="#cb147-7" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb147-8"><a href="#cb147-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"sigma["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-3</span>,<span class="st">"]"</span>),</span>
<span id="cb147-9"><a href="#cb147-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"sigma["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-2</span>,<span class="st">"]"</span>),</span>
<span id="cb147-10"><a href="#cb147-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"sigma["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-1</span>,<span class="st">"]"</span>),</span>
<span id="cb147-11"><a href="#cb147-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"sigma["</span>,<span class="dv">4</span><span class="sc">*</span>i,<span class="st">"]"</span>)</span>
<span id="cb147-12"><a href="#cb147-12" aria-hidden="true" tabindex="-1"></a> ), </span>
<span id="cb147-13"><a href="#cb147-13" aria-hidden="true" tabindex="-1"></a> <span class="at">n_bins=</span><span class="dv">100</span></span>
<span id="cb147-14"><a href="#cb147-14" aria-hidden="true" tabindex="-1"></a> )<span class="sc">+</span> <span class="fu">legend_move</span>(<span class="st">"top"</span>) <span class="sc">+</span></span>
<span id="cb147-15"><a href="#cb147-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_colour_ghibli_d</span>(<span class="st">"KikiMedium"</span>)</span>
<span id="cb147-16"><a href="#cb147-16" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb147-17"><a href="#cb147-17" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb148"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb148-1"><a href="#cb148-1" aria-hidden="true" tabindex="-1"></a><span class="co">#other diagnostics</span></span>
<span id="cb148-2"><a href="#cb148-2" aria-hidden="true" tabindex="-1"></a>logpost <span class="ot">&lt;-</span> <span class="fu">log_posterior</span>(fit)</span>
<span id="cb148-3"><a href="#cb148-3" aria-hidden="true" tabindex="-1"></a>nuts_prmts <span class="ot">&lt;-</span> <span class="fu">nuts_params</span>(fit)</span>
<span id="cb148-4"><a href="#cb148-4" aria-hidden="true" tabindex="-1"></a>posterior <span class="ot">&lt;-</span> <span class="fu">as.array</span>(fit)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb149"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb149-1"><a href="#cb149-1" aria-hidden="true" tabindex="-1"></a><span class="fu">color_scheme_set</span>(<span class="st">"darkgray"</span>)</span>
<span id="cb149-2"><a href="#cb149-2" aria-hidden="true" tabindex="-1"></a>div_style <span class="ot">&lt;-</span> <span class="fu">parcoord_style_np</span>(<span class="at">div_color =</span> <span class="st">"green"</span>, <span class="at">div_size =</span> <span class="fl">0.05</span>, <span class="at">div_alpha =</span> <span class="fl">0.4</span>)</span>
<span id="cb149-3"><a href="#cb149-3" aria-hidden="true" tabindex="-1"></a><span class="fu">mcmc_parcoord</span>(posterior, <span class="at">regex_pars =</span> <span class="st">"mu"</span>, <span class="at">np=</span>nuts_prmts, <span class="at">np_style =</span> div_style, <span class="at">alpha =</span> <span class="fl">0.05</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb150"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb150-1"><a href="#cb150-1" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>) {</span>
<span id="cb150-2"><a href="#cb150-2" aria-hidden="true" tabindex="-1"></a> mus <span class="ot">=</span> <span class="fu">sapply</span>(<span class="dv">3</span><span class="sc">:</span><span class="dv">0</span>, <span class="cf">function</span>(j) <span class="fu">paste0</span>(<span class="st">"mu["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="sc">-</span>j ,<span class="st">"]"</span>))</span>
<span id="cb150-3"><a href="#cb150-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb150-4"><a href="#cb150-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_pairs</span>(</span>
<span id="cb150-5"><a href="#cb150-5" aria-hidden="true" tabindex="-1"></a> posterior,</span>
<span id="cb150-6"><a href="#cb150-6" aria-hidden="true" tabindex="-1"></a> <span class="at">np =</span> nuts_prmts,</span>
<span id="cb150-7"><a href="#cb150-7" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb150-8"><a href="#cb150-8" aria-hidden="true" tabindex="-1"></a> mus,</span>
<span id="cb150-9"><a href="#cb150-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"lp__"</span></span>
<span id="cb150-10"><a href="#cb150-10" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb150-11"><a href="#cb150-11" aria-hidden="true" tabindex="-1"></a> <span class="at">off_diag_args =</span> <span class="fu">list</span>(<span class="at">size =</span> <span class="fl">0.75</span>)</span>
<span id="cb150-12"><a href="#cb150-12" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb150-13"><a href="#cb150-13" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb150-14"><a href="#cb150-14" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb151"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb151-1"><a href="#cb151-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mcmc_parcoord</span>(posterior,<span class="at">regex_pars =</span> <span class="st">"sigma"</span>, <span class="at">np=</span>nuts_prmts, <span class="at">alpha=</span><span class="fl">0.05</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb152"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb152-1"><a href="#cb152-1" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>) {</span>
<span id="cb152-2"><a href="#cb152-2" aria-hidden="true" tabindex="-1"></a> params <span class="ot">=</span> <span class="fu">sapply</span>(<span class="dv">3</span><span class="sc">:</span><span class="dv">0</span>, <span class="cf">function</span>(j) <span class="fu">paste0</span>(<span class="st">"sigma["</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="sc">-</span>j ,<span class="st">"]"</span>))</span>
<span id="cb152-3"><a href="#cb152-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb152-4"><a href="#cb152-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_pairs</span>(</span>
<span id="cb152-5"><a href="#cb152-5" aria-hidden="true" tabindex="-1"></a> posterior,</span>
<span id="cb152-6"><a href="#cb152-6" aria-hidden="true" tabindex="-1"></a> <span class="at">np =</span> nuts_prmts,</span>
<span id="cb152-7"><a href="#cb152-7" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb152-8"><a href="#cb152-8" aria-hidden="true" tabindex="-1"></a> params,</span>
<span id="cb152-9"><a href="#cb152-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"lp__"</span></span>
<span id="cb152-10"><a href="#cb152-10" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb152-11"><a href="#cb152-11" aria-hidden="true" tabindex="-1"></a> <span class="at">off_diag_args =</span> <span class="fu">list</span>(<span class="at">size =</span> <span class="fl">0.75</span>)</span>
<span id="cb152-12"><a href="#cb152-12" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb152-13"><a href="#cb152-13" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb152-14"><a href="#cb152-14" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb153"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb153-1"><a href="#cb153-1" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (k <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">22</span>) {</span>
<span id="cb153-2"><a href="#cb153-2" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>) {</span>
<span id="cb153-3"><a href="#cb153-3" aria-hidden="true" tabindex="-1"></a> params <span class="ot">=</span> <span class="fu">sapply</span>(<span class="dv">3</span><span class="sc">:</span><span class="dv">0</span>, <span class="cf">function</span>(j) <span class="fu">paste0</span>(<span class="st">"beta["</span>,k,<span class="st">","</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="sc">-</span>j ,<span class="st">"]"</span>))</span>
<span id="cb153-4"><a href="#cb153-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb153-5"><a href="#cb153-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_pairs</span>(</span>
<span id="cb153-6"><a href="#cb153-6" aria-hidden="true" tabindex="-1"></a> posterior,</span>
<span id="cb153-7"><a href="#cb153-7" aria-hidden="true" tabindex="-1"></a> <span class="at">np =</span> nuts_prmts,</span>
<span id="cb153-8"><a href="#cb153-8" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb153-9"><a href="#cb153-9" aria-hidden="true" tabindex="-1"></a> params,</span>
<span id="cb153-10"><a href="#cb153-10" aria-hidden="true" tabindex="-1"></a> <span class="st">"lp__"</span></span>
<span id="cb153-11"><a href="#cb153-11" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb153-12"><a href="#cb153-12" aria-hidden="true" tabindex="-1"></a> <span class="at">off_diag_args =</span> <span class="fu">list</span>(<span class="at">size =</span> <span class="fl">0.75</span>)</span>
<span id="cb153-13"><a href="#cb153-13" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb153-14"><a href="#cb153-14" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb153-15"><a href="#cb153-15" aria-hidden="true" tabindex="-1"></a>}}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="todo" class="level1">
<h1>TODO</h1>
<ul class="task-list">
<li><input type="checkbox">Double check data flow. (Write summary of this in human readable form)
<ul>
<li>Is it the data we want from the database
<ul>
<li>Training</li>
<li>Counterfactual Evaluation
<ul>
<li>choose a single snapshot per trial.</li>
</ul></li>
</ul></li>
<li>Is the model in STAN well specified.</li>
</ul></li>
<li><input type="checkbox">work on LOO validation of model</li>
</ul>
</section>
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button.blur();
// flash "checked"
button.classList.add('code-copy-button-checked');
var currentTitle = button.getAttribute("title");
button.setAttribute("title", "Copied!");
let tooltip;
if (window.bootstrap) {
button.setAttribute("data-bs-toggle", "tooltip");
button.setAttribute("data-bs-placement", "left");
button.setAttribute("data-bs-title", "Copied!");
tooltip = new bootstrap.Tooltip(button,
{ trigger: "manual",
customClass: "code-copy-button-tooltip",
offset: [0, -8]});
tooltip.show();
}
setTimeout(function() {
if (tooltip) {
tooltip.hide();
button.removeAttribute("data-bs-title");
button.removeAttribute("data-bs-toggle");
button.removeAttribute("data-bs-placement");
}
button.setAttribute("title", currentTitle);
button.classList.remove('code-copy-button-checked');
}, 1000);
// clear code selection
e.clearSelection();
});
function tippyHover(el, contentFn) {
const config = {
allowHTML: true,
content: contentFn,
maxWidth: 500,
delay: 100,
arrow: false,
appendTo: function(el) {
return el.parentElement;
},
interactive: true,
interactiveBorder: 10,
theme: 'quarto',
placement: 'bottom-start'
};
window.tippy(el, config);
}
const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
for (var i=0; i<noterefs.length; i++) {
const ref = noterefs[i];
tippyHover(ref, function() {
// use id or data attribute instead here
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
try { href = new URL(href).hash; } catch {}
const id = href.replace(/^#\/?/, "");
const note = window.document.getElementById(id);
return note.innerHTML;
});
}
let selectedAnnoteEl;
const selectorForAnnotation = ( cell, annotation) => {
let cellAttr = 'data-code-cell="' + cell + '"';
let lineAttr = 'data-code-annotation="' + annotation + '"';
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
return selector;
}
const selectCodeLines = (annoteEl) => {
const doc = window.document;
const targetCell = annoteEl.getAttribute("data-target-cell");
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
const lineIds = lines.map((line) => {
return targetCell + "-" + line;
})
let top = null;
let height = null;
let parent = null;
if (lineIds.length > 0) {
//compute the position of the single el (top and bottom and make a div)
const el = window.document.getElementById(lineIds[0]);
top = el.offsetTop;
height = el.offsetHeight;
parent = el.parentElement.parentElement;
if (lineIds.length > 1) {
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
height = bottom - top;
}
if (top !== null && height !== null && parent !== null) {
// cook up a div (if necessary) and position it
let div = window.document.getElementById("code-annotation-line-highlight");
if (div === null) {
div = window.document.createElement("div");
div.setAttribute("id", "code-annotation-line-highlight");
div.style.position = 'absolute';
parent.appendChild(div);
}
div.style.top = top - 2 + "px";
div.style.height = height + 4 + "px";
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
if (gutterDiv === null) {
gutterDiv = window.document.createElement("div");
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
gutterDiv.style.position = 'absolute';
const codeCell = window.document.getElementById(targetCell);
const gutter = codeCell.querySelector('.code-annotation-gutter');
gutter.appendChild(gutterDiv);
}
gutterDiv.style.top = top - 2 + "px";
gutterDiv.style.height = height + 4 + "px";
}
selectedAnnoteEl = annoteEl;
}
};
const unselectCodeLines = () => {
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
elementsIds.forEach((elId) => {
const div = window.document.getElementById(elId);
if (div) {
div.remove();
}
});
selectedAnnoteEl = undefined;
};
// Attach click handler to the DT
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
for (const annoteDlNode of annoteDls) {
annoteDlNode.addEventListener('click', (event) => {
const clickedEl = event.target;
if (clickedEl !== selectedAnnoteEl) {
unselectCodeLines();
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
if (activeEl) {
activeEl.classList.remove('code-annotation-active');
}
selectCodeLines(clickedEl);
clickedEl.classList.add('code-annotation-active');
} else {
// Unselect the line
unselectCodeLines();
clickedEl.classList.remove('code-annotation-active');
}
});
}
const findCites = (el) => {
const parentEl = el.parentElement;
if (parentEl) {
const cites = parentEl.dataset.cites;
if (cites) {
return {
el,
cites: cites.split(' ')
};
} else {
return findCites(el.parentElement)
}
} else {
return undefined;
}
};
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
for (var i=0; i<bibliorefs.length; i++) {
const ref = bibliorefs[i];
const citeInfo = findCites(ref);
if (citeInfo) {
tippyHover(citeInfo.el, function() {
var popup = window.document.createElement('div');
citeInfo.cites.forEach(function(cite) {
var citeDiv = window.document.createElement('div');
citeDiv.classList.add('hanging-indent');
citeDiv.classList.add('csl-entry');
var biblioDiv = window.document.getElementById('ref-' + cite);
if (biblioDiv) {
citeDiv.innerHTML = biblioDiv.innerHTML;
}
popup.appendChild(citeDiv);
});
return popup.innerHTML;
});
}
}
});
</script>
</div> <!-- /content -->
</body></html>
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