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<h1 class="title">The Effects of Recruitment status on 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>
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</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>(knitr)</span>
<span id="cb1-2"><a href="#cb1-2" 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 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ lubridate 1.9.4 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.1</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()
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>host <span class="ot">&lt;-</span> <span class="st">'aact_db-restored-2025-01-07'</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>driver <span class="ot">&lt;-</span> <span class="fu">dbDriver</span>(<span class="st">"PostgreSQL"</span>)</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>get_data <span class="ot">&lt;-</span> <span class="cf">function</span>(driver) {</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a>con <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(</span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> driver,</span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a> <span class="at">user=</span><span class="st">'root'</span>,</span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a> <span class="at">password=</span><span class="st">'root'</span>,</span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a> <span class="at">dbname=</span><span class="st">'aact_db'</span>,</span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a> <span class="at">host=</span>host</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><span class="fu">on.exit</span>(<span class="fu">dbDisconnect</span>(con))</span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="fu">dbSendQuery</span>(</span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a><span class="co"># "select * from formatted_data_with_planned_enrollment;"</span></span>
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a><span class="st">"</span></span>
<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a><span class="st">select </span></span>
<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a><span class="st"> fdqpe.nct_id</span></span>
<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.start_date</span></span>
<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.current_enrollment</span></span>
<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.enrollment_category</span></span>
<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.current_status </span></span>
<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.earliest_date_observed </span></span>
<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.elapsed_duration</span></span>
<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.n_brands as identical_brands</span></span>
<span id="cb17-29"><a href="#cb17-29" aria-hidden="true" tabindex="-1"></a><span class="st"> ,ntbtu.brand_name_counts </span></span>
<span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.category_id</span></span>
<span id="cb17-31"><a href="#cb17-31" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.final_status</span></span>
<span id="cb17-32"><a href="#cb17-32" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.h_sdi_val</span></span>
<span id="cb17-33"><a href="#cb17-33" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.h_sdi_u95</span></span>
<span id="cb17-34"><a href="#cb17-34" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.h_sdi_l95</span></span>
<span id="cb17-35"><a href="#cb17-35" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.hm_sdi_val</span></span>
<span id="cb17-36"><a href="#cb17-36" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.hm_sdi_u95</span></span>
<span id="cb17-37"><a href="#cb17-37" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.hm_sdi_l95</span></span>
<span id="cb17-38"><a href="#cb17-38" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.m_sdi_val</span></span>
<span id="cb17-39"><a href="#cb17-39" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.m_sdi_u95</span></span>
<span id="cb17-40"><a href="#cb17-40" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.m_sdi_l95</span></span>
<span id="cb17-41"><a href="#cb17-41" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.lm_sdi_val</span></span>
<span id="cb17-42"><a href="#cb17-42" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.lm_sdi_u95</span></span>
<span id="cb17-43"><a href="#cb17-43" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.lm_sdi_l95</span></span>
<span id="cb17-44"><a href="#cb17-44" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.l_sdi_val</span></span>
<span id="cb17-45"><a href="#cb17-45" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.l_sdi_u95</span></span>
<span id="cb17-46"><a href="#cb17-46" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.l_sdi_l95</span></span>
<span id="cb17-47"><a href="#cb17-47" aria-hidden="true" tabindex="-1"></a><span class="st">from formatted_data_with_planned_enrollment fdqpe</span></span>
<span id="cb17-48"><a href="#cb17-48" 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_brand_counts_through_uspdc ntbtu</span></span>
<span id="cb17-49"><a href="#cb17-49" aria-hidden="true" tabindex="-1"></a><span class="st"> on fdqpe.nct_id = ntbtu.nct_id </span></span>
<span id="cb17-50"><a href="#cb17-50" aria-hidden="true" tabindex="-1"></a><span class="st">order by fdqpe.nct_id, fdqpe.earliest_date_observed </span></span>
<span id="cb17-51"><a href="#cb17-51" aria-hidden="true" tabindex="-1"></a><span class="st">;</span></span>
<span id="cb17-52"><a href="#cb17-52" aria-hidden="true" tabindex="-1"></a><span class="st">"</span></span>
<span id="cb17-53"><a href="#cb17-53" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb17-54"><a href="#cb17-54" 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="cb17-55"><a href="#cb17-55" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(df)</span>
<span id="cb17-56"><a href="#cb17-56" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-57"><a href="#cb17-57" 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="cb17-58"><a href="#cb17-58" 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="cb17-59"><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 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="cb17-61"><a href="#cb17-61" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb17-62"><a href="#cb17-62" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-63"><a href="#cb17-63" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-64"><a href="#cb17-64" aria-hidden="true" tabindex="-1"></a>get_counterfact_base <span class="ot">&lt;-</span> <span class="cf">function</span>(driver) {</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>con <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(</span>
<span id="cb17-67"><a href="#cb17-67" aria-hidden="true" tabindex="-1"></a> driver,</span>
<span id="cb17-68"><a href="#cb17-68" aria-hidden="true" tabindex="-1"></a> <span class="at">user=</span><span class="st">'root'</span>,</span>
<span id="cb17-69"><a href="#cb17-69" aria-hidden="true" tabindex="-1"></a> <span class="at">password=</span><span class="st">'root'</span>,</span>
<span id="cb17-70"><a href="#cb17-70" aria-hidden="true" tabindex="-1"></a> <span class="at">dbname=</span><span class="st">'aact_db'</span>,</span>
<span id="cb17-71"><a href="#cb17-71" aria-hidden="true" tabindex="-1"></a> <span class="at">host=</span>host</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><span class="fu">on.exit</span>(<span class="fu">dbDisconnect</span>(con))</span>
<span id="cb17-74"><a href="#cb17-74" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-75"><a href="#cb17-75" aria-hidden="true" tabindex="-1"></a>query <span class="ot">&lt;-</span> <span class="fu">dbSendQuery</span>(</span>
<span id="cb17-76"><a href="#cb17-76" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb17-77"><a href="#cb17-77" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span></span>
<span id="cb17-78"><a href="#cb17-78" aria-hidden="true" tabindex="-1"></a><span class="st"> with cte as (</span></span>
<span id="cb17-79"><a href="#cb17-79" aria-hidden="true" tabindex="-1"></a><span class="st"> --get last recruiting state</span></span>
<span id="cb17-80"><a href="#cb17-80" aria-hidden="true" tabindex="-1"></a><span class="st"> select fd.nct_id, max(fd.earliest_date_observed),min(fd2.earliest_date_observed) as tmstmp</span></span>
<span id="cb17-81"><a href="#cb17-81" aria-hidden="true" tabindex="-1"></a><span class="st"> from formatted_data fd </span></span>
<span id="cb17-82"><a href="#cb17-82" aria-hidden="true" tabindex="-1"></a><span class="st"> join formatted_data fd2 </span></span>
<span id="cb17-83"><a href="#cb17-83" aria-hidden="true" tabindex="-1"></a><span class="st"> on fd.nct_id=fd2.nct_id and fd.earliest_date_observed &lt; fd2.earliest_date_observed </span></span>
<span id="cb17-84"><a href="#cb17-84" aria-hidden="true" tabindex="-1"></a><span class="st"> where fd.current_status = 'Recruiting'</span></span>
<span id="cb17-85"><a href="#cb17-85" aria-hidden="true" tabindex="-1"></a><span class="st"> and fd2.current_status = 'Active, not recruiting'</span></span>
<span id="cb17-86"><a href="#cb17-86" aria-hidden="true" tabindex="-1"></a><span class="st"> group by fd.nct_id </span></span>
<span id="cb17-87"><a href="#cb17-87" aria-hidden="true" tabindex="-1"></a><span class="st"> )</span></span>
<span id="cb17-88"><a href="#cb17-88" aria-hidden="true" tabindex="-1"></a><span class="st"> select </span></span>
<span id="cb17-89"><a href="#cb17-89" aria-hidden="true" tabindex="-1"></a><span class="st"> fdqpe.nct_id</span></span>
<span id="cb17-90"><a href="#cb17-90" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.start_date</span></span>
<span id="cb17-91"><a href="#cb17-91" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.current_enrollment</span></span>
<span id="cb17-92"><a href="#cb17-92" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.enrollment_category</span></span>
<span id="cb17-93"><a href="#cb17-93" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.current_status </span></span>
<span id="cb17-94"><a href="#cb17-94" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.earliest_date_observed </span></span>
<span id="cb17-95"><a href="#cb17-95" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.elapsed_duration</span></span>
<span id="cb17-96"><a href="#cb17-96" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.n_brands as identical_brands</span></span>
<span id="cb17-97"><a href="#cb17-97" aria-hidden="true" tabindex="-1"></a><span class="st"> ,ntbtu.brand_name_counts </span></span>
<span id="cb17-98"><a href="#cb17-98" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.category_id</span></span>
<span id="cb17-99"><a href="#cb17-99" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.final_status</span></span>
<span id="cb17-100"><a href="#cb17-100" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.h_sdi_val</span></span>
<span id="cb17-101"><a href="#cb17-101" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.h_sdi_u95</span></span>
<span id="cb17-102"><a href="#cb17-102" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.h_sdi_l95</span></span>
<span id="cb17-103"><a href="#cb17-103" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.hm_sdi_val</span></span>
<span id="cb17-104"><a href="#cb17-104" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.hm_sdi_u95</span></span>
<span id="cb17-105"><a href="#cb17-105" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.hm_sdi_l95</span></span>
<span id="cb17-106"><a href="#cb17-106" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.m_sdi_val</span></span>
<span id="cb17-107"><a href="#cb17-107" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.m_sdi_u95</span></span>
<span id="cb17-108"><a href="#cb17-108" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.m_sdi_l95</span></span>
<span id="cb17-109"><a href="#cb17-109" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.lm_sdi_val</span></span>
<span id="cb17-110"><a href="#cb17-110" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.lm_sdi_u95</span></span>
<span id="cb17-111"><a href="#cb17-111" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.lm_sdi_l95</span></span>
<span id="cb17-112"><a href="#cb17-112" aria-hidden="true" tabindex="-1"></a><span class="st"> ,fdqpe.l_sdi_val</span></span>
<span id="cb17-113"><a href="#cb17-113" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.l_sdi_u95</span></span>
<span id="cb17-114"><a href="#cb17-114" aria-hidden="true" tabindex="-1"></a><span class="st"> --,fdqpe.l_sdi_l95</span></span>
<span id="cb17-115"><a href="#cb17-115" aria-hidden="true" tabindex="-1"></a><span class="st"> from formatted_data_with_planned_enrollment fdqpe</span></span>
<span id="cb17-116"><a href="#cb17-116" 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_brand_counts_through_uspdc ntbtu</span></span>
<span id="cb17-117"><a href="#cb17-117" aria-hidden="true" tabindex="-1"></a><span class="st"> on fdqpe.nct_id = ntbtu.nct_id </span></span>
<span id="cb17-118"><a href="#cb17-118" aria-hidden="true" tabindex="-1"></a><span class="st"> join cte </span></span>
<span id="cb17-119"><a href="#cb17-119" aria-hidden="true" tabindex="-1"></a><span class="st"> on fdqpe.nct_id = cte.nct_id </span></span>
<span id="cb17-120"><a href="#cb17-120" aria-hidden="true" tabindex="-1"></a><span class="st"> and fdqpe.earliest_date_observed = cte.tmstmp</span></span>
<span id="cb17-121"><a href="#cb17-121" aria-hidden="true" tabindex="-1"></a><span class="st"> order by fdqpe.nct_id, fdqpe.earliest_date_observed </span></span>
<span id="cb17-122"><a href="#cb17-122" aria-hidden="true" tabindex="-1"></a><span class="st"> ;</span></span>
<span id="cb17-123"><a href="#cb17-123" aria-hidden="true" tabindex="-1"></a><span class="st"> "</span></span>
<span id="cb17-124"><a href="#cb17-124" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb17-125"><a href="#cb17-125" 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="cb17-126"><a href="#cb17-126" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(df)</span>
<span id="cb17-127"><a href="#cb17-127" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-128"><a href="#cb17-128" 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="cb17-129"><a href="#cb17-129" 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="cb17-130"><a href="#cb17-130" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-131"><a href="#cb17-131" 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="cb17-132"><a href="#cb17-132" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb17-133"><a href="#cb17-133" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-134"><a href="#cb17-134" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-135"><a href="#cb17-135" aria-hidden="true" tabindex="-1"></a>d <span class="ot">&lt;-</span> <span class="fu">get_data</span>(driver)</span>
<span id="cb17-136"><a href="#cb17-136" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> d<span class="sc">$</span>data</span>
<span id="cb17-137"><a href="#cb17-137" aria-hidden="true" tabindex="-1"></a>n_categories <span class="ot">&lt;-</span> d<span class="sc">$</span>ncat</span>
<span id="cb17-138"><a href="#cb17-138" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-139"><a href="#cb17-139" aria-hidden="true" tabindex="-1"></a>cf <span class="ot">&lt;-</span> <span class="fu">get_counterfact_base</span>(driver)</span>
<span id="cb17-140"><a href="#cb17-140" aria-hidden="true" tabindex="-1"></a>df_counterfact_base <span class="ot">&lt;-</span> cf<span class="sc">$</span>data</span>
<span id="cb17-141"><a href="#cb17-141" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-142"><a href="#cb17-142" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-143"><a href="#cb17-143" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-144"><a href="#cb17-144" aria-hidden="true" tabindex="-1"></a><span class="do">################ Format Data ###########################</span></span>
<span id="cb17-145"><a href="#cb17-145" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-146"><a href="#cb17-146" aria-hidden="true" tabindex="-1"></a>data_formatter <span class="ot">&lt;-</span> <span class="cf">function</span>(df) {</span>
<span id="cb17-147"><a href="#cb17-147" aria-hidden="true" tabindex="-1"></a>categories <span class="ot">&lt;-</span> df[<span class="st">"category_id"</span>]</span>
<span id="cb17-148"><a href="#cb17-148" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-149"><a href="#cb17-149" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> df[<span class="st">"elapsed_duration"</span>]</span>
<span id="cb17-150"><a href="#cb17-150" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"identical_brands"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>identical_brands)</span>
<span id="cb17-151"><a href="#cb17-151" 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">$</span>brand_name_count)</span>
<span id="cb17-152"><a href="#cb17-152" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"h_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>h_sdi_val)</span>
<span id="cb17-153"><a href="#cb17-153" 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">$</span>hm_sdi_val)</span>
<span id="cb17-154"><a href="#cb17-154" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"m_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>m_sdi_val)</span>
<span id="cb17-155"><a href="#cb17-155" 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">$</span>lm_sdi_val)</span>
<span id="cb17-156"><a href="#cb17-156" aria-hidden="true" tabindex="-1"></a>x[<span class="st">"l_sdi_val"</span>] <span class="ot">&lt;-</span> <span class="fu">asinh</span>(df<span class="sc">$</span>l_sdi_val)</span>
<span id="cb17-157"><a href="#cb17-157" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-158"><a href="#cb17-158" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-159"><a href="#cb17-159" aria-hidden="true" tabindex="-1"></a><span class="co">#Setup fixed effects</span></span>
<span id="cb17-160"><a href="#cb17-160" 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="cb17-161"><a href="#cb17-161" 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="cb17-162"><a href="#cb17-162" 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="cb17-163"><a href="#cb17-163" 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="cb17-164"><a href="#cb17-164" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-165"><a href="#cb17-165" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-166"><a href="#cb17-166" 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="cb17-167"><a href="#cb17-167" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-168"><a href="#cb17-168" aria-hidden="true" tabindex="-1"></a><span class="co">#get category list</span></span>
<span id="cb17-169"><a href="#cb17-169" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-170"><a href="#cb17-170" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-171"><a href="#cb17-171" 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="cb17-172"><a href="#cb17-172" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb17-173"><a href="#cb17-173" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-174"><a href="#cb17-174" aria-hidden="true" tabindex="-1"></a>train <span class="ot">&lt;-</span> <span class="fu">data_formatter</span>(df)</span>
<span id="cb17-175"><a href="#cb17-175" aria-hidden="true" tabindex="-1"></a>counterfact_base <span class="ot">&lt;-</span> <span class="fu">data_formatter</span>(df_counterfact_base)</span>
<span id="cb17-176"><a href="#cb17-176" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-177"><a href="#cb17-177" aria-hidden="true" tabindex="-1"></a>categories <span class="ot">&lt;-</span> df<span class="sc">$</span>category_id</span>
<span id="cb17-178"><a href="#cb17-178" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-179"><a href="#cb17-179" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> train<span class="sc">$</span>x</span>
<span id="cb17-180"><a href="#cb17-180" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> train<span class="sc">$</span>y</span>
<span id="cb17-181"><a href="#cb17-181" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-182"><a href="#cb17-182" aria-hidden="true" tabindex="-1"></a>x_cf_base <span class="ot">&lt;-</span> counterfact_base<span class="sc">$</span>x</span>
<span id="cb17-183"><a href="#cb17-183" aria-hidden="true" tabindex="-1"></a>y_cf_base <span class="ot">&lt;-</span> counterfact_base<span class="sc">$</span>y</span>
<span id="cb17-184"><a href="#cb17-184" aria-hidden="true" tabindex="-1"></a>cf_categories <span class="ot">&lt;-</span> df_counterfact_base<span class="sc">$</span>category_id</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 class="co"># </span><span class="al">TODO</span><span class="co">: may need to remove</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>
<span id="cb19-82"><a href="#cb19-82" aria-hidden="true" tabindex="-1"></a> <span class="co">#get all parameter names</span></span>
<span id="cb19-83"><a href="#cb19-83" aria-hidden="true" tabindex="-1"></a> params <span class="ot">&lt;-</span> <span class="fu">get_parameters</span>(stem,class_list)</span>
<span id="cb19-84"><a href="#cb19-84" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-85"><a href="#cb19-85" aria-hidden="true" tabindex="-1"></a> <span class="co">#filter down to parameters of interest</span></span>
<span id="cb19-86"><a href="#cb19-86" 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="cb19-87"><a href="#cb19-87" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get dataframe with only the rows of interest</span></span>
<span id="cb19-88"><a href="#cb19-88" 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="cb19-89"><a href="#cb19-89" aria-hidden="true" tabindex="-1"></a> <span class="co">#rename columns</span></span>
<span id="cb19-90"><a href="#cb19-90" 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">$</span>parameters_hr</span>
<span id="cb19-91"><a href="#cb19-91" aria-hidden="true" tabindex="-1"></a> <span class="co">#get group name for title</span></span>
<span id="cb19-92"><a href="#cb19-92" aria-hidden="true" tabindex="-1"></a> group_name <span class="ot">&lt;-</span> class_list<span class="sc">$</span>groups[group_id]</span>
<span id="cb19-93"><a href="#cb19-93" aria-hidden="true" tabindex="-1"></a> <span class="co">#create area plot with appropriate title</span></span>
<span id="cb19-94"><a href="#cb19-94" 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="cb19-95"><a href="#cb19-95" 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="cb19-96"><a href="#cb19-96" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="fu">seq</span>(<span class="sc">-</span><span class="dv">2</span>,<span class="dv">2</span>,<span class="fl">0.5</span>),<span class="at">color=</span><span class="st">"grey"</span>,<span class="at">alpha=</span><span class="fl">0.750</span>) </span>
<span id="cb19-97"><a href="#cb19-97" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-98"><a href="#cb19-98" 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="cb19-99"><a href="#cb19-99" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(name) <span class="sc">|&gt;</span> </span>
<span id="cb19-100"><a href="#cb19-100" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(</span>
<span id="cb19-101"><a href="#cb19-101" aria-hidden="true" tabindex="-1"></a> <span class="at">mean=</span><span class="fu">mean</span>(value)</span>
<span id="cb19-102"><a href="#cb19-102" 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="cb19-103"><a href="#cb19-103" 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="cb19-104"><a href="#cb19-104" 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="cb19-105"><a href="#cb19-105" 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="cb19-106"><a href="#cb19-106" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb19-107"><a href="#cb19-107" 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="cb19-108"><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></span>
<span id="cb19-110"><a href="#cb19-110" aria-hidden="true" tabindex="-1"></a>parameter_mcmc_areas <span class="ot">&lt;-</span> <span class="cf">function</span>(</span>
<span id="cb19-111"><a href="#cb19-111" aria-hidden="true" tabindex="-1"></a> stem,<span class="co"># = "beta"</span></span>
<span id="cb19-112"><a href="#cb19-112" aria-hidden="true" tabindex="-1"></a> class_list,<span class="co"># = beta_list</span></span>
<span id="cb19-113"><a href="#cb19-113" aria-hidden="true" tabindex="-1"></a> stanfit,<span class="co"># = fit</span></span>
<span id="cb19-114"><a href="#cb19-114" aria-hidden="true" tabindex="-1"></a> parameter_id,<span class="co"># = 2</span></span>
<span id="cb19-115"><a href="#cb19-115" aria-hidden="true" tabindex="-1"></a> <span class="at">rename=</span><span class="cn">TRUE</span></span>
<span id="cb19-116"><a href="#cb19-116" aria-hidden="true" tabindex="-1"></a> ) {</span>
<span id="cb19-117"><a href="#cb19-117" aria-hidden="true" tabindex="-1"></a> <span class="co">#get all parameter names</span></span>
<span id="cb19-118"><a href="#cb19-118" aria-hidden="true" tabindex="-1"></a> params <span class="ot">&lt;-</span> <span class="fu">get_parameters</span>(stem,class_list)</span>
<span id="cb19-119"><a href="#cb19-119" aria-hidden="true" tabindex="-1"></a> <span class="co">#filter down to parameters of interest</span></span>
<span id="cb19-120"><a href="#cb19-120" 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="cb19-121"><a href="#cb19-121" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get dataframe with only the rows of interest</span></span>
<span id="cb19-122"><a href="#cb19-122" aria-hidden="true" tabindex="-1"></a> filtdata <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(stanfit)[params<span class="sc">$</span>param_name]</span>
<span id="cb19-123"><a href="#cb19-123" aria-hidden="true" tabindex="-1"></a> <span class="co">#rename columns</span></span>
<span id="cb19-124"><a href="#cb19-124" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (rename) <span class="fu">dimnames</span>(filtdata)[[<span class="dv">2</span>]] <span class="ot">&lt;-</span> params<span class="sc">$</span>groups_hr</span>
<span id="cb19-125"><a href="#cb19-125" aria-hidden="true" tabindex="-1"></a> <span class="co">#get group name for title</span></span>
<span id="cb19-126"><a href="#cb19-126" 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="cb19-127"><a href="#cb19-127" aria-hidden="true" tabindex="-1"></a> <span class="co">#create area plot with appropriate title</span></span>
<span id="cb19-128"><a href="#cb19-128" 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="cb19-129"><a href="#cb19-129" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggtitle</span>(parameter_name,<span class="st">"Parameter Distribution"</span>) <span class="sc">+</span></span>
<span id="cb19-130"><a href="#cb19-130" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="fu">seq</span>(<span class="sc">-</span><span class="dv">2</span>,<span class="dv">2</span>,<span class="fl">0.5</span>),<span class="at">color=</span><span class="st">"grey"</span>,<span class="at">alpha=</span><span class="fl">0.750</span>) </span>
<span id="cb19-131"><a href="#cb19-131" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb19-132"><a href="#cb19-132" 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="cb19-133"><a href="#cb19-133" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(name) <span class="sc">|&gt;</span> </span>
<span id="cb19-134"><a href="#cb19-134" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(</span>
<span id="cb19-135"><a href="#cb19-135" aria-hidden="true" tabindex="-1"></a> <span class="at">mean=</span><span class="fu">mean</span>(value)</span>
<span id="cb19-136"><a href="#cb19-136" 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="cb19-137"><a href="#cb19-137" 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="cb19-138"><a href="#cb19-138" 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="cb19-139"><a href="#cb19-139" 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="cb19-140"><a href="#cb19-140" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb19-141"><a href="#cb19-141" 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="cb19-142"><a href="#cb19-142" 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>
<p>Plan: select all snapshots that are the first to have closed enrollment (Rec -&gt; ANR)</p>
<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">#delay intervention</span></span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>intervention_enrollment <span class="ot">&lt;-</span> x_cf_base[<span class="fu">c</span>(inherited_cols,<span class="st">"brand_name_counts"</span>, <span class="st">"identical_brands"</span>)]</span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>intervention_enrollment[<span class="st">"status_ANR"</span>] <span class="ot">&lt;-</span> <span class="dv">0</span></span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>intervention_enrollment[<span class="st">"status_Rec"</span>] <span class="ot">&lt;-</span> <span class="dv">1</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_delay <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_cf_base),</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>(cf_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>(intervention_enrollment),</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_cf_base)</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_delay,</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>Warning: There were 2 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="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co">#get number of trials and snapshots in each category</span></span>
<span id="cb25-2"><a href="#cb25-2" 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="cb25-3"><a href="#cb25-3" 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="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-5"><a href="#cb25-5" 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>
<div class="cell">
<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="do">################################# DATA EXPLORATION ############################</span></span>
<span id="cb26-2"><a href="#cb26-2" 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="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>con <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(</span>
<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a> driver,</span>
<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a> <span class="at">user=</span><span class="st">'root'</span>,</span>
<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a> <span class="at">password=</span><span class="st">'root'</span>,</span>
<span id="cb26-8"><a href="#cb26-8" aria-hidden="true" tabindex="-1"></a> <span class="at">dbname=</span><span class="st">'aact_db'</span>,</span>
<span id="cb26-9"><a href="#cb26-9" aria-hidden="true" tabindex="-1"></a> <span class="at">host=</span>host</span>
<span id="cb26-10"><a href="#cb26-10" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-11"><a href="#cb26-11" aria-hidden="true" tabindex="-1"></a><span class="co">#Plot histogram of count of snapshots</span></span>
<span id="cb26-12"><a href="#cb26-12" aria-hidden="true" tabindex="-1"></a>df3 <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(</span>
<span id="cb26-13"><a href="#cb26-13" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb26-14"><a href="#cb26-14" 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="cb26-15"><a href="#cb26-15" aria-hidden="true" tabindex="-1"></a><span class="st"> group by nct_id,final_status ;"</span></span>
<span id="cb26-16"><a href="#cb26-16" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-17"><a href="#cb26-17" aria-hidden="true" tabindex="-1"></a><span class="co">#df3 &lt;- fetch(query3, n = -1)</span></span>
<span id="cb26-18"><a href="#cb26-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-19"><a href="#cb26-19" 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="cb26-20"><a href="#cb26-20" 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="cb26-21"><a href="#cb26-21" 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="cb26-22"><a href="#cb26-22" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-9-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" 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="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="co">#Plot duration for terminated vs completed</span></span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>df4 <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(</span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span></span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a><span class="st"> select </span></span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a><span class="st"> nct_id, </span></span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a><span class="st"> start_date , </span></span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date, </span></span>
<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a><span class="st"> overall_status ,</span></span>
<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date - start_date as duration</span></span>
<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a><span class="st"> from ctgov.studies s </span></span>
<span id="cb29-12"><a href="#cb29-12" 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="cb29-13"><a href="#cb29-13" aria-hidden="true" tabindex="-1"></a><span class="st"> ;"</span></span>
<span id="cb29-14"><a href="#cb29-14" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb29-15"><a href="#cb29-15" aria-hidden="true" tabindex="-1"></a><span class="co">#df4 &lt;- fetch(query4, n = -1)</span></span>
<span id="cb29-16"><a href="#cb29-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-17"><a href="#cb29-17" 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="cb29-18"><a href="#cb29-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>()<span class="sc">+</span></span>
<span id="cb29-19"><a href="#cb29-19" 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="cb29-20"><a href="#cb29-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"duration"</span>)<span class="sc">+</span></span>
<span id="cb29-21"><a href="#cb29-21" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-9-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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="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="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>df5 <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(</span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a> con,</span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span></span>
<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a><span class="st"> with cte1 as (</span></span>
<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a><span class="st"> select </span></span>
<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a><span class="st"> nct_id, </span></span>
<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a><span class="st"> start_date , </span></span>
<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date, </span></span>
<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a><span class="st"> overall_status ,</span></span>
<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a><span class="st"> primary_completion_date - start_date as duration</span></span>
<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a><span class="st"> from ctgov.studies s </span></span>
<span id="cb33-12"><a href="#cb33-12" 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="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a><span class="st"> ), cte2 as (</span></span>
<span id="cb33-14"><a href="#cb33-14" 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="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a><span class="st"> group by nct_id</span></span>
<span id="cb33-16"><a href="#cb33-16" aria-hidden="true" tabindex="-1"></a><span class="st"> )</span></span>
<span id="cb33-17"><a href="#cb33-17" 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="cb33-18"><a href="#cb33-18" aria-hidden="true" tabindex="-1"></a><span class="st"> from cte1 as a</span></span>
<span id="cb33-19"><a href="#cb33-19" aria-hidden="true" tabindex="-1"></a><span class="st"> join cte2 as b</span></span>
<span id="cb33-20"><a href="#cb33-20" aria-hidden="true" tabindex="-1"></a><span class="st"> on a.nct_id=b.nct_id</span></span>
<span id="cb33-21"><a href="#cb33-21" aria-hidden="true" tabindex="-1"></a><span class="st"> ;"</span></span>
<span id="cb33-22"><a href="#cb33-22" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb33-23"><a href="#cb33-23" 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="cb33-24"><a href="#cb33-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-25"><a href="#cb33-25" 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="cb33-26"><a href="#cb33-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_jitter</span>() <span class="sc">+</span></span>
<span id="cb33-27"><a href="#cb33-27" 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="cb33-28"><a href="#cb33-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"duration"</span>) <span class="sc">+</span></span>
<span id="cb33-29"><a href="#cb33-29" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-9-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" 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="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">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="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="co">#get number of trials and snapshots in each category</span></span>
<span id="cb38-2"><a href="#cb38-2" 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="cb38-3"><a href="#cb38-3" 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="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb38-5"><a href="#cb38-5" 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="cb38-6"><a href="#cb38-6" 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="cb38-7"><a href="#cb38-7" 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="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb38-9"><a href="#cb38-9" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"bar chart of trial categories"</span></span>
<span id="cb38-10"><a href="#cb38-10" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Category ID"</span></span>
<span id="cb38-11"><a href="#cb38-11" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Count"</span></span>
<span id="cb38-12"><a href="#cb38-12" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-9-4.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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="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="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">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>
</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="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="do">################################# ANALYZE #####################################</span></span>
<span id="cb44-2"><a href="#cb44-2" 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 -0.05 -0.02
mu[2] -0.01 0.00 0.05 -0.11 -0.05 -0.01
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 -0.07 -0.03
mu[7] -0.02 0.00 0.05 -0.11 -0.05 -0.02
mu[8] 0.00 0.00 0.05 -0.10 -0.03 0.00
mu[9] -0.01 0.00 0.05 -0.10 -0.04 -0.01
mu[10] 0.00 0.00 0.05 -0.10 -0.04 0.00
mu[11] 0.01 0.00 0.05 -0.09 -0.03 0.01
mu[12] -0.03 0.00 0.05 -0.13 -0.07 -0.04
sigma[1] 0.25 0.00 0.11 0.07 0.16 0.23
sigma[2] 0.71 0.00 0.16 0.42 0.59 0.70
sigma[3] 0.73 0.00 0.17 0.42 0.61 0.73
sigma[4] 0.29 0.00 0.09 0.15 0.23 0.28
sigma[5] 0.18 0.00 0.09 0.05 0.11 0.16
sigma[6] 0.18 0.00 0.09 0.05 0.12 0.17
sigma[7] 0.19 0.00 0.09 0.05 0.12 0.17
sigma[8] 0.19 0.00 0.09 0.06 0.12 0.17
sigma[9] 0.31 0.01 0.14 0.09 0.20 0.29
sigma[10] 0.20 0.00 0.10 0.05 0.13 0.19
sigma[11] 0.23 0.00 0.11 0.06 0.15 0.21
sigma[12] 0.29 0.01 0.13 0.09 0.20 0.28
beta[1,1] -0.08 0.00 0.23 -0.58 -0.21 -0.07
beta[1,2] -0.41 0.00 0.39 -1.17 -0.67 -0.40
beta[1,3] 0.68 0.00 0.39 -0.07 0.42 0.68
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] 0.04 0.00 0.18 -0.29 -0.08 0.02
beta[1,7] 0.07 0.00 0.17 -0.25 -0.04 0.06
beta[1,8] 0.07 0.00 0.16 -0.23 -0.03 0.06
beta[1,9] 0.31 0.01 0.37 -0.23 0.05 0.24
beta[1,10] -0.03 0.00 0.23 -0.53 -0.15 -0.02
beta[1,11] 0.02 0.00 0.23 -0.44 -0.11 0.02
beta[1,12] -0.24 0.00 0.28 -0.88 -0.40 -0.21
beta[2,1] -0.32 0.01 0.24 -0.87 -0.47 -0.29
beta[2,2] -1.42 0.00 0.26 -1.95 -1.60 -1.42
beta[2,3] 0.75 0.00 0.21 0.33 0.61 0.75
beta[2,4] 0.25 0.00 0.21 -0.14 0.10 0.24
beta[2,5] -0.07 0.00 0.18 -0.46 -0.17 -0.06
beta[2,6] -0.13 0.00 0.19 -0.56 -0.24 -0.11
beta[2,7] -0.09 0.00 0.18 -0.49 -0.20 -0.08
beta[2,8] 0.04 0.00 0.17 -0.29 -0.07 0.03
beta[2,9] -0.46 0.01 0.39 -1.38 -0.69 -0.39
beta[2,10] 0.00 0.00 0.23 -0.47 -0.12 -0.01
beta[2,11] -0.15 0.00 0.21 -0.65 -0.27 -0.12
beta[2,12] -0.39 0.01 0.28 -1.01 -0.57 -0.36
beta[3,1] -0.02 0.00 0.27 -0.59 -0.17 -0.02
beta[3,2] -0.08 0.01 0.73 -1.54 -0.55 -0.08
beta[3,3] -0.13 0.01 0.75 -1.67 -0.60 -0.11
beta[3,4] -0.18 0.00 0.27 -0.76 -0.35 -0.17
beta[3,5] -0.09 0.00 0.19 -0.52 -0.19 -0.08
beta[3,6] -0.10 0.00 0.20 -0.58 -0.20 -0.08
beta[3,7] -0.09 0.00 0.19 -0.53 -0.19 -0.07
beta[3,8] -0.07 0.00 0.20 -0.52 -0.17 -0.05
beta[3,9] 0.00 0.00 0.34 -0.71 -0.19 0.00
beta[3,10] 0.00 0.00 0.23 -0.48 -0.12 0.00
beta[3,11] 0.00 0.00 0.25 -0.52 -0.14 0.00
beta[3,12] -0.04 0.00 0.32 -0.71 -0.21 -0.04
beta[4,1] -0.04 0.00 0.26 -0.58 -0.18 -0.03
beta[4,2] -0.32 0.00 0.52 -1.39 -0.66 -0.31
beta[4,3] -0.78 0.01 0.58 -2.01 -1.14 -0.76
beta[4,4] 0.06 0.00 0.24 -0.40 -0.10 0.05
beta[4,5] -0.03 0.00 0.17 -0.38 -0.13 -0.03
beta[4,6] -0.07 0.00 0.18 -0.48 -0.17 -0.06
beta[4,7] 0.00 0.00 0.18 -0.38 -0.11 -0.01
beta[4,8] 0.08 0.00 0.19 -0.25 -0.04 0.06
beta[4,9] -0.13 0.00 0.34 -0.93 -0.29 -0.09
beta[4,10] -0.01 0.00 0.23 -0.52 -0.13 -0.01
beta[4,11] 0.21 0.01 0.29 -0.22 0.02 0.16
beta[4,12] -0.22 0.01 0.32 -0.97 -0.39 -0.18
beta[5,1] -0.09 0.00 0.27 -0.69 -0.23 -0.07
beta[5,2] -0.97 0.01 0.75 -2.61 -1.42 -0.90
beta[5,3] -0.18 0.01 0.75 -1.71 -0.65 -0.17
beta[5,4] 0.02 0.00 0.25 -0.47 -0.14 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 -0.16 -0.05
beta[5,7] 0.05 0.00 0.19 -0.30 -0.07 0.04
beta[5,8] 0.10 0.00 0.20 -0.25 -0.03 0.07
beta[5,9] 0.02 0.00 0.32 -0.65 -0.16 0.01
beta[5,10] -0.01 0.00 0.22 -0.50 -0.13 -0.01
beta[5,11] 0.09 0.00 0.25 -0.36 -0.06 0.06
beta[5,12] -0.20 0.01 0.32 -0.94 -0.37 -0.16
beta[6,1] -0.04 0.00 0.27 -0.61 -0.19 -0.04
beta[6,2] 1.43 0.01 0.71 0.21 0.92 1.38
beta[6,3] 2.04 0.01 0.73 0.71 1.54 2.01
beta[6,4] -0.35 0.00 0.24 -0.86 -0.51 -0.34
beta[6,5] -0.12 0.00 0.19 -0.57 -0.22 -0.10
beta[6,6] -0.08 0.00 0.19 -0.50 -0.18 -0.07
beta[6,7] -0.04 0.00 0.18 -0.43 -0.15 -0.04
beta[6,8] 0.00 0.00 0.18 -0.36 -0.10 0.00
beta[6,9] 0.01 0.00 0.33 -0.67 -0.17 0.00
beta[6,10] 0.00 0.00 0.23 -0.49 -0.13 0.00
beta[6,11] -0.03 0.00 0.25 -0.58 -0.16 -0.02
beta[6,12] -0.03 0.00 0.31 -0.64 -0.20 -0.03
beta[7,1] -0.03 0.00 0.26 -0.57 -0.18 -0.03
beta[7,2] -0.17 0.01 0.71 -1.62 -0.61 -0.15
beta[7,3] -0.19 0.01 0.75 -1.72 -0.65 -0.17
beta[7,4] -0.24 0.00 0.28 -0.85 -0.40 -0.23
beta[7,5] -0.12 0.00 0.20 -0.58 -0.22 -0.10
beta[7,6] -0.12 0.00 0.20 -0.59 -0.22 -0.10
beta[7,7] -0.10 0.00 0.20 -0.56 -0.21 -0.08
beta[7,8] -0.09 0.00 0.21 -0.59 -0.20 -0.06
beta[7,9] 0.00 0.00 0.34 -0.70 -0.19 -0.01
beta[7,10] 0.00 0.00 0.23 -0.49 -0.13 0.00
beta[7,11] 0.00 0.00 0.26 -0.55 -0.14 0.00
beta[7,12] -0.04 0.00 0.32 -0.72 -0.22 -0.04
beta[8,1] -0.02 0.00 0.27 -0.60 -0.17 -0.02
beta[8,2] 0.00 0.01 0.74 -1.47 -0.47 0.00
beta[8,3] 0.00 0.01 0.75 -1.50 -0.49 0.00
beta[8,4] -0.05 0.00 0.31 -0.66 -0.24 -0.05
beta[8,5] -0.03 0.00 0.20 -0.44 -0.14 -0.04
beta[8,6] -0.03 0.00 0.21 -0.45 -0.14 -0.03
beta[8,7] -0.01 0.00 0.21 -0.44 -0.13 -0.02
beta[8,8] 0.00 0.00 0.21 -0.43 -0.11 0.00
beta[8,9] 0.00 0.00 0.34 -0.71 -0.18 -0.01
beta[8,10] 0.00 0.00 0.23 -0.47 -0.12 0.00
beta[8,11] 0.01 0.00 0.27 -0.54 -0.13 0.01
beta[8,12] -0.03 0.00 0.32 -0.68 -0.21 -0.04
beta[9,1] -0.04 0.00 0.26 -0.58 -0.18 -0.04
beta[9,2] -0.49 0.01 0.65 -1.91 -0.88 -0.45
beta[9,3] -0.63 0.01 0.68 -2.09 -1.05 -0.59
beta[9,4] 0.00 0.00 0.25 -0.51 -0.16 0.00
beta[9,5] 0.03 0.00 0.19 -0.32 -0.09 0.01
beta[9,6] 0.08 0.00 0.20 -0.26 -0.05 0.05
beta[9,7] 0.10 0.00 0.20 -0.23 -0.03 0.08
beta[9,8] 0.11 0.00 0.20 -0.25 -0.02 0.08
beta[9,9] 0.05 0.00 0.34 -0.59 -0.15 0.03
beta[9,10] 0.00 0.00 0.23 -0.49 -0.13 0.00
beta[9,11] -0.05 0.00 0.26 -0.63 -0.18 -0.03
beta[9,12] 0.00 0.00 0.32 -0.64 -0.18 -0.01
beta[10,1] -0.03 0.00 0.27 -0.60 -0.17 -0.03
beta[10,2] -0.15 0.01 0.71 -1.57 -0.60 -0.15
beta[10,3] -0.14 0.01 0.74 -1.63 -0.60 -0.12
beta[10,4] -0.21 0.00 0.28 -0.82 -0.38 -0.20
beta[10,5] -0.10 0.00 0.19 -0.55 -0.20 -0.08
beta[10,6] -0.11 0.00 0.20 -0.58 -0.21 -0.09
beta[10,7] -0.10 0.00 0.20 -0.56 -0.20 -0.08
beta[10,8] -0.08 0.00 0.20 -0.54 -0.18 -0.06
beta[10,9] -0.01 0.00 0.34 -0.70 -0.19 -0.01
beta[10,10] 0.00 0.00 0.23 -0.48 -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 0.33 -0.71 -0.22 -0.05
beta[11,1] -0.03 0.00 0.28 -0.60 -0.18 -0.03
beta[11,2] -0.10 0.01 0.73 -1.59 -0.55 -0.09
beta[11,3] -0.10 0.01 0.75 -1.64 -0.56 -0.09
beta[11,4] -0.25 0.00 0.27 -0.81 -0.41 -0.23
beta[11,5] -0.12 0.00 0.20 -0.59 -0.22 -0.10
beta[11,6] -0.12 0.00 0.20 -0.57 -0.22 -0.10
beta[11,7] -0.10 0.00 0.20 -0.57 -0.21 -0.08
beta[11,8] -0.08 0.00 0.21 -0.56 -0.19 -0.06
beta[11,9] 0.00 0.00 0.34 -0.71 -0.18 0.00
beta[11,10] 0.00 0.00 0.23 -0.48 -0.12 0.00
beta[11,11] -0.01 0.00 0.26 -0.55 -0.14 0.00
beta[11,12] -0.03 0.00 0.32 -0.70 -0.21 -0.03
beta[12,1] -0.15 0.00 0.26 -0.72 -0.28 -0.11
beta[12,2] -0.48 0.01 0.66 -1.89 -0.89 -0.46
beta[12,3] 0.36 0.01 0.65 -0.92 -0.07 0.34
beta[12,4] -0.18 0.00 0.24 -0.70 -0.33 -0.17
beta[12,5] -0.07 0.00 0.18 -0.45 -0.16 -0.06
beta[12,6] 0.00 0.00 0.19 -0.35 -0.11 -0.01
beta[12,7] 0.01 0.00 0.18 -0.34 -0.10 0.00
beta[12,8] 0.04 0.00 0.19 -0.32 -0.07 0.04
beta[12,9] 0.04 0.00 0.34 -0.64 -0.15 0.02
beta[12,10] 0.00 0.00 0.24 -0.47 -0.12 0.00
beta[12,11] 0.05 0.00 0.26 -0.44 -0.09 0.04
beta[12,12] -0.15 0.00 0.32 -0.87 -0.32 -0.12
beta[13,1] 0.10 0.00 0.28 -0.38 -0.07 0.06
beta[13,2] 0.98 0.00 0.46 0.14 0.66 0.97
beta[13,3] -1.12 0.01 0.50 -2.12 -1.45 -1.11
beta[13,4] -0.08 0.00 0.24 -0.56 -0.24 -0.08
beta[13,5] -0.06 0.00 0.18 -0.45 -0.17 -0.06
beta[13,6] -0.03 0.00 0.18 -0.41 -0.13 -0.04
beta[13,7] 0.01 0.00 0.18 -0.35 -0.10 0.00
beta[13,8] 0.02 0.00 0.19 -0.35 -0.08 0.02
beta[13,9] -0.05 0.00 0.31 -0.74 -0.21 -0.03
beta[13,10] -0.01 0.00 0.22 -0.46 -0.13 0.00
beta[13,11] 0.12 0.00 0.25 -0.32 -0.04 0.09
beta[13,12] -0.25 0.01 0.32 -1.00 -0.42 -0.19
beta[14,1] -0.02 0.00 0.28 -0.58 -0.18 -0.02
beta[14,2] -0.19 0.01 0.72 -1.67 -0.63 -0.17
beta[14,3] -0.21 0.01 0.73 -1.72 -0.66 -0.19
beta[14,4] -0.18 0.00 0.28 -0.79 -0.34 -0.17
beta[14,5] -0.09 0.00 0.20 -0.54 -0.19 -0.08
beta[14,6] -0.09 0.00 0.20 -0.55 -0.20 -0.08
beta[14,7] -0.08 0.00 0.20 -0.53 -0.18 -0.06
beta[14,8] -0.06 0.00 0.21 -0.54 -0.17 -0.04
beta[14,9] 0.00 0.00 0.34 -0.70 -0.18 -0.01
beta[14,10] 0.00 0.00 0.24 -0.49 -0.13 0.00
beta[14,11] 0.01 0.00 0.26 -0.56 -0.13 0.01
beta[14,12] -0.04 0.00 0.32 -0.70 -0.21 -0.04
beta[15,1] -0.02 0.00 0.28 -0.58 -0.17 -0.02
beta[15,2] 0.00 0.01 0.72 -1.46 -0.46 -0.01
beta[15,3] 0.00 0.01 0.76 -1.55 -0.48 0.00
beta[15,4] -0.04 0.00 0.32 -0.66 -0.23 -0.04
beta[15,5] -0.04 0.00 0.21 -0.47 -0.14 -0.04
beta[15,6] -0.03 0.00 0.21 -0.48 -0.15 -0.03
beta[15,7] -0.02 0.00 0.21 -0.45 -0.14 -0.02
beta[15,8] 0.00 0.00 0.21 -0.44 -0.11 0.00
beta[15,9] 0.00 0.00 0.34 -0.71 -0.18 -0.01
beta[15,10] 0.00 0.00 0.24 -0.50 -0.13 0.00
beta[15,11] 0.01 0.00 0.26 -0.53 -0.13 0.01
beta[15,12] -0.03 0.00 0.33 -0.72 -0.21 -0.03
beta[16,1] -0.02 0.00 0.27 -0.58 -0.17 -0.03
beta[16,2] -0.02 0.01 0.71 -1.46 -0.46 -0.02
beta[16,3] 0.01 0.01 0.76 -1.50 -0.47 0.00
beta[16,4] -0.05 0.00 0.31 -0.67 -0.23 -0.05
beta[16,5] -0.04 0.00 0.20 -0.43 -0.14 -0.04
beta[16,6] -0.03 0.00 0.21 -0.46 -0.15 -0.03
beta[16,7] -0.02 0.00 0.21 -0.44 -0.13 -0.02
beta[16,8] 0.00 0.00 0.21 -0.45 -0.12 0.00
beta[16,9] -0.01 0.00 0.34 -0.70 -0.19 -0.01
beta[16,10] 0.00 0.00 0.23 -0.49 -0.12 0.00
beta[16,11] 0.01 0.00 0.26 -0.54 -0.13 0.00
beta[16,12] -0.03 0.00 0.32 -0.72 -0.21 -0.03
beta[17,1] -0.02 0.00 0.27 -0.59 -0.17 -0.02
beta[17,2] -0.11 0.01 0.72 -1.59 -0.56 -0.10
beta[17,3] -0.09 0.01 0.75 -1.65 -0.54 -0.08
beta[17,4] -0.20 0.00 0.28 -0.80 -0.37 -0.18
beta[17,5] -0.10 0.00 0.19 -0.55 -0.20 -0.09
beta[17,6] -0.11 0.00 0.21 -0.59 -0.21 -0.09
beta[17,7] -0.09 0.00 0.20 -0.54 -0.20 -0.07
beta[17,8] -0.08 0.00 0.20 -0.55 -0.18 -0.05
beta[17,9] -0.01 0.00 0.34 -0.73 -0.18 -0.01
beta[17,10] 0.00 0.00 0.23 -0.49 -0.13 0.00
beta[17,11] 0.00 0.00 0.26 -0.56 -0.13 0.01
beta[17,12] -0.04 0.00 0.32 -0.70 -0.22 -0.04
beta[18,1] -0.02 0.00 0.27 -0.58 -0.17 -0.02
beta[18,2] -0.07 0.01 0.72 -1.52 -0.53 -0.06
beta[18,3] -0.08 0.01 0.74 -1.58 -0.53 -0.07
beta[18,4] -0.17 0.00 0.28 -0.76 -0.33 -0.15
beta[18,5] -0.09 0.00 0.19 -0.52 -0.19 -0.07
beta[18,6] -0.09 0.00 0.20 -0.54 -0.19 -0.07
beta[18,7] -0.07 0.00 0.20 -0.53 -0.18 -0.06
beta[18,8] -0.06 0.00 0.20 -0.51 -0.16 -0.04
beta[18,9] -0.01 0.00 0.34 -0.73 -0.19 -0.01
beta[18,10] -0.01 0.00 0.23 -0.50 -0.13 0.00
beta[18,11] 0.00 0.00 0.26 -0.55 -0.13 0.01
beta[18,12] -0.04 0.00 0.32 -0.70 -0.21 -0.04
beta[19,1] -0.02 0.00 0.27 -0.58 -0.17 -0.02
beta[19,2] 0.00 0.01 0.73 -1.49 -0.47 -0.01
beta[19,3] 0.01 0.01 0.77 -1.55 -0.47 0.01
beta[19,4] -0.04 0.00 0.31 -0.66 -0.23 -0.05
beta[19,5] -0.04 0.00 0.20 -0.44 -0.15 -0.04
beta[19,6] -0.04 0.00 0.21 -0.48 -0.15 -0.03
beta[19,7] -0.02 0.00 0.21 -0.45 -0.13 -0.02
beta[19,8] 0.00 0.00 0.22 -0.44 -0.12 0.00
beta[19,9] -0.01 0.00 0.34 -0.73 -0.19 -0.01
beta[19,10] -0.01 0.00 0.24 -0.51 -0.13 -0.01
beta[19,11] 0.00 0.00 0.26 -0.54 -0.13 0.01
beta[19,12] -0.03 0.00 0.33 -0.70 -0.21 -0.04
beta[20,1] -0.02 0.00 0.28 -0.59 -0.17 -0.02
beta[20,2] -0.01 0.01 0.71 -1.44 -0.46 -0.02
beta[20,3] 0.01 0.01 0.76 -1.51 -0.46 0.00
beta[20,4] -0.05 0.00 0.31 -0.66 -0.24 -0.05
beta[20,5] -0.04 0.00 0.21 -0.46 -0.14 -0.04
beta[20,6] -0.03 0.00 0.21 -0.46 -0.15 -0.03
beta[20,7] -0.02 0.00 0.21 -0.46 -0.13 -0.02
beta[20,8] 0.00 0.00 0.21 -0.44 -0.11 0.00
beta[20,9] -0.01 0.00 0.35 -0.74 -0.19 -0.01
beta[20,10] 0.00 0.00 0.23 -0.48 -0.12 0.00
beta[20,11] 0.01 0.00 0.26 -0.55 -0.13 0.01
beta[20,12] -0.03 0.00 0.33 -0.70 -0.21 -0.04
beta[21,1] -0.02 0.00 0.27 -0.55 -0.17 -0.03
beta[21,2] -0.02 0.01 0.72 -1.45 -0.48 -0.02
beta[21,3] 0.00 0.01 0.75 -1.48 -0.47 0.01
beta[21,4] -0.04 0.00 0.31 -0.67 -0.24 -0.05
beta[21,5] -0.04 0.00 0.21 -0.46 -0.15 -0.04
beta[21,6] -0.03 0.00 0.21 -0.46 -0.14 -0.03
beta[21,7] -0.02 0.00 0.21 -0.46 -0.14 -0.02
beta[21,8] 0.00 0.00 0.21 -0.44 -0.12 0.00
beta[21,9] 0.00 0.00 0.34 -0.69 -0.19 -0.01
beta[21,10] 0.00 0.00 0.23 -0.46 -0.12 0.00
beta[21,11] 0.01 0.00 0.25 -0.54 -0.13 0.01
beta[21,12] -0.03 0.00 0.32 -0.69 -0.21 -0.03
beta[22,1] -0.02 0.00 0.28 -0.60 -0.17 -0.02
beta[22,2] -0.02 0.01 0.73 -1.50 -0.49 -0.02
beta[22,3] 0.00 0.01 0.75 -1.50 -0.47 0.00
beta[22,4] -0.05 0.00 0.31 -0.67 -0.24 -0.05
beta[22,5] -0.04 0.00 0.20 -0.45 -0.14 -0.04
beta[22,6] -0.03 0.00 0.20 -0.45 -0.15 -0.03
beta[22,7] -0.02 0.00 0.21 -0.45 -0.13 -0.02
beta[22,8] 0.00 0.00 0.21 -0.44 -0.12 0.00
beta[22,9] -0.01 0.00 0.34 -0.70 -0.18 -0.01
beta[22,10] 0.00 0.00 0.23 -0.48 -0.12 0.00
beta[22,11] 0.01 0.00 0.26 -0.54 -0.13 0.01
beta[22,12] -0.03 0.00 0.33 -0.70 -0.21 -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 -0.04 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 0.06 0.13 0.18
sigma_prior[3] 0.20 0.00 0.10 0.06 0.13 0.18
sigma_prior[4] 0.20 0.00 0.10 0.05 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 0.05 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 0.05 0.13 0.18
sigma_prior[11] 0.20 0.00 0.10 0.06 0.13 0.18
sigma_prior[12] 0.20 0.00 0.10 0.05 0.13 0.18
p_prior[1] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[2] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[3] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[4] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[5] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[6] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[7] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[8] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[9] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[10] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[11] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[12] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[13] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[14] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[15] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[16] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[17] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[18] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[19] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[20] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[21] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[22] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[23] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[24] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[25] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[26] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[27] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[28] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[29] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[30] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[31] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[32] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[33] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[34] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[35] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[36] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[37] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[38] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[39] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[40] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[41] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[42] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[43] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[44] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[45] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[46] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[47] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[48] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[49] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[50] 0.50 0.00 0.43 0.00 0.01 0.48
p_prior[51] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[52] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[53] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[54] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[55] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[56] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[57] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[58] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[59] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[60] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[61] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[62] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[63] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[64] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[65] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[66] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[67] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[68] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[69] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[70] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[71] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[72] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[73] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[74] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[75] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[76] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[77] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[78] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[79] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[80] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[81] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[82] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[83] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[84] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[85] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[86] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[87] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[88] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[89] 0.49 0.00 0.45 0.00 0.01 0.47
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 0.48
p_prior[94] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[95] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[96] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[97] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[98] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[99] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[100] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[101] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[102] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[103] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[104] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[105] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[106] 0.50 0.00 0.44 0.00 0.01 0.51
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 0.51
p_prior[110] 0.50 0.00 0.44 0.00 0.01 0.51
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] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[118] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[119] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[120] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[121] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[122] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[123] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[124] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[125] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[126] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[127] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[128] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[129] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[130] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[131] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[132] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[133] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[134] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[135] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[136] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[137] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[138] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[139] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[140] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[141] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[142] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[143] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[144] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[145] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[146] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[147] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[148] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[149] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[150] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[151] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[152] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[153] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[154] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[155] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[156] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[157] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[158] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[159] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[160] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[161] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[162] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[163] 0.50 0.00 0.17 0.16 0.39 0.50
p_prior[164] 0.50 0.00 0.17 0.16 0.39 0.50
p_prior[165] 0.50 0.00 0.17 0.16 0.38 0.50
p_prior[166] 0.50 0.00 0.17 0.16 0.38 0.50
p_prior[167] 0.50 0.00 0.17 0.16 0.38 0.50
p_prior[168] 0.50 0.00 0.17 0.16 0.38 0.50
p_prior[169] 0.50 0.00 0.18 0.15 0.38 0.50
p_prior[170] 0.50 0.00 0.18 0.15 0.38 0.50
p_prior[171] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[172] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[173] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[174] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[175] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[176] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[177] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[178] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[179] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[180] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[181] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[182] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[183] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[184] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[185] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[186] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[187] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[188] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[189] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[190] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[191] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[192] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[193] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[194] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[195] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[196] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[197] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[198] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[199] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[200] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[201] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[202] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[203] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[204] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[205] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[206] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[207] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[208] 0.49 0.00 0.44 0.00 0.01 0.49
p_prior[209] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[210] 0.49 0.00 0.44 0.00 0.01 0.49
p_prior[211] 0.49 0.00 0.44 0.00 0.01 0.49
p_prior[212] 0.49 0.00 0.44 0.00 0.01 0.49
p_prior[213] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[214] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[215] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[216] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[217] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[218] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[219] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[220] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[221] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[222] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[223] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[224] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[225] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[226] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[227] 0.50 0.00 0.45 0.00 0.00 0.47
p_prior[228] 0.50 0.00 0.45 0.00 0.00 0.47
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 0.47
p_prior[231] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[232] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[233] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[234] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[235] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[236] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[237] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[238] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[239] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[240] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[241] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[242] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[243] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[244] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[245] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[246] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[247] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[248] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[249] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[250] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[251] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[252] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[253] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[254] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[255] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[256] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[257] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[258] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[259] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[260] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[261] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[262] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[263] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[264] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[265] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[266] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[267] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[268] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[269] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[270] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[271] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[272] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[273] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[274] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[275] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[276] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[277] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[278] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[279] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[280] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[281] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[282] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[283] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[284] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[285] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[286] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[287] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[288] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[289] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[290] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[291] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[292] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[293] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[294] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[295] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[296] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[297] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[298] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[299] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[300] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[301] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[302] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[303] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[304] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[305] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[306] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[307] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[308] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[309] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[310] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[311] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[312] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[313] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[314] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[315] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[316] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[317] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[318] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[319] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[320] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[321] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[322] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[323] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[324] 0.49 0.00 0.44 0.00 0.01 0.49
p_prior[325] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[326] 0.49 0.00 0.44 0.00 0.01 0.49
p_prior[327] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[328] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[329] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[330] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[331] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[332] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[333] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[334] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[335] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[336] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[337] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[338] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[339] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[340] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[341] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[342] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[343] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[344] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[345] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[346] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[347] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[348] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[349] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[350] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[351] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[352] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[353] 0.50 0.00 0.45 0.00 0.00 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 0.50
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 0.50
p_prior[358] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[359] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[360] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[361] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[362] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[363] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[364] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[365] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[366] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[367] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[368] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[369] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[370] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[371] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[372] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[373] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[374] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[375] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[376] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[377] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[378] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[379] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[380] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[381] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[382] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[383] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[384] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[385] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[386] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[387] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[388] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[389] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[390] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[391] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[392] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[393] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[394] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[395] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[396] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[397] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[398] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[399] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[400] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[401] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[402] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[403] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[404] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[405] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[406] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[407] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[408] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[409] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[410] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[411] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[412] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[413] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[414] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[415] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[416] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[417] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[418] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[419] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[420] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[421] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[422] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[423] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[424] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[425] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[426] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[427] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[428] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[429] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[430] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[431] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[432] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[433] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[434] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[435] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[436] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[437] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[438] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[439] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[440] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[441] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[442] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[443] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[444] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[445] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[446] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[447] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[448] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[449] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[450] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[451] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[452] 0.49 0.00 0.45 0.00 0.01 0.46
p_prior[453] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[454] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[455] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[456] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[457] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[458] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[459] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[460] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[461] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[462] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[463] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[464] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[465] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[466] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[467] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[468] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[469] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[470] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[471] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[472] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[473] 0.50 0.00 0.15 0.21 0.40 0.50
p_prior[474] 0.50 0.00 0.15 0.20 0.40 0.50
p_prior[475] 0.50 0.00 0.17 0.18 0.39 0.50
p_prior[476] 0.50 0.00 0.17 0.16 0.38 0.50
p_prior[477] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[478] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[479] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[480] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[481] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[482] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[483] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[484] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[485] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[486] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[487] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[488] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[489] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[490] 0.50 0.00 0.45 0.00 0.00 0.49
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 0.12 0.26 0.42 0.50
p_prior[498] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[499] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[500] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[501] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[502] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[503] 0.50 0.00 0.13 0.25 0.42 0.50
p_prior[504] 0.50 0.00 0.14 0.21 0.40 0.50
p_prior[505] 0.50 0.00 0.15 0.21 0.40 0.50
p_prior[506] 0.50 0.00 0.16 0.19 0.39 0.50
p_prior[507] 0.50 0.00 0.17 0.17 0.38 0.50
p_prior[508] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[509] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[510] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[511] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[512] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[513] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[514] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[515] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[516] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[517] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[518] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[519] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[520] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[521] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[522] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[523] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[524] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[525] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[526] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[527] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[528] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[529] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[530] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[531] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[532] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[533] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[534] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[535] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[536] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[537] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[538] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[539] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[540] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[541] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[542] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[543] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[544] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[545] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[546] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[547] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[548] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[549] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[550] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[551] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[552] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[553] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[554] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[555] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[556] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[557] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[558] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[559] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[560] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[561] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[562] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[563] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[564] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[565] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[566] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[567] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[568] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[569] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[570] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[571] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[572] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[573] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[574] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[575] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[576] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[577] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[578] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[579] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[580] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[581] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[582] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[583] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[584] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[585] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[586] 0.50 0.00 0.45 0.00 0.00 0.50
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 0.50
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 0.50
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 0.50
p_prior[593] 0.50 0.00 0.10 0.29 0.43 0.50
p_prior[594] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[595] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[596] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[597] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[598] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[599] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[600] 0.50 0.00 0.13 0.24 0.41 0.50
p_prior[601] 0.50 0.00 0.14 0.22 0.40 0.50
p_prior[602] 0.50 0.00 0.16 0.18 0.39 0.50
p_prior[603] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[604] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[605] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[606] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[607] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[608] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[609] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[610] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[611] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[612] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[613] 0.50 0.00 0.10 0.30 0.44 0.50
p_prior[614] 0.50 0.00 0.10 0.30 0.44 0.50
p_prior[615] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[616] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[617] 0.50 0.00 0.45 0.00 0.01 0.51
p_prior[618] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[619] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[620] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[621] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[622] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[623] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[624] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[625] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[626] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[627] 0.49 0.00 0.45 0.00 0.01 0.48
p_prior[628] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[629] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[630] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[631] 0.49 0.00 0.45 0.00 0.01 0.47
p_prior[632] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[633] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[634] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[635] 0.50 0.00 0.45 0.00 0.00 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 0.50
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 0.50
p_prior[640] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[641] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[642] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[643] 0.50 0.00 0.45 0.00 0.00 0.50
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 0.50
p_prior[646] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[647] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[648] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[649] 0.50 0.00 0.42 0.00 0.03 0.50
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 0.45 0.00 0.01 0.49
p_prior[653] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[654] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[655] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[656] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[657] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[658] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[659] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[660] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[661] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[662] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[663] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[664] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[665] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[666] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[667] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[668] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[669] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[670] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[671] 0.50 0.00 0.44 0.00 0.01 0.49
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 0.50
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 0.50
p_prior[677] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[678] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[679] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[680] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[681] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[682] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[683] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[684] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[685] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[686] 0.50 0.00 0.45 0.00 0.00 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 0.45 0.00 0.00 0.48
p_prior[690] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[691] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[692] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[693] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[694] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[695] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[696] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[697] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[698] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[699] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[700] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[701] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[702] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[703] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[704] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[705] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[706] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[707] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[708] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[709] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[710] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[711] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[712] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[713] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[714] 0.50 0.00 0.12 0.25 0.42 0.50
p_prior[715] 0.50 0.00 0.13 0.23 0.41 0.50
p_prior[716] 0.50 0.00 0.15 0.21 0.40 0.50
p_prior[717] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[718] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[719] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[720] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[721] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[722] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[723] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[724] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[725] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[726] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[727] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[728] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[729] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[730] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[731] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[732] 0.50 0.00 0.44 0.00 0.01 0.49
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 0.49
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 0.49
p_prior[739] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[740] 0.50 0.00 0.44 0.00 0.01 0.50
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] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[744] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[745] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[746] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[747] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[748] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[749] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[750] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[751] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[752] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[753] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[754] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[755] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[756] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[757] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[758] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[759] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[760] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[761] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[762] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[763] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[764] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[765] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[766] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[767] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[768] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[769] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[770] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[771] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[772] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[773] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[774] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[775] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[776] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[777] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[778] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[779] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[780] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[781] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[782] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[783] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[784] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[785] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[786] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[787] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[788] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[789] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[790] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[791] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[792] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[793] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[794] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[795] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[796] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[797] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[798] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[799] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[800] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[801] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[802] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[803] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[804] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[805] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[806] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[807] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[808] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[809] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[810] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[811] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[812] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[813] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[814] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[815] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[816] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[817] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[818] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[819] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[820] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[821] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[822] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[823] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[824] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[825] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[826] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[827] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[828] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[829] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[830] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[831] 0.50 0.00 0.45 0.00 0.00 0.50
p_prior[832] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[833] 0.50 0.00 0.17 0.17 0.39 0.50
p_prior[834] 0.49 0.00 0.43 0.00 0.01 0.49
p_prior[835] 0.50 0.00 0.17 0.17 0.39 0.50
p_prior[836] 0.49 0.00 0.43 0.00 0.01 0.49
p_prior[837] 0.50 0.00 0.17 0.17 0.39 0.50
p_prior[838] 0.49 0.00 0.43 0.00 0.01 0.49
p_prior[839] 0.50 0.00 0.17 0.17 0.39 0.50
p_prior[840] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[841] 0.50 0.00 0.18 0.15 0.38 0.50
p_prior[842] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[843] 0.50 0.00 0.18 0.15 0.38 0.50
p_prior[844] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[845] 0.50 0.00 0.18 0.14 0.38 0.50
p_prior[846] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[847] 0.50 0.00 0.18 0.15 0.37 0.50
p_prior[848] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[849] 0.50 0.00 0.19 0.14 0.37 0.50
p_prior[850] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[851] 0.50 0.00 0.19 0.13 0.36 0.50
p_prior[852] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[853] 0.50 0.00 0.20 0.11 0.35 0.50
p_prior[854] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[855] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[856] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[857] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[858] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[859] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[860] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[861] 0.50 0.00 0.12 0.27 0.42 0.50
p_prior[862] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[863] 0.50 0.00 0.12 0.27 0.42 0.50
p_prior[864] 0.50 0.00 0.12 0.27 0.42 0.50
p_prior[865] 0.50 0.00 0.11 0.27 0.43 0.50
p_prior[866] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[867] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[868] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[869] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[870] 0.49 0.00 0.44 0.00 0.01 0.46
p_prior[871] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[872] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[873] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[874] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[875] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[876] 0.50 0.00 0.45 0.00 0.01 0.48
p_prior[877] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[878] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[879] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[880] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[881] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[882] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[883] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[884] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[885] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[886] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[887] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[888] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[889] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[890] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[891] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[892] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[893] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[894] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[895] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[896] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[897] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[898] 0.50 0.00 0.12 0.26 0.42 0.50
p_prior[899] 0.50 0.00 0.13 0.23 0.41 0.50
p_prior[900] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[901] 0.50 0.00 0.14 0.22 0.41 0.50
p_prior[902] 0.50 0.00 0.15 0.20 0.40 0.50
p_prior[903] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[904] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[905] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[906] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[907] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[908] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[909] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[910] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[911] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[912] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[913] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[914] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[915] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[916] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[917] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[918] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[919] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[920] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[921] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[922] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[923] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[924] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[925] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[926] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[927] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[928] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[929] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[930] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[931] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[932] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[933] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[934] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[935] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[936] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[937] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[938] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[939] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[940] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[941] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[942] 0.49 0.00 0.44 0.00 0.01 0.48
p_prior[943] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[944] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[945] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[946] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[947] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[948] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[949] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[950] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[951] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[952] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[953] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[954] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[955] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[956] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[957] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[958] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[959] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[960] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[961] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[962] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[963] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[964] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[965] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[966] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[967] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[968] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[969] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[970] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[971] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[972] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[973] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[974] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[975] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[976] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[977] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[978] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[979] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[980] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[981] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[982] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[983] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[984] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[985] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[986] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[987] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[988] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[989] 0.49 0.00 0.45 0.00 0.00 0.45
p_prior[990] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[991] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[992] 0.51 0.00 0.44 0.00 0.01 0.51
p_prior[993] 0.51 0.00 0.44 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 0.44 0.00 0.01 0.50
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 0.50
p_prior[998] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[999] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1000] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1001] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1002] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1003] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1004] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1005] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1006] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1007] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1008] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1009] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1010] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1011] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1012] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1013] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1014] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1015] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1016] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1017] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1018] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1019] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1020] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1021] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1022] 0.50 0.00 0.44 0.00 0.01 0.49
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 0.48
p_prior[1025] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1026] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1027] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1028] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1029] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1030] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1031] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1032] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1033] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1034] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1035] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1036] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1037] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1038] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1039] 0.50 0.00 0.44 0.00 0.01 0.49
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 0.49
p_prior[1048] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1049] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1050] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1051] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1052] 0.50 0.00 0.43 0.00 0.01 0.50
p_prior[1053] 0.50 0.00 0.43 0.00 0.01 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 0.49
p_prior[1057] 0.50 0.00 0.43 0.00 0.01 0.49
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 0.01 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 0.49
p_prior[1066] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1067] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[1068] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[1069] 0.49 0.00 0.44 0.00 0.01 0.45
p_prior[1070] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1071] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1072] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1073] 0.50 0.00 0.42 0.00 0.03 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 0.50
p_prior[1077] 0.50 0.00 0.42 0.00 0.03 0.50
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 0.13 0.24 0.41 0.50
p_prior[1081] 0.50 0.00 0.13 0.23 0.42 0.50
p_prior[1082] 0.50 0.00 0.13 0.23 0.41 0.50
p_prior[1083] 0.50 0.00 0.13 0.23 0.41 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 0.21 0.40 0.50
p_prior[1086] 0.50 0.00 0.15 0.21 0.40 0.50
p_prior[1087] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1088] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1089] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1090] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1091] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1092] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1093] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1094] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1095] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1096] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1097] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1098] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1099] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1100] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1101] 0.50 0.00 0.43 0.00 0.01 0.49
p_prior[1102] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1103] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1104] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1105] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1106] 0.49 0.00 0.43 0.00 0.01 0.47
p_prior[1107] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1108] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1109] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1110] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1111] 0.49 0.00 0.43 0.00 0.01 0.48
p_prior[1112] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[1113] 0.50 0.00 0.11 0.29 0.43 0.50
p_prior[1114] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[1115] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[1116] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[1117] 0.50 0.00 0.11 0.27 0.43 0.50
p_prior[1118] 0.50 0.00 0.11 0.27 0.42 0.50
p_prior[1119] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[1120] 0.50 0.00 0.11 0.27 0.43 0.50
p_prior[1121] 0.50 0.00 0.11 0.28 0.43 0.50
p_prior[1122] 0.50 0.00 0.12 0.27 0.42 0.50
p_prior[1123] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1124] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1125] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1126] 0.50 0.00 0.42 0.00 0.02 0.49
p_prior[1127] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1128] 0.50 0.00 0.44 0.00 0.01 0.51
p_prior[1129] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[1130] 0.49 0.00 0.44 0.00 0.01 0.47
p_prior[1131] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1132] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1133] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1134] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1135] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1136] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1137] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1138] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1139] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1140] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1141] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1142] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1143] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1144] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1145] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1146] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1147] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1148] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1149] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1150] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1151] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1152] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1153] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1154] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1155] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1156] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1157] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1158] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1159] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1160] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1161] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1162] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1163] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1164] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1165] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1166] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1167] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1168] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1169] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1170] 0.51 0.00 0.45 0.00 0.00 0.50
p_prior[1171] 0.51 0.00 0.45 0.00 0.00 0.50
p_prior[1172] 0.51 0.00 0.45 0.00 0.00 0.50
p_prior[1173] 0.51 0.00 0.45 0.00 0.00 0.50
p_prior[1174] 0.51 0.00 0.45 0.00 0.00 0.50
p_prior[1175] 0.50 0.00 0.18 0.14 0.37 0.50
p_prior[1176] 0.50 0.00 0.18 0.14 0.37 0.50
p_prior[1177] 0.50 0.00 0.19 0.13 0.36 0.50
p_prior[1178] 0.50 0.00 0.19 0.13 0.37 0.50
p_prior[1179] 0.50 0.00 0.19 0.13 0.36 0.50
p_prior[1180] 0.50 0.00 0.20 0.12 0.36 0.50
p_prior[1181] 0.50 0.00 0.20 0.11 0.35 0.50
p_prior[1182] 0.51 0.00 0.43 0.00 0.02 0.51
p_prior[1183] 0.51 0.00 0.43 0.00 0.02 0.51
p_prior[1184] 0.51 0.00 0.43 0.00 0.01 0.51
p_prior[1185] 0.50 0.00 0.44 0.00 0.01 0.48
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 0.49
p_prior[1190] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1191] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1192] 0.50 0.00 0.44 0.00 0.01 0.49
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 0.49
p_prior[1195] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1196] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1197] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1198] 0.50 0.00 0.44 0.00 0.01 0.48
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 0.49
p_prior[1204] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1205] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[1206] 0.49 0.00 0.45 0.00 0.00 0.46
p_prior[1207] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1208] 0.50 0.00 0.44 0.00 0.01 0.49
p_prior[1209] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1210] 0.50 0.00 0.44 0.00 0.01 0.50
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 0.42 0.00 0.03 0.49
p_prior[1214] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1215] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1216] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1217] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1218] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1219] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1220] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1221] 0.50 0.00 0.45 0.00 0.01 0.50
p_prior[1222] 0.50 0.00 0.45 0.00 0.01 0.49
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 0.49
p_prior[1225] 0.49 0.00 0.44 0.00 0.00 0.45
p_prior[1226] 0.49 0.00 0.44 0.00 0.00 0.45
p_prior[1227] 0.49 0.00 0.44 0.00 0.00 0.46
p_prior[1228] 0.49 0.00 0.44 0.00 0.00 0.46
p_prior[1229] 0.50 0.00 0.09 0.32 0.45 0.50
p_prior[1230] 0.50 0.00 0.09 0.32 0.45 0.50
p_prior[1231] 0.50 0.00 0.09 0.32 0.44 0.50
p_prior[1232] 0.50 0.00 0.09 0.32 0.44 0.50
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 0.10 0.31 0.44 0.50
p_prior[1238] 0.50 0.00 0.10 0.31 0.44 0.50
p_prior[1239] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1240] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1241] 0.50 0.00 0.10 0.30 0.43 0.50
p_prior[1242] 0.50 0.00 0.10 0.30 0.43 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 0.44 0.00 0.01 0.48
p_prior[1246] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1247] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1248] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1249] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1250] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1251] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1252] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1253] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1254] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1255] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1256] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1257] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1258] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1259] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1260] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1261] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1262] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1263] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1264] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1265] 0.50 0.00 0.44 0.00 0.01 0.50
p_prior[1266] 0.50 0.00 0.44 0.00 0.01 0.50
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 0.48
p_prior[1269] 0.50 0.00 0.44 0.00 0.01 0.48
p_prior[1270] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1271] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1272] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1273] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1274] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[1275] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[1276] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[1277] 0.50 0.00 0.45 0.00 0.01 0.49
p_prior[1278] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[1279] 0.49 0.00 0.43 0.00 0.02 0.47
p_prior[1280] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1281] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1282] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1283] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1284] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1285] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1286] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1287] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1288] 0.49 0.00 0.43 0.00 0.02 0.48
p_prior[1289] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1290] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1291] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1292] 0.50 0.00 0.43 0.00 0.02 0.49
p_prior[1293] 0.50 0.00 0.45 0.00 0.00 0.49
p_prior[1294] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1295] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1296] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1297] 0.50 0.00 0.45 0.00 0.00 0.48
p_prior[1298] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1299] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1300] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1301] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1302] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1303] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1304] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1305] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1306] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1307] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1308] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1309] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1310] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1311] 0.50 0.00 0.42 0.00 0.03 0.49
p_prior[1312] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1313] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1314] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1315] 0.50 0.00 0.43 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 0.42 0.00 0.03 0.49
p_prior[1328] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1329] 0.50 0.00 0.42 0.00 0.03 0.50
p_prior[1330] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1331] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1332] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1333] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1334] 0.50 0.00 0.43 0.00 0.02 0.50
p_prior[1335] 0.50 0.00 0.43 0.00 0.02 0.50
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_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] 0.21 0.00 0.07 0.10 0.17 0.21
p_predicted[4] 0.20 0.00 0.06 0.09 0.15 0.19
p_predicted[5] 0.20 0.00 0.06 0.09 0.15 0.19
p_predicted[6] 0.20 0.00 0.06 0.09 0.15 0.19
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] 0.45 0.00 0.08 0.29 0.40 0.45
p_predicted[10] 0.46 0.00 0.08 0.31 0.41 0.46
p_predicted[11] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[12] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[13] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[14] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[15] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[16] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[17] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[18] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[19] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[20] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[21] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[22] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[23] 0.28 0.00 0.05 0.19 0.24 0.28
p_predicted[24] 0.27 0.00 0.05 0.18 0.23 0.26
p_predicted[25] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted[26] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted[27] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[28] 0.18 0.00 0.04 0.11 0.15 0.17
p_predicted[29] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted[30] 0.35 0.00 0.04 0.27 0.32 0.35
p_predicted[31] 0.35 0.00 0.04 0.27 0.32 0.35
p_predicted[32] 0.34 0.00 0.04 0.26 0.31 0.34
p_predicted[33] 0.34 0.00 0.04 0.26 0.31 0.34
p_predicted[34] 0.32 0.00 0.04 0.24 0.29 0.32
p_predicted[35] 0.32 0.00 0.04 0.24 0.29 0.32
p_predicted[36] 0.32 0.00 0.04 0.23 0.28 0.31
p_predicted[37] 0.32 0.00 0.04 0.23 0.28 0.31
p_predicted[38] 0.22 0.00 0.04 0.16 0.19 0.22
p_predicted[39] 0.22 0.00 0.04 0.16 0.19 0.22
p_predicted[40] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted[41] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted[42] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted[43] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted[44] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted[45] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted[46] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted[47] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted[48] 0.15 0.00 0.03 0.11 0.13 0.15
p_predicted[49] 0.15 0.00 0.03 0.11 0.13 0.15
p_predicted[50] 0.11 0.00 0.06 0.03 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 0.06
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 0.05
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 0.02 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] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[72] 0.17 0.00 0.05 0.09 0.13 0.16
p_predicted[73] 0.16 0.00 0.04 0.08 0.13 0.15
p_predicted[74] 0.16 0.00 0.04 0.08 0.13 0.15
p_predicted[75] 0.12 0.00 0.05 0.05 0.09 0.11
p_predicted[76] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[77] 0.63 0.00 0.11 0.41 0.56 0.63
p_predicted[78] 0.63 0.00 0.11 0.41 0.56 0.63
p_predicted[79] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[80] 0.63 0.00 0.11 0.41 0.56 0.63
p_predicted[81] 0.63 0.00 0.11 0.41 0.56 0.63
p_predicted[82] 0.63 0.00 0.11 0.42 0.56 0.64
p_predicted[83] 0.63 0.00 0.10 0.41 0.56 0.63
p_predicted[84] 0.63 0.00 0.11 0.41 0.56 0.63
p_predicted[85] 0.64 0.00 0.08 0.48 0.59 0.64
p_predicted[86] 0.43 0.00 0.06 0.32 0.39 0.43
p_predicted[87] 0.37 0.00 0.07 0.25 0.32 0.37
p_predicted[88] 0.18 0.00 0.03 0.12 0.15 0.17
p_predicted[89] 0.17 0.00 0.03 0.11 0.14 0.16
p_predicted[90] 0.26 0.00 0.12 0.08 0.17 0.24
p_predicted[91] 0.26 0.00 0.12 0.08 0.17 0.24
p_predicted[92] 0.26 0.00 0.12 0.08 0.17 0.25
p_predicted[93] 0.20 0.00 0.10 0.05 0.12 0.19
p_predicted[94] 0.62 0.00 0.13 0.35 0.53 0.62
p_predicted[95] 0.61 0.00 0.12 0.36 0.53 0.62
p_predicted[96] 0.57 0.00 0.12 0.34 0.50 0.58
p_predicted[97] 0.40 0.00 0.13 0.17 0.31 0.40
p_predicted[98] 0.39 0.00 0.13 0.16 0.30 0.39
p_predicted[99] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted[100] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted[101] 0.17 0.00 0.05 0.09 0.13 0.16
p_predicted[102] 0.14 0.00 0.04 0.08 0.12 0.14
p_predicted[103] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted[104] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[105] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[106] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[107] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[108] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[109] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[110] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[111] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[112] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[113] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[114] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[115] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[116] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[117] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[118] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[119] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[120] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[121] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[122] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[123] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[124] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[125] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[126] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[127] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted[128] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted[129] 0.08 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] 0.21 0.00 0.07 0.09 0.16 0.21
p_predicted[137] 0.26 0.00 0.05 0.17 0.22 0.26
p_predicted[138] 0.26 0.00 0.05 0.17 0.22 0.26
p_predicted[139] 0.26 0.00 0.05 0.17 0.22 0.26
p_predicted[140] 0.23 0.00 0.05 0.15 0.20 0.23
p_predicted[141] 0.21 0.00 0.05 0.13 0.18 0.21
p_predicted[142] 0.18 0.00 0.04 0.11 0.15 0.17
p_predicted[143] 0.17 0.00 0.04 0.11 0.15 0.17
p_predicted[144] 0.17 0.00 0.04 0.10 0.14 0.16
p_predicted[145] 0.17 0.00 0.04 0.10 0.14 0.16
p_predicted[146] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[147] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[148] 0.15 0.00 0.04 0.09 0.12 0.15
p_predicted[149] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[150] 0.09 0.00 0.04 0.03 0.06 0.08
p_predicted[151] 0.11 0.00 0.04 0.05 0.08 0.11
p_predicted[152] 0.11 0.00 0.04 0.05 0.08 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] 0.10 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 0.09
p_predicted[159] 0.07 0.00 0.02 0.04 0.05 0.07
p_predicted[160] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[161] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[162] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[163] 0.16 0.00 0.05 0.08 0.12 0.15
p_predicted[164] 0.16 0.00 0.05 0.08 0.12 0.15
p_predicted[165] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted[166] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted[167] 0.10 0.00 0.03 0.05 0.07 0.09
p_predicted[168] 0.10 0.00 0.03 0.05 0.07 0.09
p_predicted[169] 0.08 0.00 0.03 0.04 0.06 0.08
p_predicted[170] 0.08 0.00 0.03 0.04 0.06 0.08
p_predicted[171] 0.37 0.00 0.04 0.28 0.34 0.37
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 0.04 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 0.03
p_predicted[187] 0.13 0.00 0.06 0.05 0.09 0.12
p_predicted[188] 0.13 0.00 0.06 0.05 0.09 0.12
p_predicted[189] 0.10 0.00 0.03 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] 0.13 0.00 0.06 0.05 0.09 0.12
p_predicted[198] 0.13 0.00 0.06 0.05 0.09 0.12
p_predicted[199] 0.13 0.00 0.06 0.05 0.09 0.12
p_predicted[200] 0.10 0.00 0.03 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] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[209] 0.12 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] 0.10 0.00 0.03 0.06 0.08 0.10
p_predicted[213] 0.08 0.00 0.02 0.05 0.06 0.08
p_predicted[214] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted[215] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted[216] 0.57 0.00 0.12 0.33 0.49 0.58
p_predicted[217] 0.59 0.00 0.10 0.39 0.52 0.59
p_predicted[218] 0.58 0.00 0.10 0.38 0.52 0.59
p_predicted[219] 0.39 0.00 0.12 0.16 0.30 0.38
p_predicted[220] 0.38 0.00 0.13 0.15 0.29 0.38
p_predicted[221] 0.59 0.00 0.10 0.39 0.52 0.59
p_predicted[222] 0.58 0.00 0.10 0.38 0.51 0.58
p_predicted[223] 0.38 0.00 0.12 0.15 0.30 0.38
p_predicted[224] 0.38 0.00 0.13 0.15 0.29 0.38
p_predicted[225] 0.38 0.00 0.13 0.15 0.29 0.37
p_predicted[226] 0.16 0.00 0.06 0.06 0.11 0.15
p_predicted[227] 0.17 0.00 0.07 0.06 0.12 0.16
p_predicted[228] 0.17 0.00 0.07 0.06 0.12 0.16
p_predicted[229] 0.17 0.00 0.07 0.06 0.12 0.16
p_predicted[230] 0.17 0.00 0.07 0.06 0.12 0.16
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] 0.04 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] 0.44 0.00 0.10 0.24 0.38 0.45
p_predicted[241] 0.44 0.00 0.10 0.24 0.38 0.45
p_predicted[242] 0.44 0.00 0.10 0.24 0.38 0.45
p_predicted[243] 0.44 0.00 0.10 0.24 0.38 0.45
p_predicted[244] 0.44 0.00 0.10 0.24 0.38 0.45
p_predicted[245] 0.44 0.00 0.10 0.24 0.38 0.45
p_predicted[246] 0.51 0.00 0.06 0.41 0.48 0.51
p_predicted[247] 0.51 0.00 0.06 0.41 0.48 0.51
p_predicted[248] 0.51 0.00 0.06 0.41 0.48 0.51
p_predicted[249] 0.51 0.00 0.06 0.40 0.47 0.51
p_predicted[250] 0.51 0.00 0.06 0.40 0.47 0.51
p_predicted[251] 0.51 0.00 0.06 0.40 0.47 0.51
p_predicted[252] 0.50 0.00 0.06 0.39 0.46 0.50
p_predicted[253] 0.50 0.00 0.06 0.39 0.46 0.50
p_predicted[254] 0.50 0.00 0.06 0.39 0.46 0.50
p_predicted[255] 0.49 0.00 0.06 0.38 0.45 0.49
p_predicted[256] 0.49 0.00 0.06 0.38 0.45 0.49
p_predicted[257] 0.49 0.00 0.06 0.38 0.45 0.49
p_predicted[258] 0.42 0.00 0.06 0.30 0.38 0.43
p_predicted[259] 0.42 0.00 0.06 0.30 0.38 0.43
p_predicted[260] 0.42 0.00 0.06 0.30 0.38 0.43
p_predicted[261] 0.40 0.00 0.06 0.28 0.35 0.40
p_predicted[262] 0.40 0.00 0.06 0.28 0.35 0.40
p_predicted[263] 0.40 0.00 0.06 0.28 0.35 0.40
p_predicted[264] 0.39 0.00 0.06 0.28 0.35 0.39
p_predicted[265] 0.39 0.00 0.06 0.28 0.35 0.39
p_predicted[266] 0.39 0.00 0.06 0.28 0.35 0.39
p_predicted[267] 0.22 0.00 0.03 0.16 0.20 0.22
p_predicted[268] 0.22 0.00 0.03 0.16 0.20 0.22
p_predicted[269] 0.22 0.00 0.03 0.16 0.20 0.22
p_predicted[270] 0.22 0.00 0.03 0.16 0.19 0.21
p_predicted[271] 0.22 0.00 0.03 0.16 0.19 0.21
p_predicted[272] 0.22 0.00 0.03 0.16 0.19 0.21
p_predicted[273] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[274] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[275] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[276] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[277] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[278] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[279] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[280] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[281] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[282] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[283] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[284] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[285] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[286] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[287] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[288] 0.01 0.00 0.01 0.00 0.01 0.01
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] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[294] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[295] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[296] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[297] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[298] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[299] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[300] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[301] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[302] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[303] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[304] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[305] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[306] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[307] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[308] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[309] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[310] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[311] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[312] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[313] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[314] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[315] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[316] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[317] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[318] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[319] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[320] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[321] 0.09 0.00 0.03 0.04 0.07 0.09
p_predicted[322] 0.14 0.00 0.04 0.07 0.11 0.13
p_predicted[323] 0.16 0.00 0.05 0.08 0.12 0.16
p_predicted[324] 0.09 0.00 0.03 0.04 0.07 0.09
p_predicted[325] 0.10 0.00 0.04 0.05 0.08 0.10
p_predicted[326] 0.07 0.00 0.02 0.03 0.05 0.06
p_predicted[327] 0.08 0.00 0.03 0.03 0.06 0.07
p_predicted[328] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[329] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted[330] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[331] 0.06 0.00 0.02 0.03 0.04 0.06
p_predicted[332] 0.05 0.00 0.02 0.02 0.03 0.04
p_predicted[333] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[334] 0.07 0.00 0.06 0.01 0.03 0.06
p_predicted[335] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted[336] 0.07 0.00 0.05 0.01 0.03 0.05
p_predicted[337] 0.07 0.00 0.05 0.01 0.03 0.05
p_predicted[338] 0.06 0.00 0.05 0.01 0.03 0.05
p_predicted[339] 0.06 0.00 0.05 0.01 0.03 0.05
p_predicted[340] 0.06 0.00 0.05 0.01 0.03 0.05
p_predicted[341] 0.07 0.00 0.05 0.01 0.03 0.05
p_predicted[342] 0.46 0.00 0.07 0.32 0.41 0.46
p_predicted[343] 0.46 0.00 0.07 0.32 0.41 0.46
p_predicted[344] 0.51 0.00 0.06 0.41 0.48 0.52
p_predicted[345] 0.51 0.00 0.06 0.41 0.48 0.52
p_predicted[346] 0.42 0.00 0.05 0.31 0.38 0.42
p_predicted[347] 0.42 0.00 0.05 0.31 0.38 0.42
p_predicted[348] 0.33 0.00 0.05 0.22 0.29 0.33
p_predicted[349] 0.33 0.00 0.05 0.22 0.29 0.33
p_predicted[350] 0.20 0.00 0.09 0.06 0.13 0.18
p_predicted[351] 0.20 0.00 0.10 0.05 0.13 0.18
p_predicted[352] 0.25 0.00 0.09 0.11 0.18 0.24
p_predicted[353] 0.25 0.00 0.10 0.09 0.17 0.24
p_predicted[354] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[355] 0.21 0.00 0.07 0.10 0.16 0.20
p_predicted[356] 0.19 0.00 0.06 0.09 0.15 0.19
p_predicted[357] 0.19 0.00 0.07 0.08 0.14 0.18
p_predicted[358] 0.14 0.00 0.05 0.05 0.10 0.13
p_predicted[359] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[360] 0.14 0.00 0.05 0.05 0.10 0.13
p_predicted[361] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[362] 0.14 0.00 0.05 0.05 0.10 0.13
p_predicted[363] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted[364] 0.36 0.00 0.05 0.26 0.32 0.35
p_predicted[365] 0.36 0.00 0.05 0.26 0.32 0.35
p_predicted[366] 0.36 0.00 0.05 0.26 0.32 0.35
p_predicted[367] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[368] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[369] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[370] 0.19 0.00 0.04 0.12 0.16 0.18
p_predicted[371] 0.19 0.00 0.04 0.12 0.16 0.18
p_predicted[372] 0.19 0.00 0.04 0.12 0.16 0.18
p_predicted[373] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted[374] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted[375] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted[376] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[377] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[378] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[379] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[380] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[381] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[382] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[383] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[384] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[385] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[386] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[387] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[388] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[389] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[390] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[391] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[392] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[393] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[394] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[395] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[396] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[397] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[398] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[399] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[400] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[401] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[402] 0.13 0.00 0.03 0.08 0.11 0.12
p_predicted[403] 0.12 0.00 0.03 0.08 0.11 0.12
p_predicted[404] 0.12 0.00 0.03 0.08 0.11 0.12
p_predicted[405] 0.12 0.00 0.03 0.08 0.11 0.12
p_predicted[406] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[407] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[408] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[409] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[410] 0.36 0.00 0.09 0.19 0.30 0.36
p_predicted[411] 0.30 0.00 0.05 0.21 0.27 0.30
p_predicted[412] 0.29 0.00 0.05 0.21 0.26 0.29
p_predicted[413] 0.29 0.00 0.05 0.20 0.26 0.29
p_predicted[414] 0.29 0.00 0.05 0.21 0.26 0.29
p_predicted[415] 0.30 0.00 0.05 0.21 0.26 0.30
p_predicted[416] 0.25 0.00 0.05 0.16 0.21 0.25
p_predicted[417] 0.28 0.00 0.05 0.20 0.25 0.28
p_predicted[418] 0.23 0.00 0.04 0.15 0.20 0.22
p_predicted[419] 0.27 0.00 0.05 0.19 0.24 0.27
p_predicted[420] 0.23 0.00 0.04 0.15 0.20 0.22
p_predicted[421] 0.23 0.00 0.04 0.15 0.20 0.22
p_predicted[422] 0.16 0.00 0.03 0.10 0.14 0.16
p_predicted[423] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[424] 0.21 0.00 0.04 0.15 0.19 0.21
p_predicted[425] 0.20 0.00 0.03 0.14 0.18 0.20
p_predicted[426] 0.08 0.00 0.02 0.05 0.06 0.08
p_predicted[427] 0.07 0.00 0.02 0.04 0.06 0.07
p_predicted[428] 0.05 0.00 0.02 0.03 0.04 0.05
p_predicted[429] 0.33 0.00 0.07 0.21 0.28 0.33
p_predicted[430] 0.30 0.00 0.06 0.18 0.25 0.29
p_predicted[431] 0.24 0.00 0.06 0.14 0.20 0.23
p_predicted[432] 0.24 0.00 0.06 0.14 0.19 0.23
p_predicted[433] 0.26 0.00 0.06 0.15 0.22 0.26
p_predicted[434] 0.17 0.00 0.06 0.07 0.12 0.16
p_predicted[435] 0.15 0.00 0.07 0.05 0.10 0.15
p_predicted[436] 0.15 0.00 0.07 0.05 0.10 0.14
p_predicted[437] 0.14 0.00 0.07 0.03 0.09 0.13
p_predicted[438] 0.21 0.00 0.07 0.09 0.16 0.21
p_predicted[439] 0.26 0.00 0.05 0.17 0.22 0.26
p_predicted[440] 0.24 0.00 0.05 0.16 0.21 0.24
p_predicted[441] 0.25 0.00 0.05 0.16 0.21 0.25
p_predicted[442] 0.24 0.00 0.05 0.16 0.21 0.24
p_predicted[443] 0.22 0.00 0.05 0.14 0.19 0.22
p_predicted[444] 0.23 0.00 0.05 0.15 0.20 0.23
p_predicted[445] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[446] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[447] 0.22 0.00 0.05 0.13 0.18 0.21
p_predicted[448] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[449] 0.18 0.00 0.04 0.11 0.15 0.17
p_predicted[450] 0.17 0.00 0.04 0.10 0.14 0.16
p_predicted[451] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[452] 0.15 0.00 0.06 0.05 0.11 0.15
p_predicted[453] 0.12 0.00 0.04 0.05 0.09 0.12
p_predicted[454] 0.12 0.00 0.04 0.05 0.09 0.12
p_predicted[455] 0.11 0.00 0.04 0.04 0.08 0.11
p_predicted[456] 0.21 0.00 0.07 0.09 0.16 0.21
p_predicted[457] 0.26 0.00 0.05 0.17 0.22 0.26
p_predicted[458] 0.24 0.00 0.05 0.15 0.20 0.23
p_predicted[459] 0.22 0.00 0.05 0.14 0.19 0.22
p_predicted[460] 0.21 0.00 0.05 0.13 0.18 0.21
p_predicted[461] 0.21 0.00 0.05 0.12 0.17 0.20
p_predicted[462] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted[463] 0.17 0.00 0.04 0.10 0.14 0.16
p_predicted[464] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[465] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted[466] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted[467] 0.22 0.00 0.04 0.14 0.19 0.22
p_predicted[468] 0.23 0.00 0.04 0.15 0.20 0.23
p_predicted[469] 0.18 0.00 0.03 0.11 0.15 0.17
p_predicted[470] 0.18 0.00 0.04 0.12 0.16 0.18
p_predicted[471] 0.18 0.00 0.03 0.12 0.15 0.18
p_predicted[472] 0.17 0.00 0.03 0.11 0.15 0.17
p_predicted[473] 0.09 0.00 0.02 0.05 0.07 0.09
p_predicted[474] 0.09 0.00 0.03 0.04 0.07 0.08
p_predicted[475] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[476] 0.07 0.00 0.03 0.02 0.05 0.06
p_predicted[477] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted[478] 0.24 0.00 0.04 0.18 0.22 0.24
p_predicted[479] 0.25 0.00 0.04 0.18 0.22 0.25
p_predicted[480] 0.17 0.00 0.03 0.12 0.15 0.17
p_predicted[481] 0.16 0.00 0.03 0.12 0.15 0.16
p_predicted[482] 0.16 0.00 0.03 0.12 0.14 0.16
p_predicted[483] 0.16 0.00 0.03 0.12 0.15 0.16
p_predicted[484] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted[485] 0.16 0.00 0.03 0.11 0.14 0.16
p_predicted[486] 0.15 0.00 0.03 0.11 0.14 0.15
p_predicted[487] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[488] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[489] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[490] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[491] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[492] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[493] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[494] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[495] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[496] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[497] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted[498] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[499] 0.18 0.00 0.04 0.12 0.15 0.18
p_predicted[500] 0.18 0.00 0.03 0.11 0.15 0.18
p_predicted[501] 0.17 0.00 0.03 0.11 0.15 0.17
p_predicted[502] 0.17 0.00 0.03 0.11 0.15 0.17
p_predicted[503] 0.17 0.00 0.03 0.11 0.15 0.17
p_predicted[504] 0.10 0.00 0.02 0.05 0.08 0.09
p_predicted[505] 0.09 0.00 0.03 0.05 0.07 0.09
p_predicted[506] 0.08 0.00 0.03 0.03 0.06 0.07
p_predicted[507] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[508] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[509] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[510] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[511] 0.01 0.00 0.02 0.00 0.00 0.00
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] 0.15 0.00 0.07 0.05 0.10 0.15
p_predicted[520] 0.14 0.00 0.07 0.04 0.09 0.13
p_predicted[521] 0.54 0.00 0.06 0.42 0.50 0.55
p_predicted[522] 0.54 0.00 0.06 0.42 0.50 0.55
p_predicted[523] 0.54 0.00 0.06 0.42 0.50 0.55
p_predicted[524] 0.52 0.00 0.06 0.40 0.48 0.52
p_predicted[525] 0.52 0.00 0.06 0.40 0.48 0.52
p_predicted[526] 0.52 0.00 0.06 0.40 0.48 0.52
p_predicted[527] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[528] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[529] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[530] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[531] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[532] 0.33 0.00 0.04 0.25 0.30 0.33
p_predicted[533] 0.27 0.00 0.04 0.19 0.24 0.27
p_predicted[534] 0.27 0.00 0.04 0.19 0.24 0.27
p_predicted[535] 0.27 0.00 0.04 0.19 0.24 0.27
p_predicted[536] 0.44 0.00 0.06 0.33 0.40 0.44
p_predicted[537] 0.44 0.00 0.06 0.33 0.40 0.44
p_predicted[538] 0.44 0.00 0.06 0.33 0.40 0.44
p_predicted[539] 0.42 0.00 0.06 0.31 0.38 0.42
p_predicted[540] 0.42 0.00 0.06 0.31 0.38 0.42
p_predicted[541] 0.42 0.00 0.06 0.31 0.38 0.42
p_predicted[542] 0.24 0.00 0.04 0.17 0.21 0.24
p_predicted[543] 0.24 0.00 0.04 0.17 0.21 0.24
p_predicted[544] 0.24 0.00 0.04 0.17 0.21 0.24
p_predicted[545] 0.25 0.00 0.04 0.17 0.21 0.24
p_predicted[546] 0.25 0.00 0.04 0.17 0.21 0.24
p_predicted[547] 0.25 0.00 0.04 0.17 0.21 0.24
p_predicted[548] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[549] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[550] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[551] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[552] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[553] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[554] 0.02 0.00 0.01 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] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[559] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[560] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[561] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[562] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[563] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[564] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[565] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[566] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[567] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[568] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[569] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[570] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[571] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[572] 0.14 0.00 0.06 0.05 0.10 0.13
p_predicted[573] 0.08 0.00 0.04 0.02 0.05 0.07
p_predicted[574] 0.07 0.00 0.04 0.02 0.04 0.07
p_predicted[575] 0.08 0.00 0.05 0.02 0.05 0.07
p_predicted[576] 0.09 0.00 0.05 0.02 0.05 0.07
p_predicted[577] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[578] 0.21 0.00 0.07 0.09 0.16 0.21
p_predicted[579] 0.26 0.00 0.05 0.16 0.22 0.25
p_predicted[580] 0.21 0.00 0.05 0.13 0.18 0.21
p_predicted[581] 0.17 0.00 0.04 0.11 0.15 0.17
p_predicted[582] 0.16 0.00 0.04 0.10 0.13 0.16
p_predicted[583] 0.11 0.00 0.06 0.03 0.07 0.10
p_predicted[584] 0.11 0.00 0.06 0.03 0.07 0.10
p_predicted[585] 0.14 0.00 0.07 0.04 0.10 0.13
p_predicted[586] 0.14 0.00 0.06 0.05 0.10 0.13
p_predicted[587] 0.15 0.00 0.07 0.05 0.10 0.14
p_predicted[588] 0.14 0.00 0.06 0.05 0.10 0.13
p_predicted[589] 0.15 0.00 0.07 0.05 0.10 0.14
p_predicted[590] 0.14 0.00 0.06 0.05 0.10 0.13
p_predicted[591] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted[592] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted[593] 0.35 0.00 0.09 0.17 0.30 0.36
p_predicted[594] 0.41 0.00 0.05 0.31 0.38 0.41
p_predicted[595] 0.41 0.00 0.05 0.30 0.37 0.40
p_predicted[596] 0.40 0.00 0.06 0.29 0.36 0.40
p_predicted[597] 0.32 0.00 0.05 0.22 0.28 0.32
p_predicted[598] 0.34 0.00 0.06 0.23 0.30 0.33
p_predicted[599] 0.16 0.00 0.03 0.10 0.13 0.16
p_predicted[600] 0.16 0.00 0.03 0.10 0.13 0.15
p_predicted[601] 0.14 0.00 0.04 0.08 0.11 0.14
p_predicted[602] 0.12 0.00 0.04 0.04 0.08 0.11
p_predicted[603] 0.75 0.00 0.14 0.44 0.67 0.77
p_predicted[604] 0.75 0.00 0.14 0.44 0.67 0.77
p_predicted[605] 0.75 0.00 0.13 0.44 0.66 0.76
p_predicted[606] 0.75 0.00 0.13 0.44 0.66 0.76
p_predicted[607] 0.74 0.00 0.14 0.44 0.66 0.76
p_predicted[608] 0.74 0.00 0.14 0.44 0.66 0.76
p_predicted[609] 0.74 0.00 0.14 0.43 0.66 0.76
p_predicted[610] 0.74 0.00 0.14 0.43 0.66 0.76
p_predicted[611] 0.78 0.00 0.13 0.48 0.70 0.80
p_predicted[612] 0.78 0.00 0.13 0.48 0.70 0.80
p_predicted[613] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[614] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[615] 0.21 0.00 0.04 0.13 0.18 0.21
p_predicted[616] 0.05 0.00 0.11 0.00 0.00 0.00
p_predicted[617] 0.05 0.00 0.11 0.00 0.00 0.00
p_predicted[618] 0.08 0.00 0.05 0.02 0.04 0.07
p_predicted[619] 0.08 0.00 0.05 0.02 0.05 0.07
p_predicted[620] 0.08 0.00 0.05 0.02 0.05 0.07
p_predicted[621] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted[622] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[623] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[624] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[625] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[626] 0.34 0.00 0.09 0.17 0.28 0.34
p_predicted[627] 0.41 0.00 0.06 0.30 0.37 0.41
p_predicted[628] 0.34 0.00 0.06 0.23 0.30 0.34
p_predicted[629] 0.33 0.00 0.06 0.23 0.29 0.33
p_predicted[630] 0.33 0.00 0.06 0.23 0.29 0.33
p_predicted[631] 0.17 0.00 0.04 0.10 0.14 0.16
p_predicted[632] 0.21 0.00 0.08 0.08 0.15 0.20
p_predicted[633] 0.22 0.00 0.10 0.06 0.14 0.20
p_predicted[634] 0.27 0.00 0.08 0.13 0.21 0.26
p_predicted[635] 0.27 0.00 0.10 0.11 0.20 0.26
p_predicted[636] 0.26 0.00 0.08 0.13 0.21 0.26
p_predicted[637] 0.27 0.00 0.10 0.11 0.19 0.26
p_predicted[638] 0.26 0.00 0.08 0.13 0.21 0.26
p_predicted[639] 0.27 0.00 0.10 0.11 0.19 0.26
p_predicted[640] 0.19 0.00 0.07 0.08 0.14 0.19
p_predicted[641] 0.20 0.00 0.09 0.06 0.13 0.18
p_predicted[642] 0.19 0.00 0.07 0.07 0.14 0.18
p_predicted[643] 0.19 0.00 0.09 0.06 0.13 0.18
p_predicted[644] 0.19 0.00 0.07 0.07 0.14 0.18
p_predicted[645] 0.19 0.00 0.09 0.06 0.13 0.18
p_predicted[646] 0.27 0.00 0.10 0.11 0.20 0.26
p_predicted[647] 0.27 0.00 0.10 0.11 0.20 0.26
p_predicted[648] 0.26 0.00 0.10 0.10 0.19 0.25
p_predicted[649] 0.26 0.00 0.10 0.10 0.19 0.25
p_predicted[650] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[651] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[652] 0.06 0.00 0.04 0.01 0.04 0.06
p_predicted[653] 0.06 0.00 0.04 0.01 0.04 0.06
p_predicted[654] 0.06 0.00 0.04 0.01 0.04 0.05
p_predicted[655] 0.06 0.00 0.04 0.01 0.04 0.05
p_predicted[656] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[657] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[658] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[659] 0.03 0.00 0.02 0.01 0.02 0.03
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] 0.09 0.00 0.06 0.01 0.04 0.07
p_predicted[665] 0.09 0.00 0.06 0.01 0.04 0.07
p_predicted[666] 0.09 0.00 0.06 0.02 0.05 0.07
p_predicted[667] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[668] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[669] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[670] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[671] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[672] 0.05 0.00 0.03 0.01 0.02 0.04
p_predicted[673] 0.05 0.00 0.03 0.01 0.02 0.04
p_predicted[674] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[675] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[676] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[677] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[678] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[679] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[680] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[681] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[682] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[683] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[684] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[685] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[686] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[687] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[688] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[689] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted[690] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[691] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[692] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted[693] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[694] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[695] 0.05 0.00 0.03 0.02 0.03 0.05
p_predicted[696] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[697] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[698] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[699] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[700] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[701] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[702] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[703] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[704] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[705] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[706] 0.51 0.00 0.06 0.41 0.48 0.52
p_predicted[707] 0.43 0.00 0.06 0.31 0.39 0.43
p_predicted[708] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted[709] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[710] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[711] 0.24 0.00 0.04 0.16 0.21 0.24
p_predicted[712] 0.23 0.00 0.04 0.16 0.20 0.23
p_predicted[713] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted[714] 0.18 0.00 0.04 0.12 0.15 0.18
p_predicted[715] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[716] 0.09 0.00 0.03 0.05 0.07 0.09
p_predicted[717] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[718] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[719] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[720] 0.00 0.00 0.01 0.00 0.00 0.00
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] 0.32 0.00 0.11 0.14 0.24 0.31
p_predicted[727] 0.32 0.00 0.11 0.14 0.24 0.31
p_predicted[728] 0.24 0.00 0.10 0.08 0.16 0.22
p_predicted[729] 0.52 0.00 0.13 0.27 0.43 0.52
p_predicted[730] 0.52 0.00 0.13 0.27 0.43 0.52
p_predicted[731] 0.50 0.00 0.12 0.27 0.42 0.50
p_predicted[732] 0.42 0.00 0.12 0.20 0.34 0.42
p_predicted[733] 0.42 0.00 0.12 0.20 0.34 0.42
p_predicted[734] 0.42 0.00 0.12 0.19 0.33 0.41
p_predicted[735] 0.41 0.00 0.13 0.18 0.32 0.41
p_predicted[736] 0.41 0.00 0.13 0.17 0.32 0.41
p_predicted[737] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[738] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[739] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[740] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[741] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[742] 0.00 0.00 0.00 0.00 0.00 0.00
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] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[749] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[750] 0.02 0.00 0.05 0.00 0.00 0.00
p_predicted[751] 0.02 0.00 0.05 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 0.06 0.00 0.00 0.00
p_predicted[754] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[755] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[756] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[757] 0.00 0.00 0.00 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] 0.31 0.00 0.04 0.23 0.28 0.31
p_predicted[788] 0.30 0.00 0.04 0.22 0.28 0.31
p_predicted[789] 0.30 0.00 0.04 0.22 0.28 0.30
p_predicted[790] 0.31 0.00 0.04 0.23 0.28 0.31
p_predicted[791] 0.26 0.00 0.04 0.17 0.23 0.26
p_predicted[792] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[793] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[794] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[795] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[796] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[797] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[798] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[799] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[800] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[801] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[802] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[803] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[804] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[805] 0.02 0.00 0.01 0.00 0.01 0.01
p_predicted[806] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[807] 0.02 0.00 0.01 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] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[812] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[813] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[814] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[815] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[816] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[817] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[818] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[819] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted[820] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[821] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[822] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[823] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[824] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[825] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[826] 0.06 0.00 0.05 0.01 0.02 0.04
p_predicted[827] 0.07 0.00 0.05 0.01 0.04 0.06
p_predicted[828] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[829] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[830] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[831] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[832] 0.15 0.00 0.05 0.06 0.11 0.14
p_predicted[833] 0.18 0.00 0.07 0.07 0.13 0.18
p_predicted[834] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[835] 0.22 0.00 0.06 0.13 0.18 0.22
p_predicted[836] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[837] 0.23 0.00 0.06 0.13 0.18 0.22
p_predicted[838] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[839] 0.22 0.00 0.06 0.13 0.18 0.22
p_predicted[840] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted[841] 0.13 0.00 0.04 0.06 0.10 0.12
p_predicted[842] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted[843] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[844] 0.10 0.00 0.03 0.05 0.08 0.09
p_predicted[845] 0.12 0.00 0.04 0.06 0.09 0.12
p_predicted[846] 0.08 0.00 0.02 0.04 0.06 0.07
p_predicted[847] 0.10 0.00 0.03 0.05 0.07 0.09
p_predicted[848] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted[849] 0.08 0.00 0.03 0.04 0.06 0.07
p_predicted[850] 0.06 0.00 0.02 0.03 0.04 0.05
p_predicted[851] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[852] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted[853] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted[854] 0.18 0.00 0.04 0.11 0.15 0.17
p_predicted[855] 0.17 0.00 0.04 0.11 0.15 0.17
p_predicted[856] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[857] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[858] 0.16 0.00 0.03 0.10 0.13 0.15
p_predicted[859] 0.15 0.00 0.03 0.09 0.13 0.15
p_predicted[860] 0.12 0.00 0.03 0.08 0.10 0.12
p_predicted[861] 0.11 0.00 0.03 0.07 0.10 0.11
p_predicted[862] 0.11 0.00 0.02 0.07 0.09 0.11
p_predicted[863] 0.11 0.00 0.02 0.07 0.09 0.11
p_predicted[864] 0.11 0.00 0.03 0.07 0.10 0.11
p_predicted[865] 0.12 0.00 0.03 0.07 0.10 0.11
p_predicted[866] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[867] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[868] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[869] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[870] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[871] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[872] 0.25 0.00 0.07 0.12 0.20 0.24
p_predicted[873] 0.18 0.00 0.06 0.09 0.14 0.17
p_predicted[874] 0.14 0.00 0.05 0.06 0.11 0.14
p_predicted[875] 0.14 0.00 0.05 0.06 0.10 0.13
p_predicted[876] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[877] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[878] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[879] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[880] 0.02 0.00 0.01 0.00 0.01 0.02
p_predicted[881] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[882] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[883] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[884] 0.00 0.00 0.01 0.00 0.00 0.00
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 0.00 0.00 0.00 0.00
p_predicted[894] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[895] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[896] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[897] 0.20 0.00 0.06 0.09 0.16 0.20
p_predicted[898] 0.25 0.00 0.04 0.16 0.21 0.24
p_predicted[899] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[900] 0.11 0.00 0.03 0.06 0.09 0.10
p_predicted[901] 0.10 0.00 0.02 0.06 0.08 0.10
p_predicted[902] 0.09 0.00 0.03 0.05 0.07 0.08
p_predicted[903] 0.24 0.00 0.05 0.15 0.21 0.24
p_predicted[904] 0.22 0.00 0.05 0.14 0.19 0.21
p_predicted[905] 0.18 0.00 0.04 0.11 0.15 0.17
p_predicted[906] 0.16 0.00 0.03 0.10 0.13 0.16
p_predicted[907] 0.16 0.00 0.03 0.10 0.13 0.16
p_predicted[908] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[909] 0.02 0.00 0.02 0.00 0.01 0.01
p_predicted[910] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[911] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[912] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[913] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[914] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[915] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted[916] 0.02 0.00 0.02 0.00 0.01 0.02
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] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted[920] 0.26 0.00 0.08 0.11 0.20 0.26
p_predicted[921] 0.30 0.00 0.06 0.19 0.26 0.30
p_predicted[922] 0.30 0.00 0.06 0.19 0.26 0.29
p_predicted[923] 0.30 0.00 0.06 0.19 0.26 0.30
p_predicted[924] 0.11 0.00 0.05 0.04 0.08 0.10
p_predicted[925] 0.14 0.00 0.04 0.07 0.11 0.13
p_predicted[926] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted[927] 0.05 0.00 0.02 0.03 0.04 0.05
p_predicted[928] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[929] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[930] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[931] 0.00 0.00 0.00 0.00 0.00 0.00
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] 0.56 0.00 0.07 0.42 0.51 0.56
p_predicted[941] 0.54 0.00 0.07 0.41 0.50 0.54
p_predicted[942] 0.47 0.00 0.07 0.32 0.42 0.47
p_predicted[943] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[944] 0.25 0.00 0.04 0.17 0.22 0.25
p_predicted[945] 0.25 0.00 0.04 0.17 0.21 0.24
p_predicted[946] 0.23 0.00 0.07 0.12 0.19 0.23
p_predicted[947] 0.23 0.00 0.07 0.12 0.19 0.23
p_predicted[948] 0.23 0.00 0.07 0.12 0.19 0.23
p_predicted[949] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[950] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[951] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[952] 0.18 0.00 0.05 0.10 0.15 0.18
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 0.09 0.14 0.17
p_predicted[956] 0.18 0.00 0.05 0.09 0.14 0.17
p_predicted[957] 0.18 0.00 0.05 0.09 0.14 0.17
p_predicted[958] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted[959] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted[960] 0.19 0.00 0.05 0.10 0.15 0.18
p_predicted[961] 0.18 0.00 0.05 0.10 0.14 0.18
p_predicted[962] 0.18 0.00 0.05 0.10 0.14 0.18
p_predicted[963] 0.18 0.00 0.05 0.10 0.14 0.18
p_predicted[964] 0.18 0.00 0.05 0.09 0.14 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] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[968] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[969] 0.17 0.00 0.05 0.09 0.14 0.17
p_predicted[970] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[971] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[972] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[973] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[974] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[975] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[976] 0.00 0.00 0.00 0.00 0.00 0.00
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 0.01 0.00 0.00 0.00
p_predicted[990] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[991] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[992] 0.90 0.00 0.11 0.58 0.87 0.94
p_predicted[993] 0.91 0.00 0.10 0.63 0.88 0.95
p_predicted[994] 0.92 0.00 0.10 0.63 0.90 0.96
p_predicted[995] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[996] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[997] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[998] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[999] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1000] 0.12 0.00 0.07 0.03 0.07 0.10
p_predicted[1001] 0.13 0.00 0.07 0.04 0.08 0.12
p_predicted[1002] 0.13 0.00 0.07 0.04 0.08 0.12
p_predicted[1003] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted[1004] 0.11 0.00 0.06 0.03 0.06 0.09
p_predicted[1005] 0.21 0.00 0.09 0.07 0.14 0.19
p_predicted[1006] 0.23 0.00 0.09 0.09 0.17 0.22
p_predicted[1007] 0.23 0.00 0.10 0.07 0.15 0.21
p_predicted[1008] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1009] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1010] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[1011] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1012] 0.06 0.00 0.04 0.01 0.03 0.05
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] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[1016] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1017] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1018] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1019] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1020] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1021] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1022] 0.04 0.00 0.03 0.00 0.02 0.03
p_predicted[1023] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1024] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1025] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted[1026] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1027] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[1028] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1029] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1030] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1031] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1032] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1033] 0.04 0.00 0.03 0.00 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 0.04
p_predicted[1036] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1037] 0.04 0.00 0.03 0.01 0.02 0.04
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 0.03
p_predicted[1046] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted[1047] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted[1048] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1049] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1050] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1051] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1052] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1053] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1054] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1055] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1056] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1057] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1058] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1059] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1060] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1061] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1062] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1063] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1064] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1065] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1066] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1067] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1068] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1069] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted[1070] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1071] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1072] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[1073] 0.17 0.00 0.05 0.08 0.13 0.16
p_predicted[1074] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1075] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1076] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted[1077] 0.13 0.00 0.04 0.06 0.10 0.13
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 0.05 0.05 0.10 0.13
p_predicted[1081] 0.16 0.00 0.04 0.09 0.13 0.16
p_predicted[1082] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted[1083] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted[1084] 0.11 0.00 0.03 0.06 0.09 0.10
p_predicted[1085] 0.09 0.00 0.02 0.05 0.08 0.09
p_predicted[1086] 0.09 0.00 0.02 0.05 0.08 0.09
p_predicted[1087] 0.19 0.00 0.06 0.08 0.14 0.18
p_predicted[1088] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted[1089] 0.19 0.00 0.06 0.08 0.14 0.18
p_predicted[1090] 0.18 0.00 0.06 0.08 0.14 0.18
p_predicted[1091] 0.18 0.00 0.06 0.08 0.14 0.17
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 0.17
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 0.14 0.17
p_predicted[1096] 0.18 0.00 0.06 0.08 0.13 0.17
p_predicted[1097] 0.18 0.00 0.06 0.08 0.13 0.17
p_predicted[1098] 0.17 0.00 0.06 0.07 0.12 0.16
p_predicted[1099] 0.13 0.00 0.04 0.06 0.10 0.12
p_predicted[1100] 0.12 0.00 0.04 0.06 0.09 0.12
p_predicted[1101] 0.12 0.00 0.04 0.05 0.09 0.11
p_predicted[1102] 0.43 0.00 0.07 0.30 0.38 0.43
p_predicted[1103] 0.43 0.00 0.07 0.30 0.38 0.43
p_predicted[1104] 0.42 0.00 0.07 0.29 0.37 0.42
p_predicted[1105] 0.36 0.00 0.07 0.22 0.31 0.36
p_predicted[1106] 0.20 0.00 0.05 0.11 0.16 0.20
p_predicted[1107] 0.24 0.00 0.05 0.14 0.20 0.23
p_predicted[1108] 0.22 0.00 0.05 0.14 0.19 0.22
p_predicted[1109] 0.22 0.00 0.05 0.14 0.19 0.22
p_predicted[1110] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted[1111] 0.19 0.00 0.05 0.11 0.15 0.18
p_predicted[1112] 0.18 0.00 0.04 0.11 0.15 0.18
p_predicted[1113] 0.17 0.00 0.04 0.11 0.15 0.17
p_predicted[1114] 0.16 0.00 0.04 0.10 0.14 0.16
p_predicted[1115] 0.12 0.00 0.03 0.08 0.10 0.12
p_predicted[1116] 0.12 0.00 0.03 0.08 0.10 0.12
p_predicted[1117] 0.12 0.00 0.03 0.07 0.10 0.11
p_predicted[1118] 0.11 0.00 0.03 0.07 0.10 0.11
p_predicted[1119] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted[1120] 0.12 0.00 0.03 0.07 0.10 0.11
p_predicted[1121] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted[1122] 0.11 0.00 0.02 0.07 0.09 0.11
p_predicted[1123] 0.29 0.00 0.10 0.13 0.22 0.28
p_predicted[1124] 0.24 0.00 0.07 0.12 0.19 0.23
p_predicted[1125] 0.23 0.00 0.07 0.12 0.18 0.23
p_predicted[1126] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted[1127] 0.18 0.00 0.05 0.09 0.14 0.17
p_predicted[1128] 0.07 0.00 0.07 0.00 0.02 0.04
p_predicted[1129] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1130] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted[1131] 0.15 0.00 0.06 0.05 0.10 0.14
p_predicted[1132] 0.11 0.00 0.04 0.04 0.08 0.10
p_predicted[1133] 0.15 0.00 0.06 0.05 0.10 0.14
p_predicted[1134] 0.11 0.00 0.05 0.04 0.07 0.11
p_predicted[1135] 0.08 0.00 0.04 0.03 0.05 0.08
p_predicted[1136] 0.11 0.00 0.05 0.04 0.07 0.11
p_predicted[1137] 0.15 0.00 0.06 0.06 0.11 0.15
p_predicted[1138] 0.11 0.00 0.04 0.04 0.08 0.11
p_predicted[1139] 0.15 0.00 0.06 0.06 0.11 0.15
p_predicted[1140] 0.12 0.00 0.05 0.04 0.08 0.11
p_predicted[1141] 0.08 0.00 0.04 0.03 0.06 0.08
p_predicted[1142] 0.12 0.00 0.05 0.04 0.08 0.11
p_predicted[1143] 0.07 0.00 0.03 0.03 0.05 0.06
p_predicted[1144] 0.05 0.00 0.02 0.02 0.03 0.05
p_predicted[1145] 0.07 0.00 0.03 0.03 0.05 0.06
p_predicted[1146] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1147] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[1148] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1149] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1150] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[1151] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1152] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1153] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1154] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1155] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1156] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1157] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1158] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1159] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1160] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1161] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[1162] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1163] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted[1164] 0.05 0.00 0.02 0.01 0.03 0.04
p_predicted[1165] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[1166] 0.05 0.00 0.02 0.01 0.03 0.04
p_predicted[1167] 0.05 0.00 0.03 0.01 0.03 0.05
p_predicted[1168] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted[1169] 0.05 0.00 0.03 0.01 0.03 0.05
p_predicted[1170] 0.79 0.00 0.14 0.45 0.71 0.82
p_predicted[1171] 0.78 0.00 0.14 0.46 0.70 0.81
p_predicted[1172] 0.77 0.00 0.14 0.44 0.69 0.80
p_predicted[1173] 0.76 0.00 0.15 0.42 0.68 0.79
p_predicted[1174] 0.77 0.00 0.15 0.39 0.68 0.80
p_predicted[1175] 0.11 0.00 0.05 0.03 0.07 0.10
p_predicted[1176] 0.13 0.00 0.05 0.06 0.10 0.13
p_predicted[1177] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1178] 0.09 0.00 0.03 0.04 0.06 0.08
p_predicted[1179] 0.07 0.00 0.02 0.03 0.05 0.06
p_predicted[1180] 0.06 0.00 0.02 0.02 0.04 0.05
p_predicted[1181] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1182] 0.55 0.00 0.18 0.19 0.42 0.55
p_predicted[1183] 0.52 0.00 0.19 0.16 0.38 0.53
p_predicted[1184] 0.52 0.00 0.19 0.17 0.38 0.53
p_predicted[1185] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1186] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1187] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[1188] 0.04 0.00 0.03 0.01 0.02 0.04
p_predicted[1189] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1190] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted[1191] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1192] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[1193] 0.04 0.00 0.03 0.01 0.03 0.04
p_predicted[1194] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1195] 0.57 0.00 0.12 0.32 0.49 0.58
p_predicted[1196] 0.57 0.00 0.12 0.32 0.49 0.58
p_predicted[1197] 0.63 0.00 0.14 0.35 0.54 0.64
p_predicted[1198] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1199] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1200] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1201] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1202] 0.55 0.00 0.12 0.31 0.47 0.55
p_predicted[1203] 0.49 0.00 0.12 0.25 0.41 0.49
p_predicted[1204] 0.48 0.00 0.12 0.25 0.40 0.48
p_predicted[1205] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1206] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1207] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted[1208] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted[1209] 0.07 0.00 0.04 0.01 0.04 0.06
p_predicted[1210] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1211] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted[1212] 0.04 0.00 0.03 0.01 0.02 0.03
p_predicted[1213] 0.27 0.00 0.08 0.13 0.21 0.26
p_predicted[1214] 0.26 0.00 0.08 0.13 0.21 0.26
p_predicted[1215] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[1216] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[1217] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[1218] 0.08 0.00 0.06 0.01 0.04 0.07
p_predicted[1219] 0.08 0.00 0.06 0.01 0.04 0.06
p_predicted[1220] 0.08 0.00 0.06 0.01 0.04 0.06
p_predicted[1221] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1222] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1223] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1224] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1225] 0.59 0.00 0.12 0.33 0.51 0.60
p_predicted[1226] 0.61 0.00 0.10 0.39 0.54 0.61
p_predicted[1227] 0.53 0.00 0.12 0.29 0.44 0.53
p_predicted[1228] 0.52 0.00 0.12 0.27 0.43 0.52
p_predicted[1229] 0.32 0.00 0.04 0.24 0.29 0.32
p_predicted[1230] 0.32 0.00 0.04 0.24 0.29 0.32
p_predicted[1231] 0.31 0.00 0.04 0.22 0.28 0.31
p_predicted[1232] 0.31 0.00 0.04 0.22 0.28 0.31
p_predicted[1233] 0.30 0.00 0.04 0.21 0.27 0.30
p_predicted[1234] 0.30 0.00 0.04 0.21 0.27 0.30
p_predicted[1235] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted[1236] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted[1237] 0.23 0.00 0.04 0.16 0.20 0.23
p_predicted[1238] 0.23 0.00 0.04 0.16 0.20 0.23
p_predicted[1239] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[1240] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[1241] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[1242] 0.22 0.00 0.04 0.15 0.19 0.22
p_predicted[1243] 0.22 0.00 0.04 0.14 0.19 0.21
p_predicted[1244] 0.22 0.00 0.04 0.14 0.19 0.21
p_predicted[1245] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1246] 0.06 0.00 0.04 0.01 0.04 0.06
p_predicted[1247] 0.09 0.00 0.06 0.02 0.05 0.08
p_predicted[1248] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted[1249] 0.07 0.00 0.04 0.02 0.05 0.07
p_predicted[1250] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted[1251] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted[1252] 0.07 0.00 0.04 0.02 0.05 0.07
p_predicted[1253] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted[1254] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted[1255] 0.07 0.00 0.04 0.02 0.05 0.07
p_predicted[1256] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted[1257] 0.12 0.00 0.06 0.03 0.07 0.11
p_predicted[1258] 0.08 0.00 0.04 0.02 0.05 0.08
p_predicted[1259] 0.12 0.00 0.06 0.03 0.07 0.11
p_predicted[1260] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted[1261] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted[1262] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted[1263] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1264] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1265] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1266] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1267] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted[1268] 0.07 0.00 0.05 0.01 0.03 0.06
p_predicted[1269] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted[1270] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1271] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1272] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1273] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1274] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1275] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted[1276] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1277] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted[1278] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1279] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1280] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1281] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1282] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1283] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1284] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1285] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1286] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1287] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted[1288] 0.03 0.00 0.03 0.00 0.01 0.02
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] 0.09 0.00 0.04 0.03 0.06 0.08
p_predicted[1294] 0.10 0.00 0.04 0.04 0.07 0.09
p_predicted[1295] 0.13 0.00 0.05 0.05 0.10 0.13
p_predicted[1296] 0.14 0.00 0.05 0.05 0.10 0.13
p_predicted[1297] 0.10 0.00 0.05 0.03 0.07 0.09
p_predicted[1298] 0.36 0.00 0.11 0.18 0.28 0.35
p_predicted[1299] 0.36 0.00 0.11 0.18 0.28 0.35
p_predicted[1300] 0.28 0.00 0.08 0.14 0.22 0.28
p_predicted[1301] 0.28 0.00 0.08 0.14 0.22 0.28
p_predicted[1302] 0.29 0.00 0.08 0.16 0.24 0.29
p_predicted[1303] 0.29 0.00 0.08 0.16 0.24 0.29
p_predicted[1304] 0.23 0.00 0.06 0.13 0.19 0.23
p_predicted[1305] 0.23 0.00 0.06 0.13 0.19 0.23
p_predicted[1306] 0.23 0.00 0.06 0.12 0.18 0.22
p_predicted[1307] 0.23 0.00 0.06 0.12 0.18 0.22
p_predicted[1308] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[1309] 0.21 0.00 0.06 0.11 0.17 0.21
p_predicted[1310] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted[1311] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted[1312] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1313] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1314] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1315] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1316] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1317] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1318] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1319] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1320] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1321] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1322] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1323] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1324] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1325] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1326] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1327] 0.27 0.00 0.07 0.15 0.22 0.27
p_predicted[1328] 0.22 0.00 0.06 0.12 0.18 0.21
p_predicted[1329] 0.21 0.00 0.06 0.11 0.17 0.20
p_predicted[1330] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1331] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1332] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1333] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1334] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1335] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1336] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1337] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1338] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted[1339] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[1] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted_default[2] 0.20 0.00 0.04 0.13 0.17 0.20
p_predicted_default[3] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted_default[4] 0.16 0.00 0.03 0.11 0.14 0.15
p_predicted_default[5] 0.06 0.00 0.03 0.02 0.04 0.06
p_predicted_default[6] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[7] 0.37 0.00 0.07 0.25 0.32 0.37
p_predicted_default[8] 0.20 0.00 0.10 0.05 0.12 0.19
p_predicted_default[9] 0.11 0.00 0.03 0.06 0.09 0.11
p_predicted_default[10] 0.01 0.00 0.01 0.00 0.00 0.01
p_predicted_default[11] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[12] 0.18 0.00 0.04 0.11 0.15 0.17
p_predicted_default[13] 0.08 0.00 0.03 0.04 0.06 0.07
p_predicted_default[14] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted_default[15] 0.10 0.00 0.03 0.05 0.08 0.10
p_predicted_default[16] 0.26 0.00 0.04 0.19 0.23 0.26
p_predicted_default[17] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[18] 0.08 0.00 0.03 0.03 0.06 0.07
p_predicted_default[19] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[20] 0.01 0.00 0.04 0.00 0.00 0.00
p_predicted_default[21] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted_default[22] 0.39 0.00 0.12 0.16 0.30 0.38
p_predicted_default[23] 0.38 0.00 0.12 0.15 0.30 0.38
p_predicted_default[24] 0.04 0.00 0.02 0.02 0.03 0.04
p_predicted_default[25] 0.42 0.00 0.06 0.30 0.38 0.43
p_predicted_default[26] 0.42 0.00 0.06 0.30 0.38 0.43
p_predicted_default[27] 0.42 0.00 0.06 0.30 0.38 0.43
p_predicted_default[28] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted_default[29] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted_default[30] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[31] 0.06 0.00 0.02 0.03 0.05 0.06
p_predicted_default[32] 0.07 0.00 0.05 0.01 0.03 0.05
p_predicted_default[33] 0.33 0.00 0.05 0.22 0.29 0.33
p_predicted_default[34] 0.33 0.00 0.05 0.22 0.29 0.33
p_predicted_default[35] 0.14 0.00 0.05 0.05 0.10 0.13
p_predicted_default[36] 0.14 0.00 0.06 0.05 0.09 0.13
p_predicted_default[37] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted_default[38] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted_default[39] 0.13 0.00 0.03 0.08 0.11 0.13
p_predicted_default[40] 0.25 0.00 0.05 0.16 0.21 0.25
p_predicted_default[41] 0.08 0.00 0.02 0.05 0.06 0.08
p_predicted_default[42] 0.24 0.00 0.06 0.14 0.20 0.23
p_predicted_default[43] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[44] 0.17 0.00 0.04 0.10 0.14 0.17
p_predicted_default[45] 0.18 0.00 0.03 0.11 0.15 0.17
p_predicted_default[46] 0.17 0.00 0.03 0.12 0.15 0.17
p_predicted_default[47] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[48] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[49] 0.18 0.00 0.04 0.12 0.15 0.18
p_predicted_default[50] 0.01 0.00 0.02 0.00 0.00 0.00
p_predicted_default[51] 0.27 0.00 0.04 0.19 0.24 0.27
p_predicted_default[52] 0.27 0.00 0.04 0.19 0.24 0.27
p_predicted_default[53] 0.27 0.00 0.04 0.19 0.24 0.27
p_predicted_default[54] 0.24 0.00 0.04 0.17 0.21 0.24
p_predicted_default[55] 0.24 0.00 0.04 0.17 0.21 0.24
p_predicted_default[56] 0.24 0.00 0.04 0.17 0.21 0.24
p_predicted_default[57] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[58] 0.08 0.00 0.04 0.02 0.05 0.07
p_predicted_default[59] 0.06 0.00 0.04 0.01 0.03 0.05
p_predicted_default[60] 0.17 0.00 0.04 0.11 0.15 0.17
p_predicted_default[61] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted_default[62] 0.10 0.00 0.05 0.03 0.06 0.09
p_predicted_default[63] 0.32 0.00 0.05 0.22 0.28 0.32
p_predicted_default[64] 0.74 0.00 0.14 0.43 0.66 0.76
p_predicted_default[65] 0.74 0.00 0.14 0.43 0.66 0.76
p_predicted_default[66] 0.06 0.00 0.03 0.01 0.03 0.05
p_predicted_default[67] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[68] 0.34 0.00 0.06 0.23 0.30 0.34
p_predicted_default[69] 0.19 0.00 0.07 0.08 0.14 0.19
p_predicted_default[70] 0.20 0.00 0.09 0.06 0.13 0.18
p_predicted_default[71] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[72] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[73] 0.05 0.00 0.03 0.01 0.02 0.04
p_predicted_default[74] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[75] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[76] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[77] 0.43 0.00 0.06 0.31 0.39 0.43
p_predicted_default[78] 0.19 0.00 0.04 0.12 0.16 0.19
p_predicted_default[79] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[80] 0.24 0.00 0.10 0.08 0.16 0.22
p_predicted_default[81] 0.42 0.00 0.12 0.20 0.34 0.42
p_predicted_default[82] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[83] 0.02 0.00 0.06 0.00 0.00 0.00
p_predicted_default[84] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[85] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[86] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[87] 0.26 0.00 0.04 0.17 0.23 0.26
p_predicted_default[88] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[89] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[90] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[91] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[92] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[93] 0.08 0.00 0.02 0.04 0.06 0.07
p_predicted_default[94] 0.10 0.00 0.03 0.05 0.07 0.09
p_predicted_default[95] 0.12 0.00 0.03 0.08 0.10 0.12
p_predicted_default[96] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[97] 0.18 0.00 0.06 0.09 0.14 0.17
p_predicted_default[98] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[99] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[100] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[101] 0.11 0.00 0.03 0.07 0.09 0.11
p_predicted_default[102] 0.18 0.00 0.04 0.11 0.15 0.17
p_predicted_default[103] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[104] 0.01 0.00 0.01 0.00 0.01 0.01
p_predicted_default[105] 0.02 0.00 0.02 0.00 0.01 0.02
p_predicted_default[106] 0.08 0.00 0.02 0.04 0.06 0.08
p_predicted_default[107] 0.47 0.00 0.07 0.32 0.42 0.47
p_predicted_default[108] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[109] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[110] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[111] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[112] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[113] 0.10 0.00 0.06 0.03 0.06 0.09
p_predicted_default[114] 0.23 0.00 0.10 0.07 0.15 0.21
p_predicted_default[115] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[116] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[117] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[118] 0.03 0.00 0.02 0.00 0.01 0.02
p_predicted_default[119] 0.03 0.00 0.02 0.01 0.02 0.03
p_predicted_default[120] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[121] 0.00 0.00 0.01 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] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[124] 0.13 0.00 0.04 0.06 0.10 0.13
p_predicted_default[125] 0.12 0.00 0.03 0.07 0.10 0.12
p_predicted_default[126] 0.13 0.00 0.04 0.06 0.10 0.12
p_predicted_default[127] 0.36 0.00 0.07 0.22 0.31 0.36
p_predicted_default[128] 0.12 0.00 0.03 0.08 0.10 0.12
p_predicted_default[129] 0.18 0.00 0.05 0.10 0.15 0.18
p_predicted_default[130] 0.01 0.00 0.01 0.00 0.00 0.00
p_predicted_default[131] 0.11 0.00 0.05 0.04 0.07 0.11
p_predicted_default[132] 0.08 0.00 0.04 0.03 0.05 0.08
p_predicted_default[133] 0.11 0.00 0.05 0.04 0.07 0.11
p_predicted_default[134] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[135] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[136] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[137] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[138] 0.05 0.00 0.02 0.01 0.03 0.04
p_predicted_default[139] 0.04 0.00 0.02 0.01 0.02 0.03
p_predicted_default[140] 0.05 0.00 0.02 0.01 0.03 0.04
p_predicted_default[141] 0.77 0.00 0.15 0.39 0.68 0.80
p_predicted_default[142] 0.07 0.00 0.03 0.03 0.05 0.07
p_predicted_default[143] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[144] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[145] 0.49 0.00 0.12 0.25 0.41 0.49
p_predicted_default[146] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[147] 0.05 0.00 0.03 0.01 0.03 0.04
p_predicted_default[148] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[149] 0.53 0.00 0.12 0.29 0.44 0.53
p_predicted_default[150] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted_default[151] 0.24 0.00 0.04 0.17 0.22 0.24
p_predicted_default[152] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted_default[153] 0.06 0.00 0.03 0.02 0.04 0.05
p_predicted_default[154] 0.09 0.00 0.05 0.02 0.05 0.08
p_predicted_default[155] 0.00 0.00 0.00 0.00 0.00 0.00
p_predicted_default[156] 0.05 0.00 0.04 0.01 0.02 0.04
p_predicted_default[157] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[158] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[159] 0.05 0.00 0.02 0.02 0.04 0.05
p_predicted_default[160] 0.03 0.00 0.03 0.00 0.01 0.02
p_predicted_default[161] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[162] 0.10 0.00 0.05 0.03 0.07 0.09
p_predicted_default[163] 0.23 0.00 0.06 0.13 0.19 0.23
p_predicted_default[164] 0.23 0.00 0.06 0.13 0.19 0.23
p_predicted_default[165] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[166] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_default[167] 0.22 0.00 0.06 0.12 0.18 0.21
p_predicted_default[168] 0.00 0.00 0.01 0.00 0.00 0.00
p_predicted_intervention[1] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[2] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[3] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[4] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[5] 0.32 0.00 0.36 0.00 0.01 0.14
p_predicted_intervention[6] 0.29 0.00 0.37 0.00 0.00 0.06
p_predicted_intervention[7] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[8] 0.27 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[9] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[10] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[11] 0.27 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[12] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[13] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[14] 0.16 0.00 0.12 0.01 0.07 0.13
p_predicted_intervention[15] 0.16 0.00 0.12 0.01 0.07 0.13
p_predicted_intervention[16] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[17] 0.29 0.00 0.37 0.00 0.00 0.06
p_predicted_intervention[18] 0.32 0.00 0.36 0.00 0.01 0.13
p_predicted_intervention[19] 0.31 0.00 0.36 0.00 0.01 0.13
p_predicted_intervention[20] 0.21 0.00 0.38 0.00 0.00 0.00
p_predicted_intervention[21] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[22] 0.12 0.00 0.30 0.00 0.00 0.00
p_predicted_intervention[23] 0.12 0.00 0.30 0.00 0.00 0.00
p_predicted_intervention[24] 0.29 0.00 0.37 0.00 0.00 0.06
p_predicted_intervention[25] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[26] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[27] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[28] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[29] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[30] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[31] 0.00 0.00 0.02 0.00 0.00 0.00
p_predicted_intervention[32] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[33] 0.25 0.00 0.11 0.06 0.17 0.25
p_predicted_intervention[34] 0.25 0.00 0.11 0.06 0.17 0.25
p_predicted_intervention[35] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[36] 0.02 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[37] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[38] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[39] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[40] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[41] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[42] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[43] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[44] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[45] 0.22 0.00 0.11 0.04 0.13 0.20
p_predicted_intervention[46] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[47] 0.27 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[48] 0.27 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[49] 0.22 0.00 0.11 0.04 0.13 0.21
p_predicted_intervention[50] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[51] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[52] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[53] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[54] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[55] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[56] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[57] 0.23 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[58] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[59] 0.28 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[60] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[61] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[62] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[63] 0.25 0.00 0.11 0.06 0.16 0.24
p_predicted_intervention[64] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[65] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[66] 0.32 0.00 0.43 0.00 0.00 0.01
p_predicted_intervention[67] 0.28 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[68] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[69] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[70] 0.02 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[71] 0.30 0.00 0.37 0.00 0.00 0.08
p_predicted_intervention[72] 0.30 0.00 0.37 0.00 0.00 0.08
p_predicted_intervention[73] 0.32 0.00 0.43 0.00 0.00 0.00
p_predicted_intervention[74] 0.28 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[75] 0.28 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[76] 0.28 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[77] 0.00 0.00 0.04 0.00 0.00 0.00
p_predicted_intervention[78] 0.23 0.00 0.12 0.04 0.14 0.22
p_predicted_intervention[79] 0.22 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[80] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[81] 0.49 0.00 0.41 0.00 0.04 0.48
p_predicted_intervention[82] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[83] 0.27 0.00 0.42 0.00 0.00 0.00
p_predicted_intervention[84] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[85] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[86] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[87] 0.28 0.00 0.10 0.08 0.20 0.28
p_predicted_intervention[88] 0.23 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[89] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[90] 0.33 0.00 0.43 0.00 0.00 0.01
p_predicted_intervention[91] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[92] 0.03 0.00 0.14 0.00 0.00 0.00
p_predicted_intervention[93] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[94] 0.16 0.00 0.13 0.01 0.06 0.13
p_predicted_intervention[95] 0.22 0.00 0.10 0.05 0.14 0.22
p_predicted_intervention[96] 0.23 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[97] 0.01 0.00 0.05 0.00 0.00 0.00
p_predicted_intervention[98] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[99] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[100] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[101] 0.20 0.00 0.11 0.03 0.11 0.18
p_predicted_intervention[102] 0.01 0.00 0.05 0.00 0.00 0.00
p_predicted_intervention[103] 0.28 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[104] 0.28 0.00 0.41 0.00 0.00 0.01
p_predicted_intervention[105] 0.28 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[106] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[107] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[108] 0.29 0.00 0.34 0.00 0.02 0.13
p_predicted_intervention[109] 0.29 0.00 0.34 0.00 0.02 0.13
p_predicted_intervention[110] 0.29 0.00 0.34 0.00 0.02 0.13
p_predicted_intervention[111] 0.26 0.00 0.41 0.00 0.00 0.00
p_predicted_intervention[112] 0.23 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[113] 0.27 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[114] 0.26 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[115] 0.33 0.00 0.43 0.00 0.00 0.01
p_predicted_intervention[116] 0.32 0.00 0.43 0.00 0.00 0.01
p_predicted_intervention[117] 0.32 0.00 0.43 0.00 0.00 0.01
p_predicted_intervention[118] 0.30 0.00 0.37 0.00 0.00 0.07
p_predicted_intervention[119] 0.30 0.00 0.36 0.00 0.01 0.09
p_predicted_intervention[120] 0.19 0.00 0.36 0.00 0.00 0.00
p_predicted_intervention[121] 0.19 0.00 0.36 0.00 0.00 0.00
p_predicted_intervention[122] 0.19 0.00 0.36 0.00 0.00 0.00
p_predicted_intervention[123] 0.33 0.00 0.34 0.00 0.03 0.18
p_predicted_intervention[124] 0.33 0.00 0.34 0.00 0.03 0.18
p_predicted_intervention[125] 0.21 0.00 0.12 0.03 0.11 0.19
p_predicted_intervention[126] 0.29 0.00 0.35 0.00 0.01 0.10
p_predicted_intervention[127] 0.00 0.00 0.03 0.00 0.00 0.00
p_predicted_intervention[128] 0.23 0.00 0.11 0.05 0.15 0.22
p_predicted_intervention[129] 0.29 0.00 0.33 0.00 0.02 0.12
p_predicted_intervention[130] 0.23 0.00 0.39 0.00 0.00 0.00
p_predicted_intervention[131] 0.26 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[132] 0.27 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[133] 0.26 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[134] 0.28 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[135] 0.28 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[136] 0.28 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[137] 0.20 0.00 0.37 0.00 0.00 0.00
p_predicted_intervention[138] 0.28 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[139] 0.28 0.00 0.40 0.00 0.00 0.01
p_predicted_intervention[140] 0.28 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[141] 0.06 0.00 0.22 0.00 0.00 0.00
p_predicted_intervention[142] 0.15 0.00 0.12 0.01 0.05 0.11
p_predicted_intervention[143] 0.32 0.00 0.43 0.00 0.00 0.01
p_predicted_intervention[144] 0.32 0.00 0.43 0.00 0.00 0.01
p_predicted_intervention[145] 0.51 0.00 0.41 0.00 0.05 0.53
p_predicted_intervention[146] 0.23 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[147] 0.32 0.00 0.43 0.00 0.00 0.00
p_predicted_intervention[148] 0.03 0.00 0.13 0.00 0.00 0.00
p_predicted_intervention[149] 0.12 0.00 0.31 0.00 0.00 0.00
p_predicted_intervention[150] 0.27 0.00 0.10 0.08 0.19 0.26
p_predicted_intervention[151] 0.27 0.00 0.10 0.08 0.19 0.26
p_predicted_intervention[152] 0.26 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[153] 0.27 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[154] 0.26 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[155] 0.19 0.00 0.36 0.00 0.00 0.00
p_predicted_intervention[156] 0.26 0.00 0.39 0.00 0.00 0.01
p_predicted_intervention[157] 0.19 0.00 0.36 0.00 0.00 0.00
p_predicted_intervention[158] 0.30 0.00 0.37 0.00 0.00 0.08
p_predicted_intervention[159] 0.30 0.00 0.37 0.00 0.00 0.08
p_predicted_intervention[160] 1.00 0.00 0.00 1.00 1.00 1.00
p_predicted_intervention[161] 0.20 0.00 0.37 0.00 0.00 0.00
p_predicted_intervention[162] 0.27 0.00 0.40 0.00 0.00 0.00
p_predicted_intervention[163] 0.30 0.00 0.34 0.00 0.02 0.13
p_predicted_intervention[164] 0.30 0.00 0.34 0.00 0.02 0.13
p_predicted_intervention[165] 0.20 0.00 0.37 0.00 0.00 0.00
p_predicted_intervention[166] 0.20 0.00 0.37 0.00 0.00 0.00
p_predicted_intervention[167] 0.32 0.00 0.34 0.00 0.03 0.16
p_predicted_intervention[168] 0.20 0.00 0.37 0.00 0.00 0.00
predicted_difference[1] 0.97 0.00 0.03 0.89 0.96 0.98
predicted_difference[2] -0.19 0.00 0.05 -0.28 -0.22 -0.19
predicted_difference[3] -0.15 0.00 0.04 -0.21 -0.17 -0.15
predicted_difference[4] -0.15 0.00 0.04 -0.21 -0.17 -0.15
predicted_difference[5] 0.26 0.00 0.37 -0.11 -0.04 0.08
predicted_difference[6] 0.25 0.00 0.37 -0.06 -0.03 0.03
predicted_difference[7] -0.37 0.00 0.07 -0.50 -0.41 -0.36
predicted_difference[8] 0.06 0.00 0.43 -0.44 -0.23 -0.13
predicted_difference[9] -0.11 0.00 0.04 -0.18 -0.13 -0.11
predicted_difference[10] 0.99 0.00 0.01 0.97 0.99 0.99
predicted_difference[11] 0.26 0.00 0.42 -0.04 0.00 0.00
predicted_difference[12] -0.17 0.00 0.06 -0.25 -0.20 -0.17
predicted_difference[13] -0.08 0.00 0.03 -0.14 -0.09 -0.07
predicted_difference[14] 0.06 0.00 0.12 -0.11 -0.03 0.03
predicted_difference[15] 0.06 0.00 0.12 -0.11 -0.03 0.03
predicted_difference[16] -0.26 0.00 0.05 -0.34 -0.29 -0.26
predicted_difference[17] 0.25 0.00 0.37 -0.06 -0.03 0.03
predicted_difference[18] 0.24 0.00 0.36 -0.11 -0.05 0.06
predicted_difference[19] 0.24 0.00 0.36 -0.11 -0.05 0.06
predicted_difference[20] 0.20 0.00 0.38 -0.07 0.00 0.00
predicted_difference[21] -0.12 0.00 0.04 -0.19 -0.14 -0.12
predicted_difference[22] -0.27 0.00 0.29 -0.61 -0.43 -0.34
predicted_difference[23] -0.27 0.00 0.29 -0.60 -0.43 -0.34
predicted_difference[24] 0.24 0.00 0.37 -0.07 -0.03 0.02
predicted_difference[25] -0.42 0.00 0.07 -0.55 -0.47 -0.42
predicted_difference[26] -0.42 0.00 0.07 -0.55 -0.47 -0.42
predicted_difference[27] -0.42 0.00 0.07 -0.55 -0.47 -0.42
predicted_difference[28] 0.99 0.00 0.01 0.96 0.98 0.99
predicted_difference[29] 0.99 0.00 0.01 0.96 0.98 0.99
predicted_difference[30] -0.05 0.00 0.03 -0.10 -0.06 -0.05
predicted_difference[31] -0.06 0.00 0.03 -0.12 -0.08 -0.06
predicted_difference[32] 0.93 0.00 0.05 0.80 0.91 0.95
predicted_difference[33] -0.08 0.00 0.10 -0.26 -0.15 -0.08
predicted_difference[34] -0.08 0.00 0.10 -0.26 -0.15 -0.08
predicted_difference[35] -0.11 0.00 0.14 -0.25 -0.16 -0.13
predicted_difference[36] -0.11 0.00 0.15 -0.27 -0.17 -0.13
predicted_difference[37] -0.12 0.00 0.04 -0.19 -0.14 -0.12
predicted_difference[38] -0.12 0.00 0.04 -0.19 -0.14 -0.12
predicted_difference[39] -0.12 0.00 0.04 -0.19 -0.14 -0.12
predicted_difference[40] -0.25 0.00 0.06 -0.36 -0.28 -0.24
predicted_difference[41] -0.08 0.00 0.04 -0.12 -0.09 -0.08
predicted_difference[42] -0.24 0.00 0.06 -0.36 -0.27 -0.23
predicted_difference[43] -0.18 0.00 0.06 -0.28 -0.22 -0.19
predicted_difference[44] -0.16 0.00 0.06 -0.25 -0.19 -0.16
predicted_difference[45] 0.04 0.00 0.11 -0.13 -0.04 0.03
predicted_difference[46] -0.17 0.00 0.04 -0.23 -0.19 -0.17
predicted_difference[47] 0.22 0.00 0.40 -0.10 -0.05 -0.03
predicted_difference[48] 0.22 0.00 0.40 -0.10 -0.05 -0.03
predicted_difference[49] 0.04 0.00 0.11 -0.13 -0.04 0.03
predicted_difference[50] 0.25 0.00 0.41 -0.03 0.00 0.00
predicted_difference[51] -0.27 0.00 0.05 -0.35 -0.30 -0.27
predicted_difference[52] -0.27 0.00 0.05 -0.35 -0.30 -0.27
predicted_difference[53] -0.27 0.00 0.05 -0.35 -0.30 -0.27
predicted_difference[54] -0.24 0.00 0.05 -0.33 -0.27 -0.24
predicted_difference[55] -0.24 0.00 0.05 -0.33 -0.27 -0.24
predicted_difference[56] -0.24 0.00 0.05 -0.33 -0.27 -0.24
predicted_difference[57] 0.22 0.00 0.39 -0.02 0.00 0.00
predicted_difference[58] -0.05 0.00 0.13 -0.16 -0.09 -0.07
predicted_difference[59] 0.21 0.00 0.42 -0.17 -0.08 -0.04
predicted_difference[60] -0.17 0.00 0.06 -0.26 -0.20 -0.17
predicted_difference[61] -0.07 0.00 0.14 -0.23 -0.13 -0.09
predicted_difference[62] -0.07 0.00 0.15 -0.22 -0.12 -0.09
predicted_difference[63] -0.07 0.00 0.10 -0.26 -0.15 -0.08
predicted_difference[64] 0.26 0.00 0.14 0.05 0.15 0.24
predicted_difference[65] 0.26 0.00 0.14 0.05 0.15 0.24
predicted_difference[66] 0.26 0.00 0.44 -0.13 -0.06 -0.02
predicted_difference[67] 0.25 0.00 0.41 -0.09 -0.04 -0.02
predicted_difference[68] -0.34 0.00 0.07 -0.46 -0.38 -0.34
predicted_difference[69] -0.17 0.00 0.16 -0.34 -0.23 -0.18
predicted_difference[70] -0.17 0.00 0.17 -0.40 -0.25 -0.18
predicted_difference[71] 0.27 0.00 0.37 -0.06 -0.02 0.05
predicted_difference[72] 0.27 0.00 0.37 -0.06 -0.02 0.05
predicted_difference[73] 0.28 0.00 0.42 -0.07 -0.03 -0.01
predicted_difference[74] 0.22 0.00 0.39 -0.09 -0.05 -0.02
predicted_difference[75] 0.24 0.00 0.41 -0.08 -0.04 -0.02
predicted_difference[76] 0.22 0.00 0.39 -0.09 -0.05 -0.02
predicted_difference[77] -0.43 0.00 0.07 -0.55 -0.47 -0.43
predicted_difference[78] 0.04 0.00 0.11 -0.14 -0.05 0.03
predicted_difference[79] 0.22 0.00 0.39 -0.02 0.00 0.00
predicted_difference[80] -0.21 0.00 0.18 -0.47 -0.30 -0.22
predicted_difference[81] 0.07 0.01 0.46 -0.63 -0.38 0.06
predicted_difference[82] 0.25 0.00 0.41 -0.01 0.00 0.00
predicted_difference[83] 0.25 0.00 0.41 -0.09 0.00 0.00
predicted_difference[84] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[85] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[86] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[87] 0.02 0.00 0.09 -0.15 -0.05 0.02
predicted_difference[88] 0.22 0.00 0.39 -0.02 0.00 0.00
predicted_difference[89] 0.98 0.00 0.02 0.94 0.97 0.98
predicted_difference[90] 0.28 0.00 0.44 -0.11 -0.05 -0.02
predicted_difference[91] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[92] -0.02 0.00 0.12 -0.12 -0.06 -0.04
predicted_difference[93] -0.07 0.00 0.03 -0.12 -0.09 -0.07
predicted_difference[94] 0.06 0.00 0.13 -0.11 -0.03 0.03
predicted_difference[95] 0.10 0.00 0.10 -0.07 0.02 0.09
predicted_difference[96] 0.22 0.00 0.39 -0.02 0.00 0.00
predicted_difference[97] -0.17 0.00 0.08 -0.30 -0.21 -0.17
predicted_difference[98] 0.28 0.00 0.37 -0.06 -0.02 0.05
predicted_difference[99] 0.26 0.00 0.41 0.00 0.00 0.00
predicted_difference[100] 0.25 0.00 0.41 -0.01 0.00 0.00
predicted_difference[101] 0.09 0.00 0.11 -0.08 0.00 0.07
predicted_difference[102] -0.17 0.00 0.06 -0.25 -0.20 -0.17
predicted_difference[103] 0.26 0.00 0.40 -0.05 -0.02 0.00
predicted_difference[104] 0.27 0.00 0.41 -0.04 -0.01 0.00
predicted_difference[105] 0.26 0.00 0.40 -0.05 -0.02 0.00
predicted_difference[106] -0.08 0.00 0.03 -0.13 -0.09 -0.08
predicted_difference[107] -0.47 0.00 0.08 -0.61 -0.52 -0.47
predicted_difference[108] 0.11 0.00 0.32 -0.23 -0.14 -0.05
predicted_difference[109] 0.11 0.00 0.32 -0.23 -0.14 -0.05
predicted_difference[110] 0.11 0.00 0.32 -0.23 -0.14 -0.05
predicted_difference[111] 0.26 0.00 0.41 -0.01 0.00 0.00
predicted_difference[112] 0.23 0.00 0.39 -0.02 0.00 0.00
predicted_difference[113] 0.17 0.00 0.43 -0.24 -0.12 -0.06
predicted_difference[114] 0.04 0.00 0.42 -0.45 -0.25 -0.15
predicted_difference[115] 0.28 0.00 0.44 -0.11 -0.05 -0.02
predicted_difference[116] 0.28 0.00 0.44 -0.11 -0.05 -0.02
predicted_difference[117] 0.28 0.00 0.44 -0.11 -0.05 -0.02
predicted_difference[118] 0.27 0.00 0.38 -0.08 -0.02 0.05
predicted_difference[119] 0.27 0.00 0.37 -0.08 -0.02 0.06
predicted_difference[120] 0.19 0.00 0.36 0.00 0.00 0.00
predicted_difference[121] 0.19 0.00 0.36 0.00 0.00 0.00
predicted_difference[122] 0.19 0.00 0.36 0.00 0.00 0.00
predicted_difference[123] 0.20 0.00 0.36 -0.21 -0.11 0.05
predicted_difference[124] 0.20 0.00 0.36 -0.21 -0.11 0.05
predicted_difference[125] 0.09 0.00 0.12 -0.09 0.00 0.07
predicted_difference[126] 0.16 0.00 0.33 -0.14 -0.09 -0.02
predicted_difference[127] -0.36 0.00 0.08 -0.51 -0.41 -0.36
predicted_difference[128] 0.10 0.00 0.10 -0.07 0.02 0.09
predicted_difference[129] 0.11 0.00 0.32 -0.23 -0.14 -0.05
predicted_difference[130] 0.22 0.00 0.39 -0.02 0.00 0.00
predicted_difference[131] 0.15 0.00 0.36 -0.16 -0.09 -0.05
predicted_difference[132] 0.19 0.00 0.39 -0.13 -0.07 -0.04
predicted_difference[133] 0.15 0.00 0.36 -0.16 -0.09 -0.05
predicted_difference[134] 0.22 0.00 0.39 -0.10 -0.05 -0.02
predicted_difference[135] 0.24 0.00 0.41 -0.08 -0.04 -0.02
predicted_difference[136] 0.22 0.00 0.39 -0.10 -0.05 -0.02
predicted_difference[137] 0.19 0.00 0.36 -0.01 0.00 0.00
predicted_difference[138] 0.23 0.00 0.40 -0.10 -0.05 -0.02
predicted_difference[139] 0.25 0.00 0.41 -0.09 -0.04 -0.02
predicted_difference[140] 0.23 0.00 0.40 -0.10 -0.05 -0.02
predicted_difference[141] -0.70 0.00 0.26 -0.97 -0.87 -0.77
predicted_difference[142] 0.07 0.00 0.13 -0.09 -0.02 0.04
predicted_difference[143] 0.28 0.00 0.44 -0.11 -0.05 -0.02
predicted_difference[144] 0.28 0.00 0.44 -0.11 -0.05 -0.02
predicted_difference[145] 0.02 0.01 0.44 -0.67 -0.41 0.04
predicted_difference[146] 0.23 0.00 0.40 0.00 0.00 0.00
predicted_difference[147] 0.27 0.00 0.42 -0.08 -0.04 -0.01
predicted_difference[148] -0.03 0.00 0.12 -0.14 -0.07 -0.04
predicted_difference[149] -0.40 0.00 0.34 -0.74 -0.60 -0.50
predicted_difference[150] 0.02 0.00 0.09 -0.14 -0.05 0.02
predicted_difference[151] 0.02 0.00 0.09 -0.14 -0.05 0.02
predicted_difference[152] 0.18 0.00 0.36 -0.12 -0.06 -0.03
predicted_difference[153] 0.21 0.00 0.38 -0.09 -0.05 -0.02
predicted_difference[154] 0.18 0.00 0.36 -0.12 -0.06 -0.03
predicted_difference[155] 0.19 0.00 0.36 -0.01 0.00 0.00
predicted_difference[156] 0.21 0.00 0.37 -0.07 -0.03 -0.01
predicted_difference[157] 0.19 0.00 0.36 -0.01 0.00 0.00
predicted_difference[158] 0.25 0.00 0.37 -0.09 -0.04 0.03
predicted_difference[159] 0.25 0.00 0.37 -0.09 -0.04 0.03
predicted_difference[160] 0.97 0.00 0.03 0.90 0.96 0.98
predicted_difference[161] 0.19 0.00 0.37 -0.01 0.00 0.00
predicted_difference[162] 0.17 0.00 0.41 -0.20 -0.11 -0.06
predicted_difference[163] 0.07 0.00 0.32 -0.28 -0.18 -0.09
predicted_difference[164] 0.07 0.00 0.32 -0.28 -0.18 -0.09
predicted_difference[165] 0.19 0.00 0.37 -0.01 0.00 0.00
predicted_difference[166] 0.19 0.00 0.37 -0.01 0.00 0.00
predicted_difference[167] 0.10 0.00 0.34 -0.30 -0.17 -0.05
predicted_difference[168] 0.19 0.00 0.36 -0.01 0.00 0.00
lp__ -308.75 1.66 35.51 -376.50 -333.14 -309.40
75% 97.5% n_eff Rhat
mu[1] 0.01 0.07 10913 1.00
mu[2] 0.02 0.08 17190 1.00
mu[3] 0.04 0.10 21103 1.00
mu[4] -0.01 0.05 11281 1.00
mu[5] 0.00 0.06 8515 1.00
mu[6] 0.00 0.06 8475 1.00
mu[7] 0.02 0.07 8912 1.00
mu[8] 0.03 0.09 9599 1.00
mu[9] 0.03 0.09 14931 1.00
mu[10] 0.03 0.09 10124 1.00
mu[11] 0.04 0.10 12399 1.00
mu[12] 0.00 0.06 11239 1.00
sigma[1] 0.31 0.49 660 1.00
sigma[2] 0.81 1.06 2733 1.00
sigma[3] 0.84 1.09 2445 1.00
sigma[4] 0.35 0.50 1686 1.00
sigma[5] 0.23 0.38 622 1.00
sigma[6] 0.23 0.39 667 1.00
sigma[7] 0.24 0.38 692 1.01
sigma[8] 0.24 0.39 819 1.00
sigma[9] 0.40 0.64 608 1.00
sigma[10] 0.26 0.44 665 1.00
sigma[11] 0.30 0.49 617 1.01
sigma[12] 0.38 0.59 666 1.01
beta[1,1] 0.06 0.35 9714 1.00
beta[1,2] -0.15 0.35 11109 1.00
beta[1,3] 0.94 1.45 9610 1.00
beta[1,4] -0.38 -0.24 7256 1.00
beta[1,5] 0.10 0.39 7596 1.00
beta[1,6] 0.13 0.42 6381 1.00
beta[1,7] 0.17 0.43 6398 1.00
beta[1,8] 0.16 0.40 5448 1.00
beta[1,9] 0.51 1.21 1716 1.00
beta[1,10] 0.09 0.40 11060 1.00
beta[1,11] 0.15 0.49 12135 1.00
beta[1,12] -0.05 0.24 3617 1.00
beta[2,1] -0.14 0.06 1404 1.00
beta[2,2] -1.25 -0.90 4078 1.00
beta[2,3] 0.89 1.17 7778 1.00
beta[2,4] 0.39 0.69 5553 1.00
beta[2,5] 0.03 0.26 6624 1.00
beta[2,6] -0.01 0.19 3815 1.00
beta[2,7] 0.02 0.23 6055 1.00
beta[2,8] 0.13 0.41 9304 1.00
beta[2,9] -0.17 0.10 1151 1.00
beta[2,10] 0.12 0.48 12043 1.00
beta[2,11] -0.01 0.20 1842 1.00
beta[2,12] -0.19 0.06 1301 1.00
beta[3,1] 0.12 0.55 14161 1.00
beta[3,2] 0.38 1.38 18254 1.00
beta[3,3] 0.36 1.37 17245 1.00
beta[3,4] 0.00 0.32 11470 1.00
beta[3,5] 0.02 0.26 6295 1.00
beta[3,6] 0.02 0.27 6891 1.00
beta[3,7] 0.04 0.26 6551 1.00
beta[3,8] 0.06 0.28 6273 1.00
beta[3,9] 0.17 0.69 12809 1.00
beta[3,10] 0.12 0.47 13372 1.00
beta[3,11] 0.13 0.52 13244 1.00
beta[3,12] 0.13 0.64 12473 1.00
beta[4,1] 0.11 0.50 13211 1.00
beta[4,2] 0.03 0.65 12961 1.00
beta[4,3] -0.38 0.26 10279 1.00
beta[4,4] 0.21 0.55 10454 1.00
beta[4,5] 0.07 0.33 11210 1.00
beta[4,6] 0.03 0.27 7164 1.00
beta[4,7] 0.10 0.38 11934 1.00
beta[4,8] 0.18 0.49 6139 1.00
beta[4,9] 0.08 0.48 5276 1.00
beta[4,10] 0.11 0.45 10073 1.00
beta[4,11] 0.35 0.95 2187 1.00
beta[4,12] -0.01 0.32 3811 1.00
beta[5,1] 0.07 0.40 8534 1.00
beta[5,2] -0.45 0.35 7376 1.00
beta[5,3] 0.31 1.25 14648 1.00
beta[5,4] 0.18 0.51 11318 1.00
beta[5,5] 0.08 0.35 10793 1.00
beta[5,6] 0.06 0.31 7068 1.00
beta[5,7] 0.15 0.47 6983 1.00
beta[5,8] 0.20 0.56 5081 1.00
beta[5,9] 0.19 0.72 12686 1.00
beta[5,10] 0.11 0.44 11152 1.00
beta[5,11] 0.22 0.65 5881 1.00
beta[5,12] 0.00 0.36 4178 1.00
beta[6,1] 0.11 0.51 15565 1.00
beta[6,2] 1.89 2.98 4941 1.00
beta[6,3] 2.49 3.60 4097 1.00
beta[6,4] -0.19 0.08 6278 1.00
beta[6,5] -0.01 0.21 4193 1.00
beta[6,6] 0.03 0.28 8874 1.00
beta[6,7] 0.07 0.32 10588 1.00
beta[6,8] 0.11 0.38 10956 1.00
beta[6,9] 0.19 0.72 15089 1.00
beta[6,10] 0.12 0.47 10206 1.00
beta[6,11] 0.11 0.47 12245 1.00
beta[6,12] 0.14 0.62 14268 1.00
beta[7,1] 0.12 0.52 13466 1.00
beta[7,2] 0.29 1.20 15408 1.00
beta[7,3] 0.30 1.26 15757 1.00
beta[7,4] -0.06 0.26 8146 1.00
beta[7,5] 0.00 0.22 4778 1.00
beta[7,6] 0.00 0.22 4733 1.00
beta[7,7] 0.02 0.26 5930 1.00
beta[7,8] 0.04 0.28 4984 1.00
beta[7,9] 0.18 0.71 13779 1.00
beta[7,10] 0.12 0.48 11406 1.00
beta[7,11] 0.13 0.54 12893 1.00
beta[7,12] 0.13 0.62 14016 1.00
beta[8,1] 0.12 0.55 13519 1.00
beta[8,2] 0.47 1.49 18875 1.00
beta[8,3] 0.48 1.49 17612 1.00
beta[8,4] 0.14 0.57 15524 1.00
beta[8,5] 0.07 0.39 12329 1.00
beta[8,6] 0.08 0.43 12359 1.00
beta[8,7] 0.10 0.42 13732 1.00
beta[8,8] 0.11 0.46 13440 1.00
beta[8,9] 0.18 0.71 13498 1.00
beta[8,10] 0.12 0.46 13491 1.00
beta[8,11] 0.14 0.57 12100 1.00
beta[8,12] 0.14 0.65 11523 1.00
beta[9,1] 0.10 0.51 13598 1.00
beta[9,2] -0.05 0.67 9808 1.00
beta[9,3] -0.17 0.59 8908 1.00
beta[9,4] 0.16 0.50 9094 1.00
beta[9,5] 0.13 0.46 5996 1.00
beta[9,6] 0.18 0.55 3847 1.00
beta[9,7] 0.21 0.56 3526 1.00
beta[9,8] 0.21 0.58 4095 1.00
beta[9,9] 0.23 0.81 11066 1.00
beta[9,10] 0.12 0.48 12524 1.00
beta[9,11] 0.10 0.44 9208 1.00
beta[9,12] 0.17 0.69 14192 1.00
beta[10,1] 0.12 0.52 12294 1.00
beta[10,2] 0.29 1.23 14926 1.00
beta[10,3] 0.34 1.32 17121 1.00
beta[10,4] -0.03 0.30 7997 1.00
beta[10,5] 0.01 0.24 5599 1.00
beta[10,6] 0.01 0.24 5483 1.00
beta[10,7] 0.03 0.27 6379 1.00
beta[10,8] 0.05 0.28 5707 1.00
beta[10,9] 0.17 0.72 13880 1.00
beta[10,10] 0.12 0.47 14752 1.00
beta[10,11] 0.14 0.53 13054 1.00
beta[10,12] 0.13 0.65 13328 1.00
beta[11,1] 0.12 0.55 12198 1.00
beta[11,2] 0.37 1.35 15888 1.00
beta[11,3] 0.38 1.37 17542 1.00
beta[11,4] -0.07 0.23 7653 1.00
beta[11,5] 0.00 0.23 4900 1.00
beta[11,6] 0.01 0.23 4612 1.00
beta[11,7] 0.02 0.25 5813 1.00
beta[11,8] 0.04 0.28 4290 1.00
beta[11,9] 0.18 0.70 14809 1.00
beta[11,10] 0.12 0.47 12481 1.00
beta[11,11] 0.13 0.52 12471 1.00
beta[11,12] 0.14 0.65 13417 1.00
beta[12,1] 0.02 0.30 4181 1.00
beta[12,2] -0.04 0.75 12523 1.00
beta[12,3] 0.78 1.69 16471 1.00
beta[12,4] -0.02 0.28 10229 1.00
beta[12,5] 0.04 0.29 10577 1.00
beta[12,6] 0.10 0.42 9433 1.00
beta[12,7] 0.12 0.38 11004 1.00
beta[12,8] 0.15 0.46 9992 1.00
beta[12,9] 0.21 0.77 13831 1.00
beta[12,10] 0.12 0.50 12198 1.00
beta[12,11] 0.18 0.62 9984 1.00
beta[12,12] 0.04 0.42 6160 1.00
beta[13,1] 0.24 0.73 5521 1.00
beta[13,2] 1.28 1.91 8461 1.00
beta[13,3] -0.78 -0.19 7323 1.00
beta[13,4] 0.07 0.39 12260 1.00
beta[13,5] 0.04 0.28 11095 1.00
beta[13,6] 0.07 0.34 10900 1.00
beta[13,7] 0.11 0.39 11409 1.00
beta[13,8] 0.13 0.43 12074 1.00
beta[13,9] 0.13 0.57 14170 1.00
beta[13,10] 0.11 0.45 13294 1.00
beta[13,11] 0.25 0.71 4497 1.00
beta[13,12] -0.03 0.27 3169 1.00
beta[14,1] 0.13 0.56 13416 1.00
beta[14,2] 0.27 1.22 15202 1.00
beta[14,3] 0.28 1.20 15341 1.00
beta[14,4] 0.00 0.34 10135 1.00
beta[14,5] 0.03 0.27 5988 1.00
beta[14,6] 0.03 0.27 7944 1.00
beta[14,7] 0.05 0.29 8193 1.00
beta[14,8] 0.06 0.32 6469 1.00
beta[14,9] 0.17 0.71 14944 1.00
beta[14,10] 0.12 0.48 12708 1.00
beta[14,11] 0.14 0.55 13795 1.00
beta[14,12] 0.14 0.64 15079 1.00
beta[15,1] 0.13 0.55 13664 1.00
beta[15,2] 0.45 1.42 16743 1.00
beta[15,3] 0.48 1.56 17517 1.00
beta[15,4] 0.14 0.61 16833 1.00
beta[15,5] 0.07 0.39 12818 1.00
beta[15,6] 0.08 0.40 13092 1.00
beta[15,7] 0.10 0.41 14253 1.00
beta[15,8] 0.11 0.44 11935 1.00
beta[15,9] 0.18 0.71 15033 1.00
beta[15,10] 0.12 0.50 10851 1.00
beta[15,11] 0.14 0.56 13617 1.00
beta[15,12] 0.15 0.66 14027 1.00
beta[16,1] 0.13 0.54 12840 1.00
beta[16,2] 0.43 1.43 15978 1.00
beta[16,3] 0.49 1.55 17704 1.00
beta[16,4] 0.14 0.58 14752 1.00
beta[16,5] 0.07 0.39 12385 1.00
beta[16,6] 0.08 0.40 13233 1.00
beta[16,7] 0.10 0.42 11986 1.00
beta[16,8] 0.11 0.44 13483 1.00
beta[16,9] 0.17 0.69 14689 1.00
beta[16,10] 0.12 0.47 12499 1.00
beta[16,11] 0.14 0.56 13144 1.00
beta[16,12] 0.14 0.63 13453 1.00
beta[17,1] 0.12 0.54 13258 1.00
beta[17,2] 0.35 1.32 16819 1.00
beta[17,3] 0.37 1.38 16163 1.00
beta[17,4] -0.02 0.31 9159 1.00
beta[17,5] 0.01 0.24 5222 1.00
beta[17,6] 0.01 0.25 5242 1.00
beta[17,7] 0.03 0.27 6749 1.00
beta[17,8] 0.05 0.27 5509 1.00
beta[17,9] 0.17 0.72 15097 1.00
beta[17,10] 0.12 0.49 12285 1.00
beta[17,11] 0.14 0.53 14181 1.00
beta[17,12] 0.14 0.64 13948 1.00
beta[18,1] 0.13 0.55 13580 1.00
beta[18,2] 0.39 1.36 15194 1.00
beta[18,3] 0.38 1.41 16604 1.00
beta[18,4] 0.01 0.34 10418 1.00
beta[18,5] 0.02 0.27 6296 1.00
beta[18,6] 0.03 0.27 7151 1.00
beta[18,7] 0.05 0.30 8001 1.00
beta[18,8] 0.07 0.30 7229 1.00
beta[18,9] 0.17 0.68 14670 1.00
beta[18,10] 0.12 0.48 12019 1.00
beta[18,11] 0.14 0.57 12627 1.00
beta[18,12] 0.14 0.64 13332 1.00
beta[19,1] 0.12 0.54 13475 1.00
beta[19,2] 0.46 1.49 16734 1.00
beta[19,3] 0.48 1.55 16627 1.00
beta[19,4] 0.14 0.60 13288 1.00
beta[19,5] 0.07 0.39 11929 1.00
beta[19,6] 0.08 0.39 10890 1.00
beta[19,7] 0.10 0.40 13238 1.00
beta[19,8] 0.11 0.45 12875 1.00
beta[19,9] 0.18 0.70 13359 1.00
beta[19,10] 0.12 0.47 11265 1.00
beta[19,11] 0.14 0.53 13153 1.00
beta[19,12] 0.15 0.64 12440 1.00
beta[20,1] 0.12 0.55 13867 1.00
beta[20,2] 0.44 1.42 17023 1.00
beta[20,3] 0.49 1.54 19682 1.00
beta[20,4] 0.14 0.60 16576 1.00
beta[20,5] 0.07 0.40 9556 1.00
beta[20,6] 0.08 0.41 11590 1.00
beta[20,7] 0.10 0.42 12064 1.00
beta[20,8] 0.11 0.45 12371 1.00
beta[20,9] 0.19 0.72 14508 1.00
beta[20,10] 0.12 0.48 12739 1.00
beta[20,11] 0.15 0.56 13268 1.00
beta[20,12] 0.15 0.65 14082 1.00
beta[21,1] 0.13 0.54 14273 1.00
beta[21,2] 0.44 1.42 15392 1.00
beta[21,3] 0.47 1.51 17060 1.00
beta[21,4] 0.14 0.60 14768 1.00
beta[21,5] 0.07 0.38 10462 1.00
beta[21,6] 0.08 0.39 11525 1.00
beta[21,7] 0.10 0.42 12263 1.00
beta[21,8] 0.12 0.44 11992 1.00
beta[21,9] 0.18 0.71 13795 1.00
beta[21,10] 0.12 0.47 12407 1.00
beta[21,11] 0.14 0.54 13536 1.00
beta[21,12] 0.14 0.63 12557 1.00
beta[22,1] 0.13 0.55 14439 1.00
beta[22,2] 0.44 1.41 17042 1.00
beta[22,3] 0.47 1.49 17914 1.00
beta[22,4] 0.14 0.58 16496 1.00
beta[22,5] 0.07 0.39 12167 1.00
beta[22,6] 0.08 0.38 11677 1.00
beta[22,7] 0.10 0.41 12261 1.00
beta[22,8] 0.12 0.43 13715 1.00
beta[22,9] 0.17 0.69 15213 1.00
beta[22,10] 0.12 0.47 13583 1.00
beta[22,11] 0.14 0.56 13609 1.00
beta[22,12] 0.14 0.67 13718 1.00
mu_prior[1] 0.03 0.10 9883 1.00
mu_prior[2] 0.03 0.10 9881 1.00
mu_prior[3] 0.03 0.10 9723 1.00
mu_prior[4] 0.03 0.10 9840 1.00
mu_prior[5] 0.03 0.10 9844 1.00
mu_prior[6] 0.03 0.10 9343 1.00
mu_prior[7] 0.03 0.10 10006 1.00
mu_prior[8] 0.03 0.10 9959 1.00
mu_prior[9] 0.03 0.10 9657 1.00
mu_prior[10] 0.03 0.10 10005 1.00
mu_prior[11] 0.03 0.10 9935 1.00
mu_prior[12] 0.03 0.10 10114 1.00
sigma_prior[1] 0.25 0.44 10107 1.00
sigma_prior[2] 0.26 0.44 9707 1.00
sigma_prior[3] 0.26 0.44 9900 1.00
sigma_prior[4] 0.26 0.43 9702 1.00
sigma_prior[5] 0.25 0.43 10074 1.00
sigma_prior[6] 0.26 0.44 9912 1.00
sigma_prior[7] 0.25 0.43 9921 1.00
sigma_prior[8] 0.25 0.43 9686 1.00
sigma_prior[9] 0.26 0.44 9328 1.00
sigma_prior[10] 0.25 0.44 10026 1.00
sigma_prior[11] 0.25 0.43 9925 1.00
sigma_prior[12] 0.25 0.44 10120 1.00
p_prior[1] 0.99 1.00 10023 1.00
p_prior[2] 0.99 1.00 10031 1.00
p_prior[3] 0.99 1.00 10038 1.00
p_prior[4] 0.99 1.00 10054 1.00
p_prior[5] 0.99 1.00 10056 1.00
p_prior[6] 0.99 1.00 10058 1.00
p_prior[7] 0.99 1.00 10073 1.00
p_prior[8] 0.99 1.00 10079 1.00
p_prior[9] 0.99 1.00 9805 1.00
p_prior[10] 0.99 1.00 9805 1.00
p_prior[11] 0.98 1.00 9694 1.00
p_prior[12] 0.98 1.00 9713 1.00
p_prior[13] 0.98 1.00 9698 1.00
p_prior[14] 0.98 1.00 9697 1.00
p_prior[15] 0.98 1.00 9704 1.00
p_prior[16] 0.98 1.00 9709 1.00
p_prior[17] 0.98 1.00 9705 1.00
p_prior[18] 0.98 1.00 9704 1.00
p_prior[19] 0.98 1.00 9704 1.00
p_prior[20] 0.98 1.00 9703 1.00
p_prior[21] 0.99 1.00 10053 1.00
p_prior[22] 0.99 1.00 10085 1.00
p_prior[23] 0.99 1.00 9732 1.00
p_prior[24] 0.99 1.00 9729 1.00
p_prior[25] 0.99 1.00 9726 1.00
p_prior[26] 0.99 1.00 9772 1.00
p_prior[27] 0.99 1.00 9771 1.00
p_prior[28] 0.99 1.00 9770 1.00
p_prior[29] 0.99 1.00 9770 1.00
p_prior[30] 0.99 1.00 9773 1.00
p_prior[31] 0.99 1.00 9773 1.00
p_prior[32] 0.99 1.00 9772 1.00
p_prior[33] 0.99 1.00 9772 1.00
p_prior[34] 0.99 1.00 9771 1.00
p_prior[35] 0.99 1.00 9771 1.00
p_prior[36] 0.99 1.00 9770 1.00
p_prior[37] 0.99 1.00 9770 1.00
p_prior[38] 0.99 1.00 9775 1.00
p_prior[39] 0.99 1.00 9775 1.00
p_prior[40] 0.99 1.00 9795 1.00
p_prior[41] 0.99 1.00 9795 1.00
p_prior[42] 0.99 1.00 9795 1.00
p_prior[43] 0.99 1.00 9795 1.00
p_prior[44] 0.99 1.00 9795 1.00
p_prior[45] 0.99 1.00 9795 1.00
p_prior[46] 0.99 1.00 9795 1.00
p_prior[47] 0.99 1.00 9795 1.00
p_prior[48] 0.99 1.00 9796 1.00
p_prior[49] 0.99 1.00 9796 1.00
p_prior[50] 0.99 1.00 9829 1.00
p_prior[51] 0.99 1.00 9820 1.00
p_prior[52] 0.99 1.00 9818 1.00
p_prior[53] 0.98 1.00 9814 1.00
p_prior[54] 0.99 1.00 9796 1.00
p_prior[55] 0.98 1.00 9786 1.00
p_prior[56] 0.98 1.00 9783 1.00
p_prior[57] 0.99 1.00 9754 1.00
p_prior[58] 0.99 1.00 9842 1.00
p_prior[59] 0.99 1.00 9842 1.00
p_prior[60] 0.99 1.00 9842 1.00
p_prior[61] 0.99 1.00 9834 1.00
p_prior[62] 0.99 1.00 9826 1.00
p_prior[63] 0.99 1.00 9822 1.00
p_prior[64] 0.99 1.00 9818 1.00
p_prior[65] 0.99 1.00 9827 1.00
p_prior[66] 0.99 1.00 9802 1.00
p_prior[67] 0.99 1.00 9789 1.00
p_prior[68] 0.99 1.00 9783 1.00
p_prior[69] 0.99 1.00 9731 1.00
p_prior[70] 0.99 1.00 9746 1.00
p_prior[71] 0.98 1.00 9757 1.00
p_prior[72] 0.98 1.00 9757 1.00
p_prior[73] 0.98 1.00 9758 1.00
p_prior[74] 0.98 1.00 9758 1.00
p_prior[75] 0.98 1.00 9765 1.00
p_prior[76] 0.99 1.00 9859 1.00
p_prior[77] 0.99 1.00 9861 1.00
p_prior[78] 0.99 1.00 9861 1.00
p_prior[79] 0.99 1.00 9859 1.00
p_prior[80] 0.99 1.00 9861 1.00
p_prior[81] 0.99 1.00 9861 1.00
p_prior[82] 0.99 1.00 9859 1.00
p_prior[83] 0.99 1.00 9861 1.00
p_prior[84] 0.99 1.00 9861 1.00
p_prior[85] 0.99 1.00 9769 1.00
p_prior[86] 0.99 1.00 9775 1.00
p_prior[87] 0.99 1.00 9810 1.00
p_prior[88] 0.99 1.00 9788 1.00
p_prior[89] 0.99 1.00 9790 1.00
p_prior[90] 0.99 1.00 10135 1.00
p_prior[91] 0.99 1.00 10139 1.00
p_prior[92] 0.99 1.00 10144 1.00
p_prior[93] 0.99 1.00 10123 1.00
p_prior[94] 0.99 1.00 9853 1.00
p_prior[95] 0.99 1.00 9856 1.00
p_prior[96] 0.99 1.00 9862 1.00
p_prior[97] 0.99 1.00 9839 1.00
p_prior[98] 0.99 1.00 9844 1.00
p_prior[99] 0.99 1.00 9779 1.00
p_prior[100] 0.99 1.00 9779 1.00
p_prior[101] 0.99 1.00 9748 1.00
p_prior[102] 0.99 1.00 9741 1.00
p_prior[103] 0.99 1.00 9777 1.00
p_prior[104] 0.99 1.00 9949 1.00
p_prior[105] 0.99 1.00 9956 1.00
p_prior[106] 0.99 1.00 9956 1.00
p_prior[107] 0.99 1.00 9953 1.00
p_prior[108] 0.99 1.00 9953 1.00
p_prior[109] 0.99 1.00 9946 1.00
p_prior[110] 0.99 1.00 9945 1.00
p_prior[111] 0.99 1.00 9940 1.00
p_prior[112] 0.99 1.00 9941 1.00
p_prior[113] 0.99 1.00 9940 1.00
p_prior[114] 0.99 1.00 9937 1.00
p_prior[115] 0.99 1.00 9937 1.00
p_prior[116] 0.99 1.00 9935 1.00
p_prior[117] 0.98 1.00 9653 1.00
p_prior[118] 0.98 1.00 9649 1.00
p_prior[119] 0.98 1.00 9645 1.00
p_prior[120] 0.98 1.00 9668 1.00
p_prior[121] 0.98 1.00 9665 1.00
p_prior[122] 0.98 1.00 9688 1.00
p_prior[123] 0.98 1.00 9688 1.00
p_prior[124] 0.98 1.00 9684 1.00
p_prior[125] 0.98 1.00 9681 1.00
p_prior[126] 0.98 1.00 9680 1.00
p_prior[127] 0.98 1.00 9680 1.00
p_prior[128] 0.99 1.00 10136 1.00
p_prior[129] 0.99 1.00 10130 1.00
p_prior[130] 0.99 1.00 10136 1.00
p_prior[131] 0.99 1.00 10018 1.00
p_prior[132] 0.99 1.00 10017 1.00
p_prior[133] 0.99 1.00 10013 1.00
p_prior[134] 0.99 1.00 9972 1.00
p_prior[135] 0.99 1.00 9972 1.00
p_prior[136] 0.99 1.00 9792 1.00
p_prior[137] 0.99 1.00 9733 1.00
p_prior[138] 0.99 1.00 9733 1.00
p_prior[139] 0.99 1.00 9734 1.00
p_prior[140] 0.99 1.00 9728 1.00
p_prior[141] 0.99 1.00 9725 1.00
p_prior[142] 0.99 1.00 9772 1.00
p_prior[143] 0.99 1.00 9771 1.00
p_prior[144] 0.99 1.00 9771 1.00
p_prior[145] 0.99 1.00 9771 1.00
p_prior[146] 0.99 1.00 9771 1.00
p_prior[147] 0.99 1.00 9770 1.00
p_prior[148] 0.99 1.00 9769 1.00
p_prior[149] 0.99 1.00 9769 1.00
p_prior[150] 0.99 1.00 9814 1.00
p_prior[151] 0.99 1.00 9783 1.00
p_prior[152] 0.99 1.00 9783 1.00
p_prior[153] 0.99 1.00 9737 1.00
p_prior[154] 0.99 1.00 9738 1.00
p_prior[155] 0.99 1.00 9776 1.00
p_prior[156] 0.99 1.00 9774 1.00
p_prior[157] 0.99 1.00 9736 1.00
p_prior[158] 0.99 1.00 9772 1.00
p_prior[159] 0.99 1.00 9730 1.00
p_prior[160] 0.99 1.00 9717 1.00
p_prior[161] 0.99 1.00 9717 1.00
p_prior[162] 0.99 1.00 9716 1.00
p_prior[163] 0.61 0.83 9585 1.00
p_prior[164] 0.61 0.83 9585 1.00
p_prior[165] 0.62 0.84 9862 1.00
p_prior[166] 0.62 0.84 9862 1.00
p_prior[167] 0.62 0.84 9873 1.00
p_prior[168] 0.62 0.84 9873 1.00
p_prior[169] 0.63 0.85 9855 1.00
p_prior[170] 0.63 0.85 9855 1.00
p_prior[171] 0.99 1.00 9745 1.00
p_prior[172] 0.99 1.00 9742 1.00
p_prior[173] 0.99 1.00 9785 1.00
p_prior[174] 0.98 1.00 9775 1.00
p_prior[175] 0.99 1.00 9843 1.00
p_prior[176] 0.99 1.00 9842 1.00
p_prior[177] 0.99 1.00 9835 1.00
p_prior[178] 0.99 1.00 9817 1.00
p_prior[179] 0.99 1.00 9833 1.00
p_prior[180] 0.99 1.00 9825 1.00
p_prior[181] 0.99 1.00 9821 1.00
p_prior[182] 0.99 1.00 9810 1.00
p_prior[183] 0.99 1.00 9807 1.00
p_prior[184] 0.99 1.00 9794 1.00
p_prior[185] 0.99 1.00 9780 1.00
p_prior[186] 0.99 1.00 9778 1.00
p_prior[187] 0.99 1.00 9837 1.00
p_prior[188] 0.99 1.00 9837 1.00
p_prior[189] 0.99 1.00 9827 1.00
p_prior[190] 0.98 1.00 9819 1.00
p_prior[191] 0.98 1.00 9815 1.00
p_prior[192] 0.98 1.00 9820 1.00
p_prior[193] 0.99 1.00 9815 1.00
p_prior[194] 0.98 1.00 9805 1.00
p_prior[195] 0.98 1.00 9804 1.00
p_prior[196] 0.98 1.00 9802 1.00
p_prior[197] 0.99 1.00 9837 1.00
p_prior[198] 0.99 1.00 9837 1.00
p_prior[199] 0.99 1.00 9836 1.00
p_prior[200] 0.99 1.00 9827 1.00
p_prior[201] 0.98 1.00 9815 1.00
p_prior[202] 0.98 1.00 9801 1.00
p_prior[203] 0.98 1.00 9793 1.00
p_prior[204] 0.98 1.00 9805 1.00
p_prior[205] 0.99 1.00 9899 1.00
p_prior[206] 0.99 1.00 9879 1.00
p_prior[207] 0.99 1.00 9879 1.00
p_prior[208] 0.99 1.00 9774 1.00
p_prior[209] 0.99 1.00 9763 1.00
p_prior[210] 0.99 1.00 9770 1.00
p_prior[211] 0.99 1.00 9770 1.00
p_prior[212] 0.99 1.00 9772 1.00
p_prior[213] 0.99 1.00 9752 1.00
p_prior[214] 0.99 1.00 9752 1.00
p_prior[215] 0.99 1.00 9752 1.00
p_prior[216] 0.99 1.00 9861 1.00
p_prior[217] 0.99 1.00 9863 1.00
p_prior[218] 0.99 1.00 9865 1.00
p_prior[219] 0.99 1.00 9842 1.00
p_prior[220] 0.99 1.00 9846 1.00
p_prior[221] 0.99 1.00 9863 1.00
p_prior[222] 0.99 1.00 9866 1.00
p_prior[223] 0.99 1.00 9843 1.00
p_prior[224] 0.99 1.00 9847 1.00
p_prior[225] 0.99 1.00 9847 1.00
p_prior[226] 0.99 1.00 10143 1.00
p_prior[227] 0.99 1.00 10168 1.00
p_prior[228] 0.99 1.00 10167 1.00
p_prior[229] 0.99 1.00 10173 1.00
p_prior[230] 0.99 1.00 10170 1.00
p_prior[231] 0.99 1.00 9847 1.00
p_prior[232] 0.99 1.00 9838 1.00
p_prior[233] 0.99 1.00 9826 1.00
p_prior[234] 0.99 1.00 9794 1.00
p_prior[235] 0.99 1.00 9792 1.00
p_prior[236] 0.99 1.00 9876 1.00
p_prior[237] 0.99 1.00 9876 1.00
p_prior[238] 0.99 1.00 9903 1.00
p_prior[239] 0.99 1.00 9903 1.00
p_prior[240] 0.99 1.00 9817 1.00
p_prior[241] 0.99 1.00 9817 1.00
p_prior[242] 0.99 1.00 9817 1.00
p_prior[243] 0.99 1.00 9817 1.00
p_prior[244] 0.99 1.00 9817 1.00
p_prior[245] 0.99 1.00 9817 1.00
p_prior[246] 0.99 1.00 9765 1.00
p_prior[247] 0.99 1.00 9765 1.00
p_prior[248] 0.99 1.00 9765 1.00
p_prior[249] 0.99 1.00 9764 1.00
p_prior[250] 0.99 1.00 9764 1.00
p_prior[251] 0.99 1.00 9764 1.00
p_prior[252] 0.99 1.00 9763 1.00
p_prior[253] 0.99 1.00 9763 1.00
p_prior[254] 0.99 1.00 9763 1.00
p_prior[255] 0.99 1.00 9763 1.00
p_prior[256] 0.99 1.00 9763 1.00
p_prior[257] 0.99 1.00 9763 1.00
p_prior[258] 0.99 1.00 9804 1.00
p_prior[259] 0.99 1.00 9804 1.00
p_prior[260] 0.99 1.00 9804 1.00
p_prior[261] 0.99 1.00 9803 1.00
p_prior[262] 0.99 1.00 9803 1.00
p_prior[263] 0.99 1.00 9803 1.00
p_prior[264] 0.99 1.00 9803 1.00
p_prior[265] 0.99 1.00 9803 1.00
p_prior[266] 0.99 1.00 9803 1.00
p_prior[267] 0.99 1.00 9806 1.00
p_prior[268] 0.99 1.00 9806 1.00
p_prior[269] 0.99 1.00 9806 1.00
p_prior[270] 0.99 1.00 9806 1.00
p_prior[271] 0.99 1.00 9806 1.00
p_prior[272] 0.99 1.00 9806 1.00
p_prior[273] 0.99 1.00 9603 1.00
p_prior[274] 0.99 1.00 9603 1.00
p_prior[275] 0.99 1.00 9602 1.00
p_prior[276] 0.99 1.00 9602 1.00
p_prior[277] 0.99 1.00 9602 1.00
p_prior[278] 0.99 1.00 9602 1.00
p_prior[279] 0.99 1.00 9602 1.00
p_prior[280] 0.99 1.00 9602 1.00
p_prior[281] 0.99 1.00 9595 1.00
p_prior[282] 0.99 1.00 9595 1.00
p_prior[283] 0.99 1.00 9617 1.00
p_prior[284] 0.99 1.00 9617 1.00
p_prior[285] 0.99 1.00 9588 1.00
p_prior[286] 0.99 1.00 9588 1.00
p_prior[287] 0.99 1.00 9586 1.00
p_prior[288] 0.99 1.00 9586 1.00
p_prior[289] 0.99 1.00 9586 1.00
p_prior[290] 0.99 1.00 9586 1.00
p_prior[291] 0.99 1.00 9585 1.00
p_prior[292] 0.99 1.00 9585 1.00
p_prior[293] 0.99 1.00 9585 1.00
p_prior[294] 0.99 1.00 9585 1.00
p_prior[295] 0.99 1.00 9584 1.00
p_prior[296] 0.99 1.00 9584 1.00
p_prior[297] 0.99 1.00 9584 1.00
p_prior[298] 0.99 1.00 9584 1.00
p_prior[299] 0.99 1.00 9585 1.00
p_prior[300] 0.99 1.00 9585 1.00
p_prior[301] 0.99 1.00 9584 1.00
p_prior[302] 0.99 1.00 9584 1.00
p_prior[303] 0.99 1.00 9584 1.00
p_prior[304] 0.99 1.00 9584 1.00
p_prior[305] 0.99 1.00 9583 1.00
p_prior[306] 0.99 1.00 9583 1.00
p_prior[307] 0.99 1.00 9583 1.00
p_prior[308] 0.99 1.00 9583 1.00
p_prior[309] 0.99 1.00 9583 1.00
p_prior[310] 0.99 1.00 9583 1.00
p_prior[311] 0.99 1.00 9582 1.00
p_prior[312] 0.99 1.00 9582 1.00
p_prior[313] 0.99 1.00 9581 1.00
p_prior[314] 0.99 1.00 9581 1.00
p_prior[315] 0.99 1.00 9581 1.00
p_prior[316] 0.99 1.00 9581 1.00
p_prior[317] 0.99 1.00 9610 1.00
p_prior[318] 0.99 1.00 9610 1.00
p_prior[319] 0.99 1.00 9610 1.00
p_prior[320] 0.99 1.00 9610 1.00
p_prior[321] 0.99 1.00 9776 1.00
p_prior[322] 0.99 1.00 9799 1.00
p_prior[323] 0.98 1.00 9793 1.00
p_prior[324] 0.99 1.00 9788 1.00
p_prior[325] 0.98 1.00 9778 1.00
p_prior[326] 0.99 1.00 9779 1.00
p_prior[327] 0.98 1.00 9768 1.00
p_prior[328] 0.99 1.00 9720 1.00
p_prior[329] 0.98 1.00 9745 1.00
p_prior[330] 0.99 1.00 9720 1.00
p_prior[331] 0.98 1.00 9740 1.00
p_prior[332] 0.99 1.00 9716 1.00
p_prior[333] 0.98 1.00 9739 1.00
p_prior[334] 0.98 1.00 9690 1.00
p_prior[335] 0.98 1.00 9685 1.00
p_prior[336] 0.98 1.00 9672 1.00
p_prior[337] 0.98 1.00 9674 1.00
p_prior[338] 0.98 1.00 9671 1.00
p_prior[339] 0.98 1.00 9670 1.00
p_prior[340] 0.98 1.00 9665 1.00
p_prior[341] 0.98 1.00 9700 1.00
p_prior[342] 0.99 1.00 9806 1.00
p_prior[343] 0.99 1.00 9806 1.00
p_prior[344] 0.99 1.00 9765 1.00
p_prior[345] 0.99 1.00 9765 1.00
p_prior[346] 0.57 0.71 10018 1.00
p_prior[347] 0.57 0.71 10018 1.00
p_prior[348] 0.57 0.72 9964 1.00
p_prior[349] 0.57 0.72 9964 1.00
p_prior[350] 0.99 1.00 10002 1.00
p_prior[351] 1.00 1.00 10014 1.00
p_prior[352] 0.99 1.00 9999 1.00
p_prior[353] 1.00 1.00 10006 1.00
p_prior[354] 0.99 1.00 10039 1.00
p_prior[355] 1.00 1.00 10039 1.00
p_prior[356] 0.99 1.00 10068 1.00
p_prior[357] 1.00 1.00 10062 1.00
p_prior[358] 0.99 1.00 10108 1.00
p_prior[359] 1.00 1.00 10106 1.00
p_prior[360] 0.99 1.00 10110 1.00
p_prior[361] 1.00 1.00 10108 1.00
p_prior[362] 0.99 1.00 10119 1.00
p_prior[363] 1.00 1.00 10116 1.00
p_prior[364] 0.99 1.00 9751 1.00
p_prior[365] 0.99 1.00 9751 1.00
p_prior[366] 0.99 1.00 9751 1.00
p_prior[367] 0.99 1.00 9753 1.00
p_prior[368] 0.99 1.00 9753 1.00
p_prior[369] 0.99 1.00 9753 1.00
p_prior[370] 0.99 1.00 9752 1.00
p_prior[371] 0.99 1.00 9752 1.00
p_prior[372] 0.99 1.00 9752 1.00
p_prior[373] 0.99 1.00 9788 1.00
p_prior[374] 0.99 1.00 9788 1.00
p_prior[375] 0.99 1.00 9788 1.00
p_prior[376] 0.99 1.00 9788 1.00
p_prior[377] 0.99 1.00 9788 1.00
p_prior[378] 0.99 1.00 9788 1.00
p_prior[379] 0.99 1.00 9788 1.00
p_prior[380] 0.99 1.00 9788 1.00
p_prior[381] 0.99 1.00 9788 1.00
p_prior[382] 0.99 1.00 9788 1.00
p_prior[383] 0.99 1.00 9788 1.00
p_prior[384] 0.99 1.00 9788 1.00
p_prior[385] 0.99 1.00 9788 1.00
p_prior[386] 0.99 1.00 9788 1.00
p_prior[387] 0.99 1.00 9788 1.00
p_prior[388] 0.99 1.00 9788 1.00
p_prior[389] 0.99 1.00 9788 1.00
p_prior[390] 0.99 1.00 9788 1.00
p_prior[391] 0.99 1.00 9788 1.00
p_prior[392] 0.99 1.00 9788 1.00
p_prior[393] 0.99 1.00 9788 1.00
p_prior[394] 0.99 1.00 9788 1.00
p_prior[395] 0.99 1.00 9788 1.00
p_prior[396] 0.99 1.00 9788 1.00
p_prior[397] 0.99 1.00 9788 1.00
p_prior[398] 0.99 1.00 9788 1.00
p_prior[399] 0.99 1.00 9788 1.00
p_prior[400] 0.99 1.00 9788 1.00
p_prior[401] 0.99 1.00 9788 1.00
p_prior[402] 0.99 1.00 9788 1.00
p_prior[403] 0.99 1.00 9788 1.00
p_prior[404] 0.99 1.00 9788 1.00
p_prior[405] 0.99 1.00 9788 1.00
p_prior[406] 1.00 1.00 10128 1.00
p_prior[407] 1.00 1.00 10128 1.00
p_prior[408] 1.00 1.00 10128 1.00
p_prior[409] 1.00 1.00 10132 1.00
p_prior[410] 0.99 1.00 9799 1.00
p_prior[411] 0.99 1.00 9779 1.00
p_prior[412] 0.99 1.00 9779 1.00
p_prior[413] 0.99 1.00 9779 1.00
p_prior[414] 0.99 1.00 9779 1.00
p_prior[415] 0.99 1.00 9780 1.00
p_prior[416] 0.99 1.00 9798 1.00
p_prior[417] 0.99 1.00 9779 1.00
p_prior[418] 0.99 1.00 9804 1.00
p_prior[419] 0.99 1.00 9779 1.00
p_prior[420] 0.99 1.00 9805 1.00
p_prior[421] 0.99 1.00 9805 1.00
p_prior[422] 0.99 1.00 9791 1.00
p_prior[423] 0.99 1.00 9774 1.00
p_prior[424] 0.99 1.00 9773 1.00
p_prior[425] 0.99 1.00 9772 1.00
p_prior[426] 0.99 1.00 9760 1.00
p_prior[427] 0.99 1.00 9762 1.00
p_prior[428] 0.99 1.00 9752 1.00
p_prior[429] 0.98 1.00 9730 1.00
p_prior[430] 0.98 1.00 9724 1.00
p_prior[431] 0.98 1.00 9770 1.00
p_prior[432] 0.98 1.00 9770 1.00
p_prior[433] 0.98 1.00 9773 1.00
p_prior[434] 0.98 1.00 9767 1.00
p_prior[435] 0.98 1.00 9767 1.00
p_prior[436] 0.98 1.00 9767 1.00
p_prior[437] 0.98 1.00 9767 1.00
p_prior[438] 0.99 1.00 9792 1.00
p_prior[439] 0.99 1.00 9733 1.00
p_prior[440] 0.99 1.00 9731 1.00
p_prior[441] 0.99 1.00 9731 1.00
p_prior[442] 0.99 1.00 9730 1.00
p_prior[443] 0.99 1.00 9727 1.00
p_prior[444] 0.99 1.00 9729 1.00
p_prior[445] 0.99 1.00 9774 1.00
p_prior[446] 0.99 1.00 9773 1.00
p_prior[447] 0.99 1.00 9726 1.00
p_prior[448] 0.99 1.00 9772 1.00
p_prior[449] 0.99 1.00 9772 1.00
p_prior[450] 0.99 1.00 9771 1.00
p_prior[451] 0.99 1.00 9770 1.00
p_prior[452] 0.99 1.00 9720 1.00
p_prior[453] 0.99 1.00 9770 1.00
p_prior[454] 0.99 1.00 9770 1.00
p_prior[455] 0.99 1.00 9770 1.00
p_prior[456] 0.99 1.00 9791 1.00
p_prior[457] 0.99 1.00 9734 1.00
p_prior[458] 0.99 1.00 9729 1.00
p_prior[459] 0.99 1.00 9726 1.00
p_prior[460] 0.99 1.00 9725 1.00
p_prior[461] 0.99 1.00 9725 1.00
p_prior[462] 0.99 1.00 9771 1.00
p_prior[463] 0.99 1.00 9771 1.00
p_prior[464] 0.99 1.00 9770 1.00
p_prior[465] 0.58 0.74 9811 1.00
p_prior[466] 0.58 0.74 10046 1.00
p_prior[467] 0.58 0.75 10015 1.00
p_prior[468] 0.58 0.74 10029 1.00
p_prior[469] 0.58 0.75 9856 1.00
p_prior[470] 0.58 0.74 9843 1.00
p_prior[471] 0.58 0.75 9850 1.00
p_prior[472] 0.58 0.75 9858 1.00
p_prior[473] 0.60 0.79 9846 1.00
p_prior[474] 0.61 0.80 9853 1.00
p_prior[475] 0.62 0.83 9855 1.00
p_prior[476] 0.62 0.84 9849 1.00
p_prior[477] 0.99 1.00 9790 1.00
p_prior[478] 0.99 1.00 9761 1.00
p_prior[479] 0.99 1.00 9761 1.00
p_prior[480] 0.99 1.00 9798 1.00
p_prior[481] 0.99 1.00 9798 1.00
p_prior[482] 0.99 1.00 9798 1.00
p_prior[483] 0.99 1.00 9798 1.00
p_prior[484] 0.99 1.00 9798 1.00
p_prior[485] 0.99 1.00 9798 1.00
p_prior[486] 0.99 1.00 9798 1.00
p_prior[487] 0.99 1.00 10133 1.00
p_prior[488] 0.99 1.00 10133 1.00
p_prior[489] 0.99 1.00 10133 1.00
p_prior[490] 0.99 1.00 10133 1.00
p_prior[491] 0.99 1.00 10142 1.00
p_prior[492] 0.99 1.00 10142 1.00
p_prior[493] 0.99 1.00 10135 1.00
p_prior[494] 0.99 1.00 10135 1.00
p_prior[495] 0.99 1.00 10137 1.00
p_prior[496] 0.99 1.00 10137 1.00
p_prior[497] 0.58 0.74 9813 1.00
p_prior[498] 0.58 0.74 10048 1.00
p_prior[499] 0.58 0.75 9847 1.00
p_prior[500] 0.58 0.75 9854 1.00
p_prior[501] 0.58 0.75 9859 1.00
p_prior[502] 0.58 0.75 9861 1.00
p_prior[503] 0.58 0.75 9865 1.00
p_prior[504] 0.60 0.78 9841 1.00
p_prior[505] 0.60 0.79 9849 1.00
p_prior[506] 0.61 0.82 9856 1.00
p_prior[507] 0.62 0.83 9853 1.00
p_prior[508] 0.99 1.00 10010 1.00
p_prior[509] 0.99 1.00 10014 1.00
p_prior[510] 0.99 1.00 10011 1.00
p_prior[511] 0.99 1.00 10010 1.00
p_prior[512] 0.99 1.00 10006 1.00
p_prior[513] 0.99 1.00 9974 1.00
p_prior[514] 0.99 1.00 9971 1.00
p_prior[515] 0.99 1.00 9972 1.00
p_prior[516] 0.99 1.00 9972 1.00
p_prior[517] 0.99 1.00 9971 1.00
p_prior[518] 0.99 1.00 9999 1.00
p_prior[519] 0.98 1.00 9767 1.00
p_prior[520] 0.98 1.00 9767 1.00
p_prior[521] 0.99 1.00 9767 1.00
p_prior[522] 0.99 1.00 9767 1.00
p_prior[523] 0.99 1.00 9767 1.00
p_prior[524] 0.99 1.00 9765 1.00
p_prior[525] 0.99 1.00 9765 1.00
p_prior[526] 0.99 1.00 9765 1.00
p_prior[527] 0.99 1.00 9772 1.00
p_prior[528] 0.99 1.00 9772 1.00
p_prior[529] 0.99 1.00 9772 1.00
p_prior[530] 0.99 1.00 9771 1.00
p_prior[531] 0.99 1.00 9771 1.00
p_prior[532] 0.99 1.00 9771 1.00
p_prior[533] 0.99 1.00 9808 1.00
p_prior[534] 0.99 1.00 9808 1.00
p_prior[535] 0.99 1.00 9808 1.00
p_prior[536] 0.99 1.00 9780 1.00
p_prior[537] 0.99 1.00 9780 1.00
p_prior[538] 0.99 1.00 9780 1.00
p_prior[539] 0.99 1.00 9779 1.00
p_prior[540] 0.99 1.00 9779 1.00
p_prior[541] 0.99 1.00 9779 1.00
p_prior[542] 0.99 1.00 9796 1.00
p_prior[543] 0.99 1.00 9796 1.00
p_prior[544] 0.99 1.00 9796 1.00
p_prior[545] 0.99 1.00 9795 1.00
p_prior[546] 0.99 1.00 9795 1.00
p_prior[547] 0.99 1.00 9795 1.00
p_prior[548] 0.99 1.00 9602 1.00
p_prior[549] 0.99 1.00 9602 1.00
p_prior[550] 0.99 1.00 9602 1.00
p_prior[551] 0.99 1.00 9595 1.00
p_prior[552] 0.99 1.00 9588 1.00
p_prior[553] 0.99 1.00 9586 1.00
p_prior[554] 0.99 1.00 9586 1.00
p_prior[555] 0.99 1.00 9584 1.00
p_prior[556] 0.99 1.00 9585 1.00
p_prior[557] 0.99 1.00 9584 1.00
p_prior[558] 0.99 1.00 9584 1.00
p_prior[559] 0.99 1.00 9583 1.00
p_prior[560] 0.99 1.00 9582 1.00
p_prior[561] 0.99 1.00 9584 1.00
p_prior[562] 0.99 1.00 9584 1.00
p_prior[563] 0.99 1.00 9582 1.00
p_prior[564] 0.99 1.00 9580 1.00
p_prior[565] 0.99 1.00 9580 1.00
p_prior[566] 0.99 1.00 9579 1.00
p_prior[567] 0.99 1.00 9799 1.00
p_prior[568] 0.99 1.00 9812 1.00
p_prior[569] 0.99 1.00 9817 1.00
p_prior[570] 0.99 1.00 9819 1.00
p_prior[571] 0.99 1.00 9843 1.00
p_prior[572] 1.00 1.00 10019 1.00
p_prior[573] 1.00 1.00 10107 1.00
p_prior[574] 1.00 1.00 10126 1.00
p_prior[575] 0.99 1.00 10128 1.00
p_prior[576] 0.99 1.00 10130 1.00
p_prior[577] 0.99 1.00 10126 1.00
p_prior[578] 0.99 1.00 9791 1.00
p_prior[579] 0.99 1.00 9733 1.00
p_prior[580] 0.99 1.00 9725 1.00
p_prior[581] 0.99 1.00 9771 1.00
p_prior[582] 0.99 1.00 9769 1.00
p_prior[583] 0.99 1.00 10032 1.00
p_prior[584] 1.00 1.00 10040 1.00
p_prior[585] 0.99 1.00 10035 1.00
p_prior[586] 1.00 1.00 10034 1.00
p_prior[587] 0.99 1.00 10022 1.00
p_prior[588] 1.00 1.00 10023 1.00
p_prior[589] 0.99 1.00 10029 1.00
p_prior[590] 1.00 1.00 10028 1.00
p_prior[591] 0.99 1.00 10070 1.00
p_prior[592] 1.00 1.00 10073 1.00
p_prior[593] 0.57 0.71 10096 1.00
p_prior[594] 0.57 0.71 10015 1.00
p_prior[595] 0.57 0.71 10008 1.00
p_prior[596] 0.57 0.71 10000 1.00
p_prior[597] 0.57 0.72 9971 1.00
p_prior[598] 0.57 0.72 9949 1.00
p_prior[599] 0.59 0.76 9884 1.00
p_prior[600] 0.59 0.76 9889 1.00
p_prior[601] 0.60 0.78 9900 1.00
p_prior[602] 0.61 0.82 9891 1.00
p_prior[603] 0.99 1.00 9670 1.00
p_prior[604] 0.99 1.00 9670 1.00
p_prior[605] 0.99 1.00 9653 1.00
p_prior[606] 0.99 1.00 9653 1.00
p_prior[607] 0.99 1.00 9650 1.00
p_prior[608] 0.99 1.00 9650 1.00
p_prior[609] 0.99 1.00 9681 1.00
p_prior[610] 0.99 1.00 9681 1.00
p_prior[611] 0.99 1.00 9686 1.00
p_prior[612] 0.99 1.00 9686 1.00
p_prior[613] 0.56 0.70 9909 1.00
p_prior[614] 0.56 0.70 9909 1.00
p_prior[615] 0.57 0.71 9918 1.00
p_prior[616] 0.99 1.00 10035 1.00
p_prior[617] 0.99 1.00 10037 1.00
p_prior[618] 0.99 1.00 9739 1.00
p_prior[619] 0.99 1.00 9712 1.00
p_prior[620] 0.99 1.00 9707 1.00
p_prior[621] 0.99 1.00 9669 1.00
p_prior[622] 0.99 1.00 10134 1.00
p_prior[623] 0.99 1.00 10153 1.00
p_prior[624] 0.99 1.00 10154 1.00
p_prior[625] 0.99 1.00 10135 1.00
p_prior[626] 0.99 1.00 9800 1.00
p_prior[627] 0.99 1.00 9775 1.00
p_prior[628] 0.99 1.00 9810 1.00
p_prior[629] 0.99 1.00 9810 1.00
p_prior[630] 0.99 1.00 9810 1.00
p_prior[631] 0.99 1.00 9807 1.00
p_prior[632] 0.99 1.00 10029 1.00
p_prior[633] 1.00 1.00 10037 1.00
p_prior[634] 0.99 1.00 10025 1.00
p_prior[635] 1.00 1.00 10028 1.00
p_prior[636] 0.99 1.00 10039 1.00
p_prior[637] 1.00 1.00 10039 1.00
p_prior[638] 0.99 1.00 10037 1.00
p_prior[639] 1.00 1.00 10037 1.00
p_prior[640] 0.99 1.00 10088 1.00
p_prior[641] 1.00 1.00 10089 1.00
p_prior[642] 0.99 1.00 10105 1.00
p_prior[643] 1.00 1.00 10105 1.00
p_prior[644] 0.99 1.00 10102 1.00
p_prior[645] 1.00 1.00 10102 1.00
p_prior[646] 0.97 1.00 9843 1.00
p_prior[647] 0.97 1.00 9843 1.00
p_prior[648] 0.97 1.00 9832 1.00
p_prior[649] 0.97 1.00 9832 1.00
p_prior[650] 0.97 1.00 9816 1.00
p_prior[651] 0.97 1.00 9816 1.00
p_prior[652] 0.99 1.00 9831 1.00
p_prior[653] 0.99 1.00 9831 1.00
p_prior[654] 0.99 1.00 9823 1.00
p_prior[655] 0.99 1.00 9823 1.00
p_prior[656] 0.99 1.00 9812 1.00
p_prior[657] 0.99 1.00 9812 1.00
p_prior[658] 0.99 1.00 9801 1.00
p_prior[659] 0.99 1.00 9801 1.00
p_prior[660] 0.99 1.00 9793 1.00
p_prior[661] 0.99 1.00 9793 1.00
p_prior[662] 0.99 1.00 9790 1.00
p_prior[663] 0.99 1.00 9790 1.00
p_prior[664] 0.99 1.00 9739 1.00
p_prior[665] 0.99 1.00 9739 1.00
p_prior[666] 0.99 1.00 9711 1.00
p_prior[667] 0.99 1.00 9722 1.00
p_prior[668] 0.99 1.00 9716 1.00
p_prior[669] 0.99 1.00 9715 1.00
p_prior[670] 0.99 1.00 9711 1.00
p_prior[671] 0.99 1.00 9721 1.00
p_prior[672] 0.99 1.00 9692 1.00
p_prior[673] 0.99 1.00 9690 1.00
p_prior[674] 0.99 1.00 9690 1.00
p_prior[675] 0.99 1.00 9690 1.00
p_prior[676] 0.99 1.00 9689 1.00
p_prior[677] 0.99 1.00 9689 1.00
p_prior[678] 0.99 1.00 9687 1.00
p_prior[679] 0.99 1.00 9686 1.00
p_prior[680] 0.99 1.00 9685 1.00
p_prior[681] 0.99 1.00 9685 1.00
p_prior[682] 0.99 1.00 9684 1.00
p_prior[683] 0.99 1.00 9683 1.00
p_prior[684] 0.99 1.00 9683 1.00
p_prior[685] 0.99 1.00 10139 1.00
p_prior[686] 0.99 1.00 10132 1.00
p_prior[687] 0.99 1.00 10139 1.00
p_prior[688] 0.99 1.00 10160 1.00
p_prior[689] 0.99 1.00 10150 1.00
p_prior[690] 0.99 1.00 10160 1.00
p_prior[691] 0.99 1.00 10163 1.00
p_prior[692] 0.99 1.00 10153 1.00
p_prior[693] 0.99 1.00 10163 1.00
p_prior[694] 0.99 1.00 10171 1.00
p_prior[695] 0.99 1.00 10160 1.00
p_prior[696] 0.99 1.00 10171 1.00
p_prior[697] 0.99 1.00 10146 1.00
p_prior[698] 0.99 1.00 10139 1.00
p_prior[699] 0.99 1.00 10146 1.00
p_prior[700] 0.99 1.00 10146 1.00
p_prior[701] 0.99 1.00 10139 1.00
p_prior[702] 0.99 1.00 10146 1.00
p_prior[703] 0.99 1.00 10149 1.00
p_prior[704] 0.99 1.00 10141 1.00
p_prior[705] 0.99 1.00 10149 1.00
p_prior[706] 0.99 1.00 9765 1.00
p_prior[707] 0.99 1.00 9804 1.00
p_prior[708] 0.58 0.74 9810 1.00
p_prior[709] 0.58 0.74 10048 1.00
p_prior[710] 0.58 0.74 10048 1.00
p_prior[711] 0.58 0.74 10043 1.00
p_prior[712] 0.58 0.74 10036 1.00
p_prior[713] 0.58 0.74 9832 1.00
p_prior[714] 0.58 0.75 9848 1.00
p_prior[715] 0.59 0.77 9808 1.00
p_prior[716] 0.60 0.79 9849 1.00
p_prior[717] 1.00 1.00 9794 1.00
p_prior[718] 1.00 1.00 9817 1.00
p_prior[719] 1.00 1.00 9825 1.00
p_prior[720] 1.00 1.00 9826 1.00
p_prior[721] 1.00 1.00 9831 1.00
p_prior[722] 1.00 1.00 9831 1.00
p_prior[723] 1.00 1.00 9833 1.00
p_prior[724] 1.00 1.00 9839 1.00
p_prior[725] 1.00 1.00 9846 1.00
p_prior[726] 0.99 1.00 10033 1.00
p_prior[727] 0.99 1.00 10041 1.00
p_prior[728] 0.99 1.00 10107 1.00
p_prior[729] 0.99 1.00 9862 1.00
p_prior[730] 0.99 1.00 9859 1.00
p_prior[731] 0.99 1.00 9846 1.00
p_prior[732] 0.99 1.00 9868 1.00
p_prior[733] 0.99 1.00 9867 1.00
p_prior[734] 0.99 1.00 9854 1.00
p_prior[735] 0.99 1.00 9845 1.00
p_prior[736] 0.99 1.00 9842 1.00
p_prior[737] 0.99 1.00 9868 1.00
p_prior[738] 0.99 1.00 9868 1.00
p_prior[739] 0.99 1.00 10160 1.00
p_prior[740] 0.99 1.00 10160 1.00
p_prior[741] 0.99 1.00 10166 1.00
p_prior[742] 0.99 1.00 10156 1.00
p_prior[743] 0.99 1.00 10163 1.00
p_prior[744] 0.99 1.00 10147 1.00
p_prior[745] 0.99 1.00 10164 1.00
p_prior[746] 0.99 1.00 10162 1.00
p_prior[747] 0.99 1.00 10129 1.00
p_prior[748] 0.99 1.00 9854 1.00
p_prior[749] 0.99 1.00 9864 1.00
p_prior[750] 0.99 1.00 9861 1.00
p_prior[751] 0.99 1.00 9867 1.00
p_prior[752] 0.99 1.00 9870 1.00
p_prior[753] 0.99 1.00 9881 1.00
p_prior[754] 0.99 1.00 10182 1.00
p_prior[755] 0.99 1.00 10182 1.00
p_prior[756] 0.99 1.00 10185 1.00
p_prior[757] 0.99 1.00 10188 1.00
p_prior[758] 0.99 1.00 10198 1.00
p_prior[759] 0.99 1.00 10193 1.00
p_prior[760] 0.99 1.00 10194 1.00
p_prior[761] 0.99 1.00 10194 1.00
p_prior[762] 0.99 1.00 10195 1.00
p_prior[763] 0.99 1.00 10199 1.00
p_prior[764] 0.99 1.00 10200 1.00
p_prior[765] 0.99 1.00 10163 1.00
p_prior[766] 0.99 1.00 10182 1.00
p_prior[767] 0.99 1.00 10185 1.00
p_prior[768] 0.99 1.00 10203 1.00
p_prior[769] 0.99 1.00 10199 1.00
p_prior[770] 0.99 1.00 10193 1.00
p_prior[771] 0.99 1.00 10195 1.00
p_prior[772] 0.99 1.00 10197 1.00
p_prior[773] 0.99 1.00 10197 1.00
p_prior[774] 0.99 1.00 10160 1.00
p_prior[775] 0.99 1.00 10160 1.00
p_prior[776] 0.99 1.00 10163 1.00
p_prior[777] 0.99 1.00 10182 1.00
p_prior[778] 0.99 1.00 10183 1.00
p_prior[779] 0.99 1.00 10194 1.00
p_prior[780] 0.99 1.00 10198 1.00
p_prior[781] 0.99 1.00 10200 1.00
p_prior[782] 0.99 1.00 10199 1.00
p_prior[783] 0.99 1.00 10162 1.00
p_prior[784] 0.99 1.00 10163 1.00
p_prior[785] 0.99 1.00 10166 1.00
p_prior[786] 0.99 1.00 10166 1.00
p_prior[787] 0.56 0.68 10166 1.00
p_prior[788] 0.56 0.68 10165 1.00
p_prior[789] 0.56 0.68 10164 1.00
p_prior[790] 0.56 0.68 10166 1.00
p_prior[791] 0.56 0.68 9841 1.00
p_prior[792] 0.56 0.68 9856 1.00
p_prior[793] 0.56 0.68 9856 1.00
p_prior[794] 0.56 0.68 9859 1.00
p_prior[795] 1.00 1.00 9813 1.00
p_prior[796] 1.00 1.00 9818 1.00
p_prior[797] 1.00 1.00 9820 1.00
p_prior[798] 1.00 1.00 9822 1.00
p_prior[799] 1.00 1.00 9828 1.00
p_prior[800] 1.00 1.00 9832 1.00
p_prior[801] 1.00 1.00 9842 1.00
p_prior[802] 1.00 1.00 9842 1.00
p_prior[803] 0.99 1.00 9602 1.00
p_prior[804] 0.99 1.00 9594 1.00
p_prior[805] 0.99 1.00 9602 1.00
p_prior[806] 0.99 1.00 9594 1.00
p_prior[807] 0.99 1.00 9586 1.00
p_prior[808] 0.99 1.00 9584 1.00
p_prior[809] 0.99 1.00 9613 1.00
p_prior[810] 0.99 1.00 9583 1.00
p_prior[811] 0.99 1.00 9583 1.00
p_prior[812] 0.99 1.00 9580 1.00
p_prior[813] 0.99 1.00 9580 1.00
p_prior[814] 0.99 1.00 9745 1.00
p_prior[815] 0.99 1.00 9716 1.00
p_prior[816] 0.99 1.00 9716 1.00
p_prior[817] 0.99 1.00 9707 1.00
p_prior[818] 0.99 1.00 9685 1.00
p_prior[819] 0.99 1.00 9701 1.00
p_prior[820] 0.99 1.00 9689 1.00
p_prior[821] 0.99 1.00 9682 1.00
p_prior[822] 0.99 1.00 9685 1.00
p_prior[823] 0.99 1.00 9673 1.00
p_prior[824] 0.99 1.00 10189 1.00
p_prior[825] 0.99 1.00 10172 1.00
p_prior[826] 1.00 1.00 10061 1.00
p_prior[827] 1.00 1.00 10056 1.00
p_prior[828] 1.00 1.00 10113 1.00
p_prior[829] 1.00 1.00 10124 1.00
p_prior[830] 1.00 1.00 10126 1.00
p_prior[831] 1.00 1.00 10128 1.00
p_prior[832] 0.98 1.00 9807 1.00
p_prior[833] 0.62 0.82 9549 1.00
p_prior[834] 0.98 1.00 9792 1.00
p_prior[835] 0.62 0.83 9559 1.00
p_prior[836] 0.98 1.00 9792 1.00
p_prior[837] 0.61 0.83 9550 1.00
p_prior[838] 0.98 1.00 9792 1.00
p_prior[839] 0.61 0.83 9556 1.00
p_prior[840] 0.98 1.00 9792 1.00
p_prior[841] 0.63 0.85 9630 1.00
p_prior[842] 0.98 1.00 9792 1.00
p_prior[843] 0.63 0.85 9618 1.00
p_prior[844] 0.98 1.00 9791 1.00
p_prior[845] 0.63 0.85 9728 1.00
p_prior[846] 0.98 1.00 9747 1.00
p_prior[847] 0.63 0.85 9849 1.00
p_prior[848] 0.98 1.00 9745 1.00
p_prior[849] 0.64 0.86 9890 1.00
p_prior[850] 0.98 1.00 9745 1.00
p_prior[851] 0.64 0.86 9898 1.00
p_prior[852] 0.98 1.00 9737 1.00
p_prior[853] 0.65 0.88 9899 1.00
p_prior[854] 0.57 0.71 10086 1.00
p_prior[855] 0.57 0.71 10090 1.00
p_prior[856] 0.57 0.72 10093 1.00
p_prior[857] 0.57 0.72 10093 1.00
p_prior[858] 0.57 0.72 10092 1.00
p_prior[859] 0.57 0.72 10088 1.00
p_prior[860] 0.57 0.72 9766 1.00
p_prior[861] 0.58 0.73 9787 1.00
p_prior[862] 0.58 0.74 9798 1.00
p_prior[863] 0.58 0.73 9791 1.00
p_prior[864] 0.58 0.73 9787 1.00
p_prior[865] 0.57 0.73 9782 1.00
p_prior[866] 0.99 1.00 9799 1.00
p_prior[867] 0.99 1.00 9812 1.00
p_prior[868] 0.99 1.00 9820 1.00
p_prior[869] 0.99 1.00 9821 1.00
p_prior[870] 0.99 1.00 9843 1.00
p_prior[871] 0.99 1.00 9844 1.00
p_prior[872] 0.98 1.00 9714 1.00
p_prior[873] 0.98 1.00 9753 1.00
p_prior[874] 0.98 1.00 9761 1.00
p_prior[875] 0.98 1.00 9762 1.00
p_prior[876] 0.99 1.00 9832 1.00
p_prior[877] 0.99 1.00 9824 1.00
p_prior[878] 0.99 1.00 9815 1.00
p_prior[879] 0.99 1.00 9795 1.00
p_prior[880] 0.99 1.00 9784 1.00
p_prior[881] 0.99 1.00 10184 1.00
p_prior[882] 0.99 1.00 10189 1.00
p_prior[883] 0.99 1.00 10189 1.00
p_prior[884] 0.99 1.00 10194 1.00
p_prior[885] 0.99 1.00 10198 1.00
p_prior[886] 0.99 1.00 10195 1.00
p_prior[887] 0.99 1.00 10186 1.00
p_prior[888] 0.99 1.00 10185 1.00
p_prior[889] 0.99 1.00 10187 1.00
p_prior[890] 0.99 1.00 10188 1.00
p_prior[891] 0.99 1.00 10194 1.00
p_prior[892] 0.99 1.00 10157 1.00
p_prior[893] 0.99 1.00 10147 1.00
p_prior[894] 0.99 1.00 10158 1.00
p_prior[895] 0.99 1.00 10127 1.00
p_prior[896] 0.99 1.00 10130 1.00
p_prior[897] 0.58 0.74 9810 1.00
p_prior[898] 0.58 0.74 10047 1.00
p_prior[899] 0.59 0.77 9808 1.00
p_prior[900] 0.59 0.77 9822 1.00
p_prior[901] 0.60 0.78 9837 1.00
p_prior[902] 0.60 0.79 9852 1.00
p_prior[903] 0.98 1.00 9737 1.00
p_prior[904] 0.98 1.00 9734 1.00
p_prior[905] 0.98 1.00 9776 1.00
p_prior[906] 0.98 1.00 9775 1.00
p_prior[907] 0.98 1.00 9775 1.00
p_prior[908] 0.99 1.00 10137 1.00
p_prior[909] 0.99 1.00 10132 1.00
p_prior[910] 0.99 1.00 10137 1.00
p_prior[911] 0.99 1.00 10145 1.00
p_prior[912] 0.99 1.00 10137 1.00
p_prior[913] 0.99 1.00 10145 1.00
p_prior[914] 0.99 1.00 10136 1.00
p_prior[915] 0.99 1.00 10129 1.00
p_prior[916] 0.99 1.00 10136 1.00
p_prior[917] 0.99 1.00 10151 1.00
p_prior[918] 0.99 1.00 10142 1.00
p_prior[919] 0.99 1.00 10151 1.00
p_prior[920] 0.98 1.00 9815 1.00
p_prior[921] 0.98 1.00 9750 1.00
p_prior[922] 0.98 1.00 9750 1.00
p_prior[923] 0.98 1.00 9751 1.00
p_prior[924] 0.99 1.00 9772 1.00
p_prior[925] 0.99 1.00 9798 1.00
p_prior[926] 0.99 1.00 9743 1.00
p_prior[927] 0.99 1.00 9742 1.00
p_prior[928] 0.99 1.00 10183 1.00
p_prior[929] 0.99 1.00 10183 1.00
p_prior[930] 0.99 1.00 10180 1.00
p_prior[931] 0.99 1.00 10183 1.00
p_prior[932] 0.99 1.00 10184 1.00
p_prior[933] 0.99 1.00 10189 1.00
p_prior[934] 0.99 1.00 10190 1.00
p_prior[935] 0.99 1.00 10199 1.00
p_prior[936] 0.99 1.00 10195 1.00
p_prior[937] 0.99 1.00 10196 1.00
p_prior[938] 0.99 1.00 10201 1.00
p_prior[939] 0.99 1.00 10204 1.00
p_prior[940] 0.99 1.00 9766 1.00
p_prior[941] 0.99 1.00 9765 1.00
p_prior[942] 0.99 1.00 9805 1.00
p_prior[943] 0.99 1.00 9803 1.00
p_prior[944] 0.99 1.00 9803 1.00
p_prior[945] 0.99 1.00 9803 1.00
p_prior[946] 0.97 1.00 9833 1.00
p_prior[947] 0.97 1.00 9833 1.00
p_prior[948] 0.97 1.00 9833 1.00
p_prior[949] 0.97 1.00 9832 1.00
p_prior[950] 0.97 1.00 9832 1.00
p_prior[951] 0.97 1.00 9832 1.00
p_prior[952] 0.97 1.00 9829 1.00
p_prior[953] 0.97 1.00 9829 1.00
p_prior[954] 0.97 1.00 9829 1.00
p_prior[955] 0.97 1.00 9810 1.00
p_prior[956] 0.97 1.00 9810 1.00
p_prior[957] 0.97 1.00 9810 1.00
p_prior[958] 0.97 1.00 9837 1.00
p_prior[959] 0.97 1.00 9837 1.00
p_prior[960] 0.97 1.00 9837 1.00
p_prior[961] 0.97 1.00 9833 1.00
p_prior[962] 0.97 1.00 9833 1.00
p_prior[963] 0.97 1.00 9833 1.00
p_prior[964] 0.97 1.00 9832 1.00
p_prior[965] 0.97 1.00 9832 1.00
p_prior[966] 0.97 1.00 9832 1.00
p_prior[967] 0.97 1.00 9826 1.00
p_prior[968] 0.97 1.00 9826 1.00
p_prior[969] 0.97 1.00 9826 1.00
p_prior[970] 0.97 1.00 9817 1.00
p_prior[971] 0.97 1.00 9817 1.00
p_prior[972] 0.97 1.00 9817 1.00
p_prior[973] 0.99 1.00 10178 1.00
p_prior[974] 0.99 1.00 10183 1.00
p_prior[975] 0.99 1.00 10183 1.00
p_prior[976] 0.99 1.00 10184 1.00
p_prior[977] 0.99 1.00 10180 1.00
p_prior[978] 0.99 1.00 10183 1.00
p_prior[979] 0.99 1.00 10186 1.00
p_prior[980] 0.99 1.00 10189 1.00
p_prior[981] 0.99 1.00 10203 1.00
p_prior[982] 0.99 1.00 10203 1.00
p_prior[983] 0.99 1.00 10199 1.00
p_prior[984] 0.99 1.00 10195 1.00
p_prior[985] 0.99 1.00 10197 1.00
p_prior[986] 0.99 1.00 10201 1.00
p_prior[987] 0.99 1.00 10204 1.00
p_prior[988] 0.99 1.00 10165 1.00
p_prior[989] 0.99 1.00 9814 1.00
p_prior[990] 0.99 1.00 9838 1.00
p_prior[991] 0.99 1.00 9843 1.00
p_prior[992] 0.99 1.00 10149 1.00
p_prior[993] 0.99 1.00 10094 1.00
p_prior[994] 0.99 1.00 10109 1.00
p_prior[995] 0.99 1.00 10140 1.00
p_prior[996] 0.99 1.00 10147 1.00
p_prior[997] 0.99 1.00 10162 1.00
p_prior[998] 0.99 1.00 10163 1.00
p_prior[999] 0.99 1.00 10166 1.00
p_prior[1000] 0.99 1.00 10127 1.00
p_prior[1001] 0.99 1.00 10132 1.00
p_prior[1002] 0.99 1.00 10132 1.00
p_prior[1003] 0.99 1.00 10130 1.00
p_prior[1004] 0.99 1.00 10143 1.00
p_prior[1005] 0.99 1.00 10122 1.00
p_prior[1006] 0.99 1.00 10131 1.00
p_prior[1007] 0.99 1.00 10123 1.00
p_prior[1008] 0.99 1.00 9717 1.00
p_prior[1009] 0.99 1.00 9684 1.00
p_prior[1010] 0.99 1.00 9673 1.00
p_prior[1011] 0.99 1.00 9746 1.00
p_prior[1012] 0.99 1.00 9715 1.00
p_prior[1013] 0.99 1.00 9680 1.00
p_prior[1014] 0.99 1.00 9677 1.00
p_prior[1015] 0.99 1.00 9675 1.00
p_prior[1016] 0.99 1.00 9624 1.00
p_prior[1017] 0.99 1.00 9648 1.00
p_prior[1018] 0.99 1.00 9641 1.00
p_prior[1019] 0.99 1.00 9637 1.00
p_prior[1020] 0.99 1.00 9631 1.00
p_prior[1021] 0.99 1.00 9627 1.00
p_prior[1022] 0.99 1.00 9626 1.00
p_prior[1023] 0.99 1.00 9745 1.00
p_prior[1024] 0.99 1.00 9716 1.00
p_prior[1025] 0.99 1.00 9703 1.00
p_prior[1026] 0.99 1.00 9676 1.00
p_prior[1027] 0.99 1.00 9675 1.00
p_prior[1028] 0.99 1.00 9652 1.00
p_prior[1029] 0.99 1.00 9645 1.00
p_prior[1030] 0.99 1.00 9643 1.00
p_prior[1031] 0.99 1.00 9637 1.00
p_prior[1032] 0.99 1.00 9633 1.00
p_prior[1033] 0.99 1.00 9628 1.00
p_prior[1034] 1.00 1.00 9834 1.00
p_prior[1035] 0.99 1.00 9815 1.00
p_prior[1036] 1.00 1.00 9827 1.00
p_prior[1037] 0.99 1.00 9809 1.00
p_prior[1038] 1.00 1.00 9817 1.00
p_prior[1039] 0.99 1.00 9800 1.00
p_prior[1040] 1.00 1.00 9817 1.00
p_prior[1041] 0.99 1.00 9800 1.00
p_prior[1042] 1.00 1.00 9815 1.00
p_prior[1043] 0.99 1.00 9797 1.00
p_prior[1044] 1.00 1.00 9812 1.00
p_prior[1045] 0.99 1.00 9795 1.00
p_prior[1046] 1.00 1.00 9806 1.00
p_prior[1047] 0.99 1.00 9786 1.00
p_prior[1048] 0.98 1.00 9915 1.00
p_prior[1049] 0.98 1.00 9915 1.00
p_prior[1050] 0.98 1.00 9910 1.00
p_prior[1051] 0.98 1.00 9910 1.00
p_prior[1052] 0.98 1.00 9907 1.00
p_prior[1053] 0.98 1.00 9907 1.00
p_prior[1054] 0.98 1.00 9906 1.00
p_prior[1055] 0.98 1.00 9906 1.00
p_prior[1056] 0.98 1.00 9876 1.00
p_prior[1057] 0.98 1.00 9876 1.00
p_prior[1058] 0.98 1.00 9914 1.00
p_prior[1059] 0.98 1.00 9911 1.00
p_prior[1060] 0.98 1.00 9910 1.00
p_prior[1061] 0.98 1.00 9907 1.00
p_prior[1062] 0.98 1.00 9907 1.00
p_prior[1063] 0.98 1.00 9909 1.00
p_prior[1064] 0.98 1.00 9907 1.00
p_prior[1065] 0.98 1.00 9878 1.00
p_prior[1066] 0.98 1.00 9876 1.00
p_prior[1067] 0.99 1.00 10019 1.00
p_prior[1068] 0.99 1.00 10017 1.00
p_prior[1069] 0.99 1.00 9974 1.00
p_prior[1070] 0.97 1.00 9817 1.00
p_prior[1071] 0.97 1.00 9817 1.00
p_prior[1072] 0.97 1.00 9801 1.00
p_prior[1073] 0.97 1.00 9801 1.00
p_prior[1074] 0.97 1.00 9808 1.00
p_prior[1075] 0.97 1.00 9808 1.00
p_prior[1076] 0.97 1.00 9805 1.00
p_prior[1077] 0.97 1.00 9805 1.00
p_prior[1078] 0.97 1.00 9799 1.00
p_prior[1079] 0.97 1.00 9799 1.00
p_prior[1080] 0.59 0.76 9790 1.00
p_prior[1081] 0.59 0.77 10043 1.00
p_prior[1082] 0.59 0.77 9792 1.00
p_prior[1083] 0.59 0.77 9801 1.00
p_prior[1084] 0.59 0.77 9821 1.00
p_prior[1085] 0.60 0.78 9843 1.00
p_prior[1086] 0.60 0.79 9845 1.00
p_prior[1087] 0.99 1.00 9849 1.00
p_prior[1088] 0.99 1.00 9848 1.00
p_prior[1089] 0.99 1.00 9850 1.00
p_prior[1090] 0.99 1.00 9848 1.00
p_prior[1091] 0.99 1.00 9836 1.00
p_prior[1092] 0.99 1.00 9847 1.00
p_prior[1093] 0.99 1.00 9836 1.00
p_prior[1094] 0.99 1.00 9845 1.00
p_prior[1095] 0.98 1.00 9837 1.00
p_prior[1096] 0.99 1.00 9837 1.00
p_prior[1097] 0.99 1.00 9834 1.00
p_prior[1098] 0.98 1.00 9828 1.00
p_prior[1099] 0.98 1.00 9831 1.00
p_prior[1100] 0.98 1.00 9828 1.00
p_prior[1101] 0.99 1.00 9824 1.00
p_prior[1102] 0.98 1.00 9739 1.00
p_prior[1103] 0.98 1.00 9739 1.00
p_prior[1104] 0.98 1.00 9737 1.00
p_prior[1105] 0.98 1.00 9784 1.00
p_prior[1106] 0.98 1.00 9773 1.00
p_prior[1107] 0.98 1.00 9741 1.00
p_prior[1108] 0.98 1.00 9740 1.00
p_prior[1109] 0.98 1.00 9740 1.00
p_prior[1110] 0.98 1.00 9777 1.00
p_prior[1111] 0.98 1.00 9775 1.00
p_prior[1112] 0.57 0.71 10084 1.00
p_prior[1113] 0.57 0.71 10088 1.00
p_prior[1114] 0.57 0.72 10093 1.00
p_prior[1115] 0.57 0.72 9761 1.00
p_prior[1116] 0.57 0.72 9765 1.00
p_prior[1117] 0.57 0.73 9782 1.00
p_prior[1118] 0.58 0.73 9786 1.00
p_prior[1119] 0.57 0.73 9775 1.00
p_prior[1120] 0.58 0.73 9783 1.00
p_prior[1121] 0.57 0.72 9772 1.00
p_prior[1122] 0.58 0.73 9790 1.00
p_prior[1123] 0.97 1.00 9851 1.00
p_prior[1124] 0.97 1.00 9834 1.00
p_prior[1125] 0.97 1.00 9828 1.00
p_prior[1126] 0.97 1.00 9826 1.00
p_prior[1127] 0.97 1.00 9813 1.00
p_prior[1128] 0.99 1.00 10115 1.00
p_prior[1129] 0.99 1.00 9804 1.00
p_prior[1130] 0.99 1.00 9837 1.00
p_prior[1131] 0.99 1.00 10151 1.00
p_prior[1132] 0.99 1.00 10144 1.00
p_prior[1133] 0.99 1.00 10151 1.00
p_prior[1134] 0.99 1.00 10142 1.00
p_prior[1135] 0.99 1.00 10136 1.00
p_prior[1136] 0.99 1.00 10142 1.00
p_prior[1137] 0.99 1.00 10172 1.00
p_prior[1138] 0.99 1.00 10162 1.00
p_prior[1139] 0.99 1.00 10172 1.00
p_prior[1140] 0.99 1.00 10149 1.00
p_prior[1141] 0.99 1.00 10142 1.00
p_prior[1142] 0.99 1.00 10149 1.00
p_prior[1143] 0.99 1.00 10147 1.00
p_prior[1144] 0.99 1.00 10139 1.00
p_prior[1145] 0.99 1.00 10147 1.00
p_prior[1146] 0.99 1.00 10149 1.00
p_prior[1147] 0.99 1.00 10141 1.00
p_prior[1148] 0.99 1.00 10149 1.00
p_prior[1149] 0.99 1.00 10143 1.00
p_prior[1150] 0.99 1.00 10136 1.00
p_prior[1151] 0.99 1.00 10143 1.00
p_prior[1152] 0.99 1.00 10156 1.00
p_prior[1153] 0.99 1.00 10147 1.00
p_prior[1154] 0.99 1.00 10156 1.00
p_prior[1155] 0.98 1.00 9938 1.00
p_prior[1156] 0.98 1.00 9968 1.00
p_prior[1157] 0.98 1.00 9967 1.00
p_prior[1158] 0.98 1.00 9960 1.00
p_prior[1159] 0.98 1.00 9931 1.00
p_prior[1160] 0.98 1.00 9925 1.00
p_prior[1161] 0.99 1.00 10143 1.00
p_prior[1162] 0.99 1.00 10135 1.00
p_prior[1163] 0.99 1.00 10143 1.00
p_prior[1164] 0.99 1.00 10137 1.00
p_prior[1165] 0.99 1.00 10130 1.00
p_prior[1166] 0.99 1.00 10137 1.00
p_prior[1167] 0.99 1.00 10155 1.00
p_prior[1168] 0.99 1.00 10145 1.00
p_prior[1169] 0.99 1.00 10155 1.00
p_prior[1170] 1.00 1.00 10083 1.00
p_prior[1171] 1.00 1.00 10036 1.00
p_prior[1172] 1.00 1.00 10036 1.00
p_prior[1173] 1.00 1.00 10032 1.00
p_prior[1174] 1.00 1.00 10074 1.00
p_prior[1175] 0.63 0.85 9397 1.00
p_prior[1176] 0.63 0.86 9474 1.00
p_prior[1177] 0.64 0.87 9850 1.00
p_prior[1178] 0.64 0.87 9721 1.00
p_prior[1179] 0.64 0.87 9866 1.00
p_prior[1180] 0.65 0.88 9890 1.00
p_prior[1181] 0.65 0.88 9898 1.00
p_prior[1182] 0.99 1.00 10175 1.00
p_prior[1183] 0.99 1.00 10168 1.00
p_prior[1184] 0.99 1.00 10165 1.00
p_prior[1185] 0.99 1.00 9716 1.00
p_prior[1186] 0.99 1.00 9678 1.00
p_prior[1187] 0.99 1.00 9672 1.00
p_prior[1188] 0.99 1.00 9669 1.00
p_prior[1189] 0.99 1.00 9634 1.00
p_prior[1190] 0.99 1.00 9716 1.00
p_prior[1191] 0.99 1.00 9678 1.00
p_prior[1192] 0.99 1.00 9675 1.00
p_prior[1193] 0.99 1.00 9672 1.00
p_prior[1194] 0.99 1.00 9642 1.00
p_prior[1195] 0.99 1.00 9863 1.00
p_prior[1196] 0.99 1.00 9863 1.00
p_prior[1197] 0.99 1.00 9875 1.00
p_prior[1198] 0.99 1.00 9854 1.00
p_prior[1199] 0.99 1.00 9848 1.00
p_prior[1200] 0.99 1.00 9847 1.00
p_prior[1201] 0.99 1.00 9847 1.00
p_prior[1202] 0.99 1.00 9849 1.00
p_prior[1203] 0.99 1.00 9880 1.00
p_prior[1204] 0.99 1.00 9865 1.00
p_prior[1205] 1.00 1.00 9799 1.00
p_prior[1206] 1.00 1.00 9832 1.00
p_prior[1207] 0.99 1.00 9749 1.00
p_prior[1208] 0.99 1.00 9750 1.00
p_prior[1209] 0.99 1.00 9724 1.00
p_prior[1210] 0.99 1.00 9680 1.00
p_prior[1211] 0.99 1.00 9673 1.00
p_prior[1212] 0.99 1.00 9641 1.00
p_prior[1213] 0.97 1.00 9847 1.00
p_prior[1214] 0.97 1.00 9843 1.00
p_prior[1215] 0.97 1.00 9822 1.00
p_prior[1216] 0.97 1.00 9822 1.00
p_prior[1217] 0.97 1.00 9816 1.00
p_prior[1218] 0.99 1.00 10068 1.00
p_prior[1219] 0.99 1.00 10102 1.00
p_prior[1220] 0.99 1.00 10112 1.00
p_prior[1221] 0.99 1.00 10146 1.00
p_prior[1222] 0.99 1.00 10147 1.00
p_prior[1223] 0.99 1.00 10146 1.00
p_prior[1224] 0.99 1.00 10147 1.00
p_prior[1225] 0.99 1.00 9864 1.00
p_prior[1226] 0.99 1.00 9868 1.00
p_prior[1227] 0.99 1.00 9862 1.00
p_prior[1228] 0.99 1.00 9866 1.00
p_prior[1229] 0.56 0.68 10162 1.00
p_prior[1230] 0.56 0.68 10162 1.00
p_prior[1231] 0.56 0.68 10165 1.00
p_prior[1232] 0.56 0.68 10165 1.00
p_prior[1233] 0.56 0.68 10158 1.00
p_prior[1234] 0.56 0.68 10158 1.00
p_prior[1235] 0.56 0.68 9869 1.00
p_prior[1236] 0.56 0.68 9869 1.00
p_prior[1237] 0.56 0.69 9900 1.00
p_prior[1238] 0.56 0.69 9900 1.00
p_prior[1239] 0.57 0.70 9913 1.00
p_prior[1240] 0.57 0.70 9913 1.00
p_prior[1241] 0.57 0.70 9911 1.00
p_prior[1242] 0.57 0.70 9911 1.00
p_prior[1243] 0.57 0.70 9914 1.00
p_prior[1244] 0.57 0.70 9914 1.00
p_prior[1245] 0.99 1.00 10150 1.00
p_prior[1246] 0.99 1.00 10144 1.00
p_prior[1247] 0.99 1.00 10150 1.00
p_prior[1248] 0.99 1.00 10156 1.00
p_prior[1249] 0.99 1.00 10149 1.00
p_prior[1250] 0.99 1.00 10156 1.00
p_prior[1251] 0.99 1.00 10162 1.00
p_prior[1252] 0.99 1.00 10154 1.00
p_prior[1253] 0.99 1.00 10162 1.00
p_prior[1254] 0.99 1.00 10169 1.00
p_prior[1255] 0.99 1.00 10160 1.00
p_prior[1256] 0.99 1.00 10169 1.00
p_prior[1257] 0.99 1.00 10171 1.00
p_prior[1258] 0.99 1.00 10162 1.00
p_prior[1259] 0.99 1.00 10171 1.00
p_prior[1260] 0.99 1.00 10145 1.00
p_prior[1261] 0.99 1.00 10139 1.00
p_prior[1262] 0.99 1.00 10145 1.00
p_prior[1263] 0.99 1.00 9940 1.00
p_prior[1264] 0.99 1.00 9939 1.00
p_prior[1265] 0.99 1.00 9937 1.00
p_prior[1266] 0.99 1.00 9936 1.00
p_prior[1267] 0.99 1.00 9908 1.00
p_prior[1268] 0.99 1.00 10171 1.00
p_prior[1269] 0.99 1.00 10152 1.00
p_prior[1270] 0.98 1.00 9954 1.00
p_prior[1271] 0.98 1.00 9945 1.00
p_prior[1272] 0.98 1.00 9943 1.00
p_prior[1273] 0.98 1.00 9913 1.00
p_prior[1274] 0.99 1.00 9846 1.00
p_prior[1275] 0.99 1.00 9846 1.00
p_prior[1276] 0.99 1.00 9827 1.00
p_prior[1277] 0.99 1.00 9827 1.00
p_prior[1278] 0.98 1.00 9691 1.00
p_prior[1279] 0.98 1.00 9691 1.00
p_prior[1280] 0.98 1.00 9683 1.00
p_prior[1281] 0.98 1.00 9681 1.00
p_prior[1282] 0.98 1.00 9704 1.00
p_prior[1283] 0.98 1.00 9705 1.00
p_prior[1284] 0.98 1.00 9704 1.00
p_prior[1285] 0.98 1.00 9702 1.00
p_prior[1286] 0.98 1.00 9693 1.00
p_prior[1287] 0.98 1.00 9693 1.00
p_prior[1288] 0.98 1.00 9692 1.00
p_prior[1289] 0.98 1.00 9967 1.00
p_prior[1290] 0.98 1.00 9962 1.00
p_prior[1291] 0.98 1.00 9934 1.00
p_prior[1292] 0.98 1.00 9924 1.00
p_prior[1293] 0.99 1.00 10130 1.00
p_prior[1294] 0.99 1.00 10135 1.00
p_prior[1295] 0.99 1.00 10161 1.00
p_prior[1296] 0.99 1.00 10170 1.00
p_prior[1297] 0.99 1.00 10149 1.00
p_prior[1298] 0.97 1.00 9855 1.00
p_prior[1299] 0.97 1.00 9855 1.00
p_prior[1300] 0.97 1.00 9818 1.00
p_prior[1301] 0.97 1.00 9818 1.00
p_prior[1302] 0.97 1.00 9837 1.00
p_prior[1303] 0.97 1.00 9837 1.00
p_prior[1304] 0.97 1.00 9832 1.00
p_prior[1305] 0.97 1.00 9832 1.00
p_prior[1306] 0.97 1.00 9839 1.00
p_prior[1307] 0.97 1.00 9839 1.00
p_prior[1308] 0.97 1.00 9831 1.00
p_prior[1309] 0.97 1.00 9831 1.00
p_prior[1310] 0.97 1.00 9829 1.00
p_prior[1311] 0.97 1.00 9829 1.00
p_prior[1312] 0.98 1.00 9939 1.00
p_prior[1313] 0.98 1.00 9965 1.00
p_prior[1314] 0.98 1.00 9963 1.00
p_prior[1315] 0.98 1.00 9959 1.00
p_prior[1316] 0.98 1.00 9932 1.00
p_prior[1317] 0.98 1.00 9928 1.00
p_prior[1318] 0.98 1.00 9924 1.00
p_prior[1319] 0.98 1.00 9923 1.00
p_prior[1320] 0.98 1.00 9939 1.00
p_prior[1321] 0.98 1.00 9965 1.00
p_prior[1322] 0.98 1.00 9963 1.00
p_prior[1323] 0.98 1.00 9959 1.00
p_prior[1324] 0.98 1.00 9932 1.00
p_prior[1325] 0.98 1.00 9928 1.00
p_prior[1326] 0.98 1.00 9923 1.00
p_prior[1327] 0.97 1.00 9838 1.00
p_prior[1328] 0.97 1.00 9835 1.00
p_prior[1329] 0.97 1.00 9811 1.00
p_prior[1330] 0.98 1.00 9938 1.00
p_prior[1331] 0.98 1.00 9938 1.00
p_prior[1332] 0.98 1.00 9967 1.00
p_prior[1333] 0.98 1.00 9964 1.00
p_prior[1334] 0.98 1.00 9964 1.00
p_prior[1335] 0.98 1.00 9963 1.00
p_prior[1336] 0.98 1.00 9963 1.00
p_prior[1337] 0.98 1.00 9963 1.00
p_prior[1338] 0.98 1.00 9962 1.00
p_prior[1339] 0.98 1.00 9927 1.00
p_predicted[1] 0.26 0.37 9730 1.00
p_predicted[2] 0.26 0.36 9529 1.00
p_predicted[3] 0.26 0.36 9327 1.00
p_predicted[4] 0.23 0.34 7774 1.00
p_predicted[5] 0.23 0.34 7777 1.00
p_predicted[6] 0.23 0.34 7746 1.00
p_predicted[7] 0.23 0.35 7700 1.00
p_predicted[8] 0.23 0.35 7712 1.00
p_predicted[9] 0.51 0.61 5191 1.00
p_predicted[10] 0.52 0.62 5734 1.00
p_predicted[11] 0.04 0.12 7474 1.00
p_predicted[12] 0.04 0.11 7586 1.00
p_predicted[13] 0.04 0.11 7617 1.00
p_predicted[14] 0.04 0.11 7624 1.00
p_predicted[15] 0.04 0.10 7592 1.00
p_predicted[16] 0.07 0.15 8585 1.00
p_predicted[17] 0.07 0.15 8610 1.00
p_predicted[18] 0.07 0.15 8631 1.00
p_predicted[19] 0.07 0.15 8641 1.00
p_predicted[20] 0.07 0.15 8629 1.00
p_predicted[21] 0.12 0.24 10782 1.00
p_predicted[22] 0.12 0.23 10377 1.00
p_predicted[23] 0.31 0.38 5610 1.00
p_predicted[24] 0.29 0.36 7993 1.00
p_predicted[25] 0.27 0.33 6112 1.00
p_predicted[26] 0.22 0.28 5221 1.00
p_predicted[27] 0.21 0.26 6535 1.00
p_predicted[28] 0.20 0.25 6264 1.00
p_predicted[29] 0.19 0.25 5214 1.00
p_predicted[30] 0.38 0.44 5084 1.00
p_predicted[31] 0.38 0.44 5084 1.00
p_predicted[32] 0.37 0.43 5807 1.00
p_predicted[33] 0.37 0.43 5807 1.00
p_predicted[34] 0.35 0.41 4616 1.00
p_predicted[35] 0.35 0.41 4616 1.00
p_predicted[36] 0.34 0.41 3912 1.00
p_predicted[37] 0.34 0.41 3912 1.00
p_predicted[38] 0.24 0.29 3970 1.00
p_predicted[39] 0.24 0.29 3970 1.00
p_predicted[40] 0.17 0.21 6092 1.00
p_predicted[41] 0.17 0.21 6092 1.00
p_predicted[42] 0.17 0.21 6143 1.00
p_predicted[43] 0.17 0.21 6143 1.00
p_predicted[44] 0.17 0.21 6097 1.00
p_predicted[45] 0.17 0.21 6097 1.00
p_predicted[46] 0.17 0.21 6085 1.00
p_predicted[47] 0.17 0.21 6085 1.00
p_predicted[48] 0.17 0.21 5702 1.00
p_predicted[49] 0.17 0.21 5702 1.00
p_predicted[50] 0.14 0.25 4041 1.00
p_predicted[51] 0.10 0.17 7581 1.00
p_predicted[52] 0.10 0.17 7494 1.00
p_predicted[53] 0.10 0.17 7321 1.00
p_predicted[54] 0.08 0.12 7831 1.00
p_predicted[55] 0.08 0.12 8331 1.00
p_predicted[56] 0.07 0.13 8453 1.00
p_predicted[57] 0.07 0.14 9691 1.00
p_predicted[58] 0.08 0.16 4239 1.00
p_predicted[59] 0.08 0.16 4241 1.00
p_predicted[60] 0.08 0.16 4242 1.00
p_predicted[61] 0.06 0.10 8262 1.00
p_predicted[62] 0.06 0.10 7833 1.00
p_predicted[63] 0.05 0.08 8538 1.00
p_predicted[64] 0.05 0.08 8577 1.00
p_predicted[65] 0.05 0.08 8526 1.00
p_predicted[66] 0.04 0.07 8876 1.00
p_predicted[67] 0.04 0.07 9275 1.00
p_predicted[68] 0.04 0.08 9528 1.00
p_predicted[69] 0.04 0.11 10028 1.00
p_predicted[70] 0.04 0.09 10384 1.00
p_predicted[71] 0.20 0.28 6694 1.00
p_predicted[72] 0.19 0.27 7592 1.00
p_predicted[73] 0.19 0.26 7705 1.00
p_predicted[74] 0.18 0.26 7643 1.00
p_predicted[75] 0.15 0.23 3168 1.00
p_predicted[76] 0.71 0.82 9662 1.00
p_predicted[77] 0.70 0.82 9395 1.00
p_predicted[78] 0.70 0.82 9358 1.00
p_predicted[79] 0.71 0.82 9662 1.00
p_predicted[80] 0.70 0.82 9395 1.00
p_predicted[81] 0.70 0.82 9334 1.00
p_predicted[82] 0.71 0.82 9650 1.00
p_predicted[83] 0.70 0.82 9412 1.00
p_predicted[84] 0.70 0.82 9346 1.00
p_predicted[85] 0.69 0.78 8708 1.00
p_predicted[86] 0.48 0.56 7143 1.00
p_predicted[87] 0.41 0.50 4458 1.00
p_predicted[88] 0.20 0.25 4094 1.00
p_predicted[89] 0.19 0.24 5144 1.00
p_predicted[90] 0.33 0.52 7379 1.00
p_predicted[91] 0.33 0.52 7474 1.00
p_predicted[92] 0.33 0.52 7557 1.00
p_predicted[93] 0.26 0.45 6797 1.00
p_predicted[94] 0.71 0.84 9969 1.00
p_predicted[95] 0.70 0.82 9406 1.00
p_predicted[96] 0.66 0.79 9600 1.00
p_predicted[97] 0.48 0.66 8758 1.00
p_predicted[98] 0.48 0.65 8970 1.00
p_predicted[99] 0.17 0.26 2146 1.00
p_predicted[100] 0.17 0.26 2148 1.00
p_predicted[101] 0.20 0.27 5172 1.00
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p_predicted[105] 0.00 0.02 10169 1.00
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p_predicted[110] 0.00 0.02 10145 1.00
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p_predicted[113] 0.00 0.02 9745 1.00
p_predicted[114] 0.00 0.02 9750 1.00
p_predicted[115] 0.00 0.02 9751 1.00
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p_predicted[125] 0.01 0.03 8336 1.00
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p_predicted[130] 0.13 0.22 8979 1.00
p_predicted[131] 0.00 0.06 10375 1.00
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p_predicted[137] 0.29 0.37 6013 1.00
p_predicted[138] 0.29 0.37 6130 1.00
p_predicted[139] 0.29 0.37 5797 1.00
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p_predicted[142] 0.20 0.25 6535 1.00
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p_predicted[144] 0.19 0.24 7387 1.00
p_predicted[145] 0.19 0.25 7319 1.00
p_predicted[146] 0.19 0.24 7394 1.00
p_predicted[147] 0.19 0.24 7370 1.00
p_predicted[148] 0.17 0.23 5668 1.00
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p_predicted[150] 0.11 0.19 2623 1.00
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p_predicted[152] 0.14 0.21 3455 1.00
p_predicted[153] 0.09 0.14 3573 1.00
p_predicted[154] 0.09 0.14 3305 1.00
p_predicted[155] 0.11 0.17 3779 1.00
p_predicted[156] 0.11 0.17 3671 1.00
p_predicted[157] 0.09 0.14 3576 1.00
p_predicted[158] 0.11 0.16 3542 1.00
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p_predicted[160] 0.06 0.09 4702 1.00
p_predicted[161] 0.06 0.09 4007 1.00
p_predicted[162] 0.06 0.09 3617 1.00
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p_predicted[164] 0.19 0.26 7232 1.00
p_predicted[165] 0.12 0.17 10260 1.00
p_predicted[166] 0.12 0.17 10260 1.00
p_predicted[167] 0.11 0.16 9965 1.00
p_predicted[168] 0.11 0.16 9965 1.00
p_predicted[169] 0.09 0.14 10201 1.00
p_predicted[170] 0.09 0.14 10201 1.00
p_predicted[171] 0.40 0.46 5623 1.00
p_predicted[172] 0.37 0.43 6875 1.00
p_predicted[173] 0.29 0.34 4790 1.00
p_predicted[174] 0.15 0.21 2326 1.00
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p_predicted[176] 0.09 0.16 4230 1.00
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p_predicted[178] 0.06 0.10 7557 1.00
p_predicted[179] 0.06 0.10 8203 1.00
p_predicted[180] 0.06 0.10 7822 1.00
p_predicted[181] 0.06 0.10 7657 1.00
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p_predicted[184] 0.04 0.07 9027 1.00
p_predicted[185] 0.04 0.07 9300 1.00
p_predicted[186] 0.04 0.07 9360 1.00
p_predicted[187] 0.16 0.28 3482 1.00
p_predicted[188] 0.16 0.28 3476 1.00
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p_predicted[190] 0.12 0.17 7667 1.00
p_predicted[191] 0.12 0.17 7403 1.00
p_predicted[192] 0.12 0.17 7754 1.00
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p_predicted[195] 0.09 0.13 7563 1.00
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p_predicted[199] 0.16 0.28 3475 1.00
p_predicted[200] 0.12 0.18 8265 1.00
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p_predicted[211] 0.15 0.21 4498 1.00
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p_predicted[215] 0.09 0.12 5930 1.00
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p_predicted[217] 0.66 0.78 9799 1.00
p_predicted[218] 0.65 0.77 9409 1.00
p_predicted[219] 0.47 0.64 8893 1.00
p_predicted[220] 0.46 0.64 9072 1.00
p_predicted[221] 0.66 0.78 9813 1.00
p_predicted[222] 0.65 0.77 9235 1.00
p_predicted[223] 0.47 0.64 8927 1.00
p_predicted[224] 0.46 0.64 9092 1.00
p_predicted[225] 0.46 0.64 9106 1.00
p_predicted[226] 0.20 0.30 8822 1.00
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p_predicted[228] 0.21 0.31 9408 1.00
p_predicted[229] 0.21 0.32 9322 1.00
p_predicted[230] 0.21 0.32 9376 1.00
p_predicted[231] 0.11 0.20 3957 1.00
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p_predicted[234] 0.05 0.08 9549 1.00
p_predicted[235] 0.05 0.08 9610 1.00
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p_predicted[237] 0.01 0.13 10269 1.00
p_predicted[238] 0.01 0.13 10344 1.00
p_predicted[239] 0.01 0.13 10344 1.00
p_predicted[240] 0.51 0.61 1917 1.00
p_predicted[241] 0.51 0.61 1917 1.00
p_predicted[242] 0.51 0.61 1917 1.00
p_predicted[243] 0.51 0.61 1914 1.00
p_predicted[244] 0.51 0.61 1914 1.00
p_predicted[245] 0.51 0.61 1914 1.00
p_predicted[246] 0.55 0.62 7526 1.00
p_predicted[247] 0.55 0.62 7526 1.00
p_predicted[248] 0.55 0.62 7526 1.00
p_predicted[249] 0.54 0.62 7703 1.00
p_predicted[250] 0.54 0.62 7703 1.00
p_predicted[251] 0.54 0.62 7703 1.00
p_predicted[252] 0.54 0.61 7267 1.00
p_predicted[253] 0.54 0.61 7267 1.00
p_predicted[254] 0.54 0.61 7267 1.00
p_predicted[255] 0.53 0.61 6796 1.00
p_predicted[256] 0.53 0.61 6796 1.00
p_predicted[257] 0.53 0.61 6796 1.00
p_predicted[258] 0.47 0.55 3820 1.00
p_predicted[259] 0.47 0.55 3820 1.00
p_predicted[260] 0.47 0.55 3820 1.00
p_predicted[261] 0.44 0.52 4835 1.00
p_predicted[262] 0.44 0.52 4835 1.00
p_predicted[263] 0.44 0.52 4835 1.00
p_predicted[264] 0.43 0.52 4734 1.00
p_predicted[265] 0.43 0.52 4734 1.00
p_predicted[266] 0.43 0.52 4734 1.00
p_predicted[267] 0.24 0.29 5958 1.00
p_predicted[268] 0.24 0.29 5958 1.00
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p_predicted[270] 0.24 0.29 5750 1.00
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p_predicted[273] 0.02 0.05 8946 1.00
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p_predicted[321] 0.11 0.17 3845 1.00
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p_predicted[328] 0.06 0.10 3448 1.00
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p_predicted[344] 0.55 0.62 7494 1.00
p_predicted[345] 0.55 0.62 7494 1.00
p_predicted[346] 0.46 0.53 5244 1.00
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p_predicted[348] 0.36 0.44 3075 1.00
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p_predicted[350] 0.25 0.40 8918 1.00
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p_predicted[352] 0.30 0.44 11331 1.00
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p_predicted[354] 0.25 0.34 7557 1.00
p_predicted[355] 0.26 0.37 10040 1.00
p_predicted[356] 0.23 0.33 6953 1.00
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p_predicted[358] 0.17 0.25 7658 1.00
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p_predicted[364] 0.39 0.47 6719 1.00
p_predicted[365] 0.39 0.47 6719 1.00
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p_predicted[367] 0.21 0.28 6218 1.00
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p_predicted[370] 0.21 0.27 6564 1.00
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p_predicted[373] 0.15 0.19 7998 1.00
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p_predicted[406] 0.05 0.11 9244 1.00
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p_predicted[410] 0.43 0.54 2253 1.00
p_predicted[411] 0.33 0.40 4588 1.00
p_predicted[412] 0.33 0.39 4703 1.00
p_predicted[413] 0.32 0.39 4616 1.00
p_predicted[414] 0.33 0.39 4704 1.00
p_predicted[415] 0.33 0.40 4684 1.00
p_predicted[416] 0.28 0.36 2579 1.00
p_predicted[417] 0.31 0.38 4465 1.00
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p_predicted[419] 0.30 0.37 3973 1.00
p_predicted[420] 0.26 0.32 4322 1.00
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p_predicted[422] 0.18 0.23 4428 1.00
p_predicted[423] 0.25 0.31 3381 1.00
p_predicted[424] 0.23 0.29 3992 1.00
p_predicted[425] 0.22 0.27 4129 1.00
p_predicted[426] 0.09 0.12 4753 1.00
p_predicted[427] 0.08 0.11 5793 1.00
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p_predicted[429] 0.38 0.48 5921 1.00
p_predicted[430] 0.34 0.44 6411 1.00
p_predicted[431] 0.27 0.36 5154 1.00
p_predicted[432] 0.27 0.36 5239 1.00
p_predicted[433] 0.30 0.40 3692 1.00
p_predicted[434] 0.21 0.30 2444 1.00
p_predicted[435] 0.19 0.29 2116 1.00
p_predicted[436] 0.19 0.29 2094 1.00
p_predicted[437] 0.18 0.29 1947 1.00
p_predicted[438] 0.25 0.35 1991 1.00
p_predicted[439] 0.29 0.37 5643 1.00
p_predicted[440] 0.28 0.35 7642 1.00
p_predicted[441] 0.28 0.35 7176 1.00
p_predicted[442] 0.27 0.34 7868 1.00
p_predicted[443] 0.25 0.32 7361 1.00
p_predicted[444] 0.26 0.33 8254 1.00
p_predicted[445] 0.22 0.28 4085 1.00
p_predicted[446] 0.21 0.27 5052 1.00
p_predicted[447] 0.25 0.32 6510 1.00
p_predicted[448] 0.20 0.26 5849 1.00
p_predicted[449] 0.20 0.26 6307 1.00
p_predicted[450] 0.19 0.25 7241 1.00
p_predicted[451] 0.19 0.24 7440 1.00
p_predicted[452] 0.19 0.29 1939 1.00
p_predicted[453] 0.15 0.22 2627 1.00
p_predicted[454] 0.15 0.22 2574 1.00
p_predicted[455] 0.14 0.22 2286 1.00
p_predicted[456] 0.25 0.35 1982 1.00
p_predicted[457] 0.29 0.37 5584 1.00
p_predicted[458] 0.27 0.34 8246 1.00
p_predicted[459] 0.25 0.32 6883 1.00
p_predicted[460] 0.24 0.31 5871 1.00
p_predicted[461] 0.24 0.31 5001 1.00
p_predicted[462] 0.19 0.25 7211 1.00
p_predicted[463] 0.19 0.25 7276 1.00
p_predicted[464] 0.19 0.24 7431 1.00
p_predicted[465] 0.24 0.32 1725 1.00
p_predicted[466] 0.27 0.33 7048 1.00
p_predicted[467] 0.25 0.31 5772 1.00
p_predicted[468] 0.26 0.31 7063 1.00
p_predicted[469] 0.20 0.25 4541 1.00
p_predicted[470] 0.21 0.26 3692 1.00
p_predicted[471] 0.20 0.25 4143 1.00
p_predicted[472] 0.20 0.25 4694 1.00
p_predicted[473] 0.11 0.15 6514 1.00
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p_predicted[475] 0.09 0.14 2585 1.00
p_predicted[476] 0.09 0.14 2279 1.00
p_predicted[477] 0.24 0.33 1868 1.00
p_predicted[478] 0.27 0.32 4499 1.00
p_predicted[479] 0.27 0.33 4238 1.00
p_predicted[480] 0.19 0.23 5313 1.00
p_predicted[481] 0.18 0.22 6654 1.00
p_predicted[482] 0.18 0.21 6970 1.00
p_predicted[483] 0.18 0.22 6931 1.00
p_predicted[484] 0.18 0.21 6941 1.00
p_predicted[485] 0.17 0.21 6682 1.00
p_predicted[486] 0.17 0.21 6136 1.00
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p_predicted[488] 0.08 0.13 9608 1.00
p_predicted[489] 0.08 0.13 9627 1.00
p_predicted[490] 0.08 0.13 9627 1.00
p_predicted[491] 0.08 0.13 8319 1.00
p_predicted[492] 0.08 0.13 8319 1.00
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p_predicted[494] 0.06 0.10 8727 1.00
p_predicted[495] 0.06 0.11 8631 1.00
p_predicted[496] 0.06 0.11 8631 1.00
p_predicted[497] 0.24 0.32 1714 1.00
p_predicted[498] 0.28 0.34 6468 1.00
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p_predicted[500] 0.20 0.25 4360 1.00
p_predicted[501] 0.20 0.25 4713 1.00
p_predicted[502] 0.20 0.24 4855 1.00
p_predicted[503] 0.19 0.24 5160 1.00
p_predicted[504] 0.11 0.15 7577 1.00
p_predicted[505] 0.11 0.15 5699 1.00
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p_predicted[507] 0.09 0.14 2463 1.00
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p_predicted[512] 0.00 0.06 9124 1.00
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p_predicted[516] 0.00 0.06 9680 1.00
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p_predicted[519] 0.19 0.29 2114 1.00
p_predicted[520] 0.18 0.29 1963 1.00
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p_predicted[522] 0.59 0.66 8026 1.00
p_predicted[523] 0.59 0.66 8026 1.00
p_predicted[524] 0.57 0.65 6594 1.00
p_predicted[525] 0.57 0.65 6594 1.00
p_predicted[526] 0.57 0.65 6594 1.00
p_predicted[527] 0.36 0.42 5620 1.00
p_predicted[528] 0.36 0.42 5620 1.00
p_predicted[529] 0.36 0.42 5620 1.00
p_predicted[530] 0.36 0.41 5123 1.00
p_predicted[531] 0.36 0.41 5123 1.00
p_predicted[532] 0.36 0.41 5123 1.00
p_predicted[533] 0.30 0.35 3648 1.00
p_predicted[534] 0.30 0.35 3648 1.00
p_predicted[535] 0.30 0.35 3648 1.00
p_predicted[536] 0.48 0.56 7382 1.00
p_predicted[537] 0.48 0.56 7382 1.00
p_predicted[538] 0.48 0.56 7382 1.00
p_predicted[539] 0.46 0.54 6864 1.00
p_predicted[540] 0.46 0.54 6864 1.00
p_predicted[541] 0.46 0.54 6864 1.00
p_predicted[542] 0.27 0.33 4054 1.00
p_predicted[543] 0.27 0.33 4054 1.00
p_predicted[544] 0.27 0.33 4054 1.00
p_predicted[545] 0.28 0.34 3648 1.00
p_predicted[546] 0.28 0.34 3648 1.00
p_predicted[547] 0.28 0.34 3648 1.00
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p_predicted[550] 0.02 0.05 8970 1.00
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p_predicted[562] 0.03 0.06 7699 1.00
p_predicted[563] 0.03 0.06 7726 1.00
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p_predicted[567] 0.00 0.04 9223 1.00
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p_predicted[569] 0.00 0.04 9081 1.00
p_predicted[570] 0.00 0.04 9080 1.00
p_predicted[571] 0.00 0.04 8999 1.00
p_predicted[572] 0.18 0.29 11162 1.00
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p_predicted[574] 0.10 0.17 9167 1.00
p_predicted[575] 0.11 0.22 8397 1.00
p_predicted[576] 0.11 0.22 8403 1.00
p_predicted[577] 0.08 0.17 9928 1.00
p_predicted[578] 0.25 0.35 2006 1.00
p_predicted[579] 0.29 0.37 5693 1.00
p_predicted[580] 0.24 0.31 5844 1.00
p_predicted[581] 0.20 0.26 6467 1.00
p_predicted[582] 0.18 0.24 7417 1.00
p_predicted[583] 0.15 0.26 8962 1.00
p_predicted[584] 0.14 0.26 7710 1.00
p_predicted[585] 0.18 0.30 9597 1.00
p_predicted[586] 0.18 0.29 10858 1.00
p_predicted[587] 0.19 0.30 10053 1.00
p_predicted[588] 0.18 0.29 11091 1.00
p_predicted[589] 0.18 0.30 9851 1.00
p_predicted[590] 0.18 0.29 11001 1.00
p_predicted[591] 0.13 0.24 8176 1.00
p_predicted[592] 0.13 0.23 7372 1.00
p_predicted[593] 0.41 0.51 1512 1.00
p_predicted[594] 0.45 0.52 5321 1.00
p_predicted[595] 0.44 0.51 5042 1.00
p_predicted[596] 0.44 0.51 4563 1.00
p_predicted[597] 0.36 0.43 3181 1.00
p_predicted[598] 0.37 0.45 2827 1.00
p_predicted[599] 0.18 0.23 5369 1.00
p_predicted[600] 0.18 0.23 4982 1.00
p_predicted[601] 0.16 0.22 3369 1.00
p_predicted[602] 0.15 0.21 2170 1.00
p_predicted[603] 0.86 0.96 8683 1.00
p_predicted[604] 0.86 0.96 8683 1.00
p_predicted[605] 0.85 0.95 7358 1.00
p_predicted[606] 0.85 0.95 7358 1.00
p_predicted[607] 0.85 0.95 7344 1.00
p_predicted[608] 0.85 0.95 7344 1.00
p_predicted[609] 0.85 0.95 7640 1.00
p_predicted[610] 0.85 0.95 7640 1.00
p_predicted[611] 0.88 0.96 7963 1.00
p_predicted[612] 0.88 0.96 7963 1.00
p_predicted[613] 0.25 0.30 2832 1.00
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p_predicted[615] 0.24 0.29 2733 1.00
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p_predicted[619] 0.11 0.20 9871 1.00
p_predicted[620] 0.10 0.19 9916 1.00
p_predicted[621] 0.08 0.15 9872 1.00
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p_predicted[623] 0.07 0.13 8003 1.00
p_predicted[624] 0.07 0.13 7931 1.00
p_predicted[625] 0.05 0.09 9282 1.00
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p_predicted[627] 0.45 0.53 6323 1.00
p_predicted[628] 0.38 0.46 3290 1.00
p_predicted[629] 0.37 0.45 3585 1.00
p_predicted[630] 0.37 0.45 3737 1.00
p_predicted[631] 0.19 0.25 3195 1.00
p_predicted[632] 0.26 0.40 7205 1.00
p_predicted[633] 0.27 0.45 7456 1.00
p_predicted[634] 0.32 0.44 8371 1.00
p_predicted[635] 0.33 0.49 10003 1.00
p_predicted[636] 0.31 0.43 8120 1.00
p_predicted[637] 0.33 0.49 9796 1.00
p_predicted[638] 0.31 0.43 8154 1.00
p_predicted[639] 0.33 0.49 9828 1.00
p_predicted[640] 0.23 0.34 6911 1.00
p_predicted[641] 0.25 0.40 7978 1.00
p_predicted[642] 0.24 0.35 7429 1.00
p_predicted[643] 0.25 0.40 8355 1.00
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p_predicted[646] 0.33 0.50 10499 1.00
p_predicted[647] 0.33 0.50 10499 1.00
p_predicted[648] 0.32 0.49 10794 1.00
p_predicted[649] 0.32 0.49 10794 1.00
p_predicted[650] 0.25 0.34 10171 1.00
p_predicted[651] 0.25 0.34 10171 1.00
p_predicted[652] 0.09 0.16 9539 1.00
p_predicted[653] 0.09 0.16 9539 1.00
p_predicted[654] 0.08 0.16 9362 1.00
p_predicted[655] 0.08 0.16 9362 1.00
p_predicted[656] 0.04 0.07 7830 1.00
p_predicted[657] 0.04 0.07 7830 1.00
p_predicted[658] 0.04 0.07 7879 1.00
p_predicted[659] 0.04 0.07 7879 1.00
p_predicted[660] 0.04 0.07 8165 1.00
p_predicted[661] 0.04 0.07 8165 1.00
p_predicted[662] 0.04 0.07 8352 1.00
p_predicted[663] 0.04 0.07 8352 1.00
p_predicted[664] 0.11 0.24 10385 1.00
p_predicted[665] 0.11 0.24 10386 1.00
p_predicted[666] 0.11 0.23 10762 1.00
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p_predicted[669] 0.08 0.17 9992 1.00
p_predicted[670] 0.08 0.17 9857 1.00
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p_predicted[674] 0.06 0.13 10764 1.00
p_predicted[675] 0.06 0.13 10765 1.00
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p_predicted[680] 0.06 0.13 10792 1.00
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p_predicted[683] 0.06 0.13 10782 1.00
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p_predicted[685] 0.06 0.11 7542 1.00
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p_predicted[687] 0.06 0.11 7542 1.00
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p_predicted[691] 0.09 0.14 7629 1.00
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p_predicted[702] 0.07 0.11 7694 1.00
p_predicted[703] 0.07 0.11 7410 1.00
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p_predicted[705] 0.07 0.11 7410 1.00
p_predicted[706] 0.55 0.62 7475 1.00
p_predicted[707] 0.47 0.55 3435 1.00
p_predicted[708] 0.24 0.32 1727 1.00
p_predicted[709] 0.28 0.34 6552 1.00
p_predicted[710] 0.28 0.34 6535 1.00
p_predicted[711] 0.27 0.33 7390 1.00
p_predicted[712] 0.26 0.32 7511 1.00
p_predicted[713] 0.21 0.27 3172 1.00
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p_predicted[720] 0.00 0.02 10115 1.00
p_predicted[721] 0.00 0.02 10105 1.00
p_predicted[722] 0.00 0.02 10105 1.00
p_predicted[723] 0.00 0.02 10094 1.00
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p_predicted[725] 0.00 0.02 10319 1.00
p_predicted[726] 0.39 0.56 9705 1.00
p_predicted[727] 0.39 0.55 9558 1.00
p_predicted[728] 0.30 0.47 7432 1.00
p_predicted[729] 0.61 0.76 9196 1.00
p_predicted[730] 0.61 0.76 9148 1.00
p_predicted[731] 0.59 0.74 8666 1.00
p_predicted[732] 0.50 0.67 8571 1.00
p_predicted[733] 0.50 0.67 8610 1.00
p_predicted[734] 0.50 0.68 8839 1.00
p_predicted[735] 0.50 0.68 8990 1.00
p_predicted[736] 0.50 0.68 9121 1.00
p_predicted[737] 0.00 0.01 9511 1.00
p_predicted[738] 0.00 0.01 9511 1.00
p_predicted[739] 0.00 0.01 9168 1.00
p_predicted[740] 0.00 0.01 9179 1.00
p_predicted[741] 0.00 0.01 9317 1.00
p_predicted[742] 0.00 0.01 9118 1.00
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p_predicted[744] 0.00 0.01 8470 1.00
p_predicted[745] 0.00 0.01 8575 1.00
p_predicted[746] 0.00 0.01 8559 1.00
p_predicted[747] 0.00 0.01 8808 1.00
p_predicted[748] 0.01 0.18 9733 1.00
p_predicted[749] 0.01 0.18 9681 1.00
p_predicted[750] 0.01 0.17 9692 1.00
p_predicted[751] 0.01 0.18 9678 1.00
p_predicted[752] 0.01 0.18 9682 1.00
p_predicted[753] 0.01 0.20 9786 1.00
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p_predicted[755] 0.00 0.01 8877 1.00
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p_predicted[757] 0.00 0.01 8854 1.00
p_predicted[758] 0.00 0.01 8910 1.00
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p_predicted[761] 0.00 0.01 8869 1.00
p_predicted[762] 0.00 0.01 8879 1.00
p_predicted[763] 0.00 0.01 8920 1.00
p_predicted[764] 0.00 0.01 8931 1.00
p_predicted[765] 0.00 0.01 9232 1.00
p_predicted[766] 0.00 0.01 8876 1.00
p_predicted[767] 0.00 0.01 8867 1.00
p_predicted[768] 0.00 0.01 8974 1.00
p_predicted[769] 0.00 0.01 8914 1.00
p_predicted[770] 0.00 0.01 8866 1.00
p_predicted[771] 0.00 0.01 8880 1.00
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p_predicted[773] 0.00 0.01 8895 1.00
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p_predicted[779] 0.00 0.01 8870 1.00
p_predicted[780] 0.00 0.01 8904 1.00
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p_predicted[782] 0.00 0.01 8918 1.00
p_predicted[783] 0.00 0.01 9214 1.00
p_predicted[784] 0.00 0.01 9241 1.00
p_predicted[785] 0.00 0.01 9301 1.00
p_predicted[786] 0.00 0.01 9308 1.00
p_predicted[787] 0.34 0.39 3998 1.00
p_predicted[788] 0.33 0.38 3928 1.00
p_predicted[789] 0.33 0.38 3881 1.00
p_predicted[790] 0.34 0.39 3997 1.00
p_predicted[791] 0.29 0.34 1858 1.00
p_predicted[792] 0.28 0.33 2021 1.00
p_predicted[793] 0.28 0.33 2017 1.00
p_predicted[794] 0.28 0.33 2066 1.00
p_predicted[795] 0.00 0.04 9788 1.00
p_predicted[796] 0.00 0.03 9832 1.00
p_predicted[797] 0.00 0.03 9842 1.00
p_predicted[798] 0.00 0.03 9845 1.00
p_predicted[799] 0.00 0.03 9871 1.00
p_predicted[800] 0.00 0.03 9896 1.00
p_predicted[801] 0.00 0.03 10329 1.00
p_predicted[802] 0.00 0.03 10340 1.00
p_predicted[803] 0.02 0.05 8964 1.00
p_predicted[804] 0.02 0.04 8785 1.00
p_predicted[805] 0.02 0.05 8951 1.00
p_predicted[806] 0.02 0.04 8777 1.00
p_predicted[807] 0.03 0.06 7685 1.00
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p_predicted[809] 0.03 0.06 7988 1.00
p_predicted[810] 0.03 0.06 7700 1.00
p_predicted[811] 0.03 0.06 7703 1.00
p_predicted[812] 0.03 0.06 7763 1.00
p_predicted[813] 0.03 0.06 7774 1.00
p_predicted[814] 0.08 0.17 10627 1.00
p_predicted[815] 0.08 0.16 10230 1.00
p_predicted[816] 0.08 0.16 10248 1.00
p_predicted[817] 0.08 0.15 10280 1.00
p_predicted[818] 0.06 0.12 10622 1.00
p_predicted[819] 0.07 0.14 10024 1.00
p_predicted[820] 0.06 0.12 10348 1.00
p_predicted[821] 0.06 0.11 10751 1.00
p_predicted[822] 0.06 0.12 10643 1.00
p_predicted[823] 0.06 0.11 10891 1.00
p_predicted[824] 0.00 0.02 9825 1.00
p_predicted[825] 0.00 0.02 9467 1.00
p_predicted[826] 0.08 0.17 10394 1.00
p_predicted[827] 0.10 0.20 10984 1.00
p_predicted[828] 0.06 0.14 10180 1.00
p_predicted[829] 0.06 0.14 10143 1.00
p_predicted[830] 0.06 0.14 10159 1.00
p_predicted[831] 0.06 0.14 10333 1.00
p_predicted[832] 0.18 0.26 2395 1.00
p_predicted[833] 0.22 0.33 2514 1.00
p_predicted[834] 0.21 0.27 3843 1.00
p_predicted[835] 0.26 0.35 9279 1.00
p_predicted[836] 0.21 0.27 3727 1.00
p_predicted[837] 0.26 0.35 9014 1.00
p_predicted[838] 0.21 0.27 3809 1.00
p_predicted[839] 0.26 0.35 9208 1.00
p_predicted[840] 0.12 0.17 4001 1.00
p_predicted[841] 0.15 0.22 7443 1.00
p_predicted[842] 0.12 0.17 3997 1.00
p_predicted[843] 0.15 0.22 7525 1.00
p_predicted[844] 0.11 0.16 3894 1.00
p_predicted[845] 0.15 0.22 6558 1.00
p_predicted[846] 0.09 0.12 4407 1.00
p_predicted[847] 0.11 0.17 9849 1.00
p_predicted[848] 0.07 0.10 4824 1.00
p_predicted[849] 0.09 0.14 6989 1.00
p_predicted[850] 0.07 0.10 3807 1.00
p_predicted[851] 0.09 0.14 5201 1.00
p_predicted[852] 0.05 0.08 2650 1.00
p_predicted[853] 0.07 0.11 3331 1.00
p_predicted[854] 0.20 0.26 5128 1.00
p_predicted[855] 0.20 0.25 5950 1.00
p_predicted[856] 0.19 0.24 6515 1.00
p_predicted[857] 0.19 0.24 6532 1.00
p_predicted[858] 0.18 0.23 5870 1.00
p_predicted[859] 0.17 0.23 5199 1.00
p_predicted[860] 0.14 0.18 4400 1.00
p_predicted[861] 0.13 0.17 5956 1.00
p_predicted[862] 0.12 0.16 5979 1.00
p_predicted[863] 0.13 0.17 6075 1.00
p_predicted[864] 0.13 0.17 5981 1.00
p_predicted[865] 0.13 0.17 5696 1.00
p_predicted[866] 0.00 0.04 9218 1.00
p_predicted[867] 0.00 0.04 9109 1.00
p_predicted[868] 0.00 0.04 9078 1.00
p_predicted[869] 0.00 0.04 9081 1.00
p_predicted[870] 0.00 0.04 9016 1.00
p_predicted[871] 0.00 0.04 8982 1.00
p_predicted[872] 0.29 0.41 7662 1.00
p_predicted[873] 0.21 0.30 6801 1.00
p_predicted[874] 0.17 0.25 5888 1.00
p_predicted[875] 0.17 0.25 5263 1.00
p_predicted[876] 0.06 0.13 5326 1.00
p_predicted[877] 0.04 0.09 7259 1.00
p_predicted[878] 0.03 0.07 7601 1.00
p_predicted[879] 0.03 0.06 7703 1.00
p_predicted[880] 0.03 0.06 7943 1.00
p_predicted[881] 0.00 0.02 9710 1.00
p_predicted[882] 0.00 0.02 9823 1.00
p_predicted[883] 0.00 0.02 9819 1.00
p_predicted[884] 0.00 0.02 9836 1.00
p_predicted[885] 0.00 0.02 9841 1.00
p_predicted[886] 0.00 0.02 9718 1.00
p_predicted[887] 0.00 0.02 9318 1.00
p_predicted[888] 0.00 0.02 9319 1.00
p_predicted[889] 0.00 0.02 9315 1.00
p_predicted[890] 0.00 0.02 9312 1.00
p_predicted[891] 0.00 0.02 9313 1.00
p_predicted[892] 0.00 0.02 9378 1.00
p_predicted[893] 0.00 0.01 8470 1.00
p_predicted[894] 0.00 0.01 8539 1.00
p_predicted[895] 0.00 0.01 8757 1.00
p_predicted[896] 0.00 0.01 8851 1.00
p_predicted[897] 0.24 0.32 1727 1.00
p_predicted[898] 0.27 0.34 6870 1.00
p_predicted[899] 0.13 0.17 5991 1.00
p_predicted[900] 0.12 0.16 8563 1.00
p_predicted[901] 0.11 0.15 8109 1.00
p_predicted[902] 0.10 0.14 4816 1.00
p_predicted[903] 0.27 0.34 6450 1.00
p_predicted[904] 0.25 0.31 5946 1.00
p_predicted[905] 0.20 0.25 4707 1.00
p_predicted[906] 0.18 0.23 6105 1.00
p_predicted[907] 0.18 0.23 6081 1.00
p_predicted[908] 0.03 0.08 8401 1.00
p_predicted[909] 0.02 0.06 8773 1.00
p_predicted[910] 0.03 0.08 8401 1.00
p_predicted[911] 0.04 0.08 7542 1.00
p_predicted[912] 0.03 0.06 7922 1.00
p_predicted[913] 0.04 0.08 7542 1.00
p_predicted[914] 0.03 0.06 7572 1.00
p_predicted[915] 0.02 0.05 8537 1.00
p_predicted[916] 0.03 0.06 7572 1.00
p_predicted[917] 0.03 0.07 7329 1.00
p_predicted[918] 0.02 0.05 8253 1.00
p_predicted[919] 0.03 0.07 7329 1.00
p_predicted[920] 0.32 0.43 2026 1.00
p_predicted[921] 0.34 0.43 8393 1.00
p_predicted[922] 0.34 0.43 8440 1.00
p_predicted[923] 0.34 0.44 8228 1.00
p_predicted[924] 0.14 0.22 2432 1.00
p_predicted[925] 0.16 0.22 3080 1.00
p_predicted[926] 0.09 0.13 3401 1.00
p_predicted[927] 0.06 0.10 2761 1.00
p_predicted[928] 0.00 0.01 8873 1.00
p_predicted[929] 0.00 0.01 8875 1.00
p_predicted[930] 0.00 0.01 9004 1.00
p_predicted[931] 0.00 0.01 8873 1.00
p_predicted[932] 0.00 0.01 8867 1.00
p_predicted[933] 0.00 0.01 8851 1.00
p_predicted[934] 0.00 0.01 8851 1.00
p_predicted[935] 0.00 0.01 8912 1.00
p_predicted[936] 0.00 0.01 8874 1.00
p_predicted[937] 0.00 0.01 8883 1.00
p_predicted[938] 0.00 0.01 8940 1.00
p_predicted[939] 0.00 0.01 8980 1.00
p_predicted[940] 0.60 0.68 7028 1.00
p_predicted[941] 0.59 0.67 6966 1.00
p_predicted[942] 0.52 0.61 4122 1.00
p_predicted[943] 0.28 0.34 5766 1.00
p_predicted[944] 0.28 0.34 5760 1.00
p_predicted[945] 0.27 0.34 5579 1.00
p_predicted[946] 0.28 0.38 10240 1.00
p_predicted[947] 0.28 0.38 10240 1.00
p_predicted[948] 0.28 0.38 10240 1.00
p_predicted[949] 0.22 0.29 9498 1.00
p_predicted[950] 0.22 0.29 9498 1.00
p_predicted[951] 0.22 0.29 9498 1.00
p_predicted[952] 0.22 0.29 9533 1.00
p_predicted[953] 0.22 0.29 9533 1.00
p_predicted[954] 0.22 0.29 9533 1.00
p_predicted[955] 0.21 0.29 10010 1.00
p_predicted[956] 0.21 0.29 10010 1.00
p_predicted[957] 0.21 0.29 10010 1.00
p_predicted[958] 0.22 0.30 9427 1.00
p_predicted[959] 0.22 0.30 9427 1.00
p_predicted[960] 0.22 0.30 9427 1.00
p_predicted[961] 0.21 0.29 9477 1.00
p_predicted[962] 0.21 0.29 9477 1.00
p_predicted[963] 0.21 0.29 9477 1.00
p_predicted[964] 0.21 0.29 9494 1.00
p_predicted[965] 0.21 0.29 9494 1.00
p_predicted[966] 0.21 0.29 9494 1.00
p_predicted[967] 0.20 0.28 9645 1.00
p_predicted[968] 0.20 0.28 9645 1.00
p_predicted[969] 0.20 0.28 9645 1.00
p_predicted[970] 0.20 0.28 9818 1.00
p_predicted[971] 0.20 0.28 9818 1.00
p_predicted[972] 0.20 0.28 9818 1.00
p_predicted[973] 0.00 0.01 8995 1.00
p_predicted[974] 0.00 0.01 8873 1.00
p_predicted[975] 0.00 0.01 8872 1.00
p_predicted[976] 0.00 0.01 8871 1.00
p_predicted[977] 0.00 0.01 9003 1.00
p_predicted[978] 0.00 0.01 8870 1.00
p_predicted[979] 0.00 0.01 8858 1.00
p_predicted[980] 0.00 0.01 8851 1.00
p_predicted[981] 0.00 0.01 8970 1.00
p_predicted[982] 0.00 0.01 8975 1.00
p_predicted[983] 0.00 0.01 8912 1.00
p_predicted[984] 0.00 0.01 8874 1.00
p_predicted[985] 0.00 0.01 8890 1.00
p_predicted[986] 0.00 0.01 8937 1.00
p_predicted[987] 0.00 0.01 8980 1.00
p_predicted[988] 0.00 0.01 9288 1.00
p_predicted[989] 0.00 0.03 9105 1.00
p_predicted[990] 0.00 0.03 9463 1.00
p_predicted[991] 0.00 0.03 9450 1.00
p_predicted[992] 0.98 1.00 11134 1.00
p_predicted[993] 0.98 1.00 8951 1.00
p_predicted[994] 0.99 1.00 10261 1.00
p_predicted[995] 0.00 0.01 8815 1.00
p_predicted[996] 0.00 0.01 8849 1.00
p_predicted[997] 0.00 0.01 9223 1.00
p_predicted[998] 0.00 0.01 9249 1.00
p_predicted[999] 0.00 0.01 9304 1.00
p_predicted[1000] 0.15 0.28 7872 1.00
p_predicted[1001] 0.17 0.29 7856 1.00
p_predicted[1002] 0.17 0.29 7843 1.00
p_predicted[1003] 0.13 0.24 9423 1.00
p_predicted[1004] 0.14 0.26 7898 1.00
p_predicted[1005] 0.26 0.43 8204 1.00
p_predicted[1006] 0.29 0.43 7446 1.00
p_predicted[1007] 0.29 0.46 6353 1.00
p_predicted[1008] 0.08 0.16 10191 1.00
p_predicted[1009] 0.06 0.12 10653 1.00
p_predicted[1010] 0.06 0.11 10892 1.00
p_predicted[1011] 0.08 0.17 10560 1.00
p_predicted[1012] 0.08 0.16 10280 1.00
p_predicted[1013] 0.06 0.11 10826 1.00
p_predicted[1014] 0.06 0.11 10874 1.00
p_predicted[1015] 0.06 0.11 10890 1.00
p_predicted[1016] 0.05 0.12 10831 1.00
p_predicted[1017] 0.05 0.10 10622 1.00
p_predicted[1018] 0.05 0.11 10560 1.00
p_predicted[1019] 0.05 0.11 10578 1.00
p_predicted[1020] 0.05 0.11 10483 1.00
p_predicted[1021] 0.05 0.12 10726 1.00
p_predicted[1022] 0.05 0.12 10822 1.00
p_predicted[1023] 0.08 0.17 10612 1.00
p_predicted[1024] 0.08 0.16 10230 1.00
p_predicted[1025] 0.07 0.14 10111 1.00
p_predicted[1026] 0.06 0.11 10880 1.00
p_predicted[1027] 0.06 0.11 10887 1.00
p_predicted[1028] 0.05 0.10 10681 1.00
p_predicted[1029] 0.05 0.11 10578 1.00
p_predicted[1030] 0.05 0.11 10564 1.00
p_predicted[1031] 0.05 0.11 10569 1.00
p_predicted[1032] 0.05 0.11 10528 1.00
p_predicted[1033] 0.05 0.11 10643 1.00
p_predicted[1034] 0.05 0.12 7410 1.00
p_predicted[1035] 0.06 0.11 7798 1.00
p_predicted[1036] 0.05 0.11 7475 1.00
p_predicted[1037] 0.06 0.11 7836 1.00
p_predicted[1038] 0.05 0.11 7754 1.00
p_predicted[1039] 0.05 0.11 8047 1.00
p_predicted[1040] 0.05 0.11 7738 1.00
p_predicted[1041] 0.05 0.11 8054 1.00
p_predicted[1042] 0.05 0.11 7854 1.00
p_predicted[1043] 0.05 0.11 8056 1.00
p_predicted[1044] 0.05 0.11 7918 1.00
p_predicted[1045] 0.05 0.11 8054 1.00
p_predicted[1046] 0.04 0.08 8448 1.00
p_predicted[1047] 0.04 0.08 8407 1.00
p_predicted[1048] 0.00 0.02 9153 1.00
p_predicted[1049] 0.00 0.02 9153 1.00
p_predicted[1050] 0.00 0.02 9055 1.00
p_predicted[1051] 0.00 0.02 9055 1.00
p_predicted[1052] 0.00 0.02 9000 1.00
p_predicted[1053] 0.00 0.02 9000 1.00
p_predicted[1054] 0.00 0.02 8980 1.00
p_predicted[1055] 0.00 0.02 8980 1.00
p_predicted[1056] 0.00 0.02 9384 1.00
p_predicted[1057] 0.00 0.02 9384 1.00
p_predicted[1058] 0.00 0.02 9138 1.00
p_predicted[1059] 0.00 0.02 9077 1.00
p_predicted[1060] 0.00 0.02 9063 1.00
p_predicted[1061] 0.00 0.02 9004 1.00
p_predicted[1062] 0.00 0.02 8991 1.00
p_predicted[1063] 0.00 0.02 9033 1.00
p_predicted[1064] 0.00 0.02 9004 1.00
p_predicted[1065] 0.00 0.02 9400 1.00
p_predicted[1066] 0.00 0.02 9381 1.00
p_predicted[1067] 0.00 0.06 9864 1.00
p_predicted[1068] 0.00 0.06 10438 1.00
p_predicted[1069] 0.00 0.06 10269 1.00
p_predicted[1070] 0.16 0.23 7325 1.00
p_predicted[1071] 0.16 0.23 7325 1.00
p_predicted[1072] 0.20 0.28 8169 1.00
p_predicted[1073] 0.20 0.28 8169 1.00
p_predicted[1074] 0.16 0.22 7769 1.00
p_predicted[1075] 0.16 0.22 7769 1.00
p_predicted[1076] 0.16 0.22 7980 1.00
p_predicted[1077] 0.16 0.22 7980 1.00
p_predicted[1078] 0.16 0.22 8453 1.00
p_predicted[1079] 0.16 0.22 8453 1.00
p_predicted[1080] 0.16 0.24 2051 1.00
p_predicted[1081] 0.19 0.25 6200 1.00
p_predicted[1082] 0.14 0.18 4147 1.00
p_predicted[1083] 0.13 0.18 5125 1.00
p_predicted[1084] 0.12 0.16 8392 1.00
p_predicted[1085] 0.11 0.15 7176 1.00
p_predicted[1086] 0.11 0.15 6691 1.00
p_predicted[1087] 0.22 0.32 9732 1.00
p_predicted[1088] 0.22 0.32 9632 1.00
p_predicted[1089] 0.22 0.33 9793 1.00
p_predicted[1090] 0.22 0.32 9724 1.00
p_predicted[1091] 0.21 0.31 8948 1.00
p_predicted[1092] 0.22 0.32 9658 1.00
p_predicted[1093] 0.21 0.31 8945 1.00
p_predicted[1094] 0.22 0.32 9562 1.00
p_predicted[1095] 0.21 0.31 9045 1.00
p_predicted[1096] 0.21 0.31 9020 1.00
p_predicted[1097] 0.21 0.31 8865 1.00
p_predicted[1098] 0.20 0.30 8666 1.00
p_predicted[1099] 0.15 0.22 9759 1.00
p_predicted[1100] 0.15 0.22 9816 1.00
p_predicted[1101] 0.14 0.22 9923 1.00
p_predicted[1102] 0.48 0.57 5843 1.00
p_predicted[1103] 0.48 0.57 5904 1.00
p_predicted[1104] 0.46 0.56 6279 1.00
p_predicted[1105] 0.41 0.51 3007 1.00
p_predicted[1106] 0.23 0.32 3068 1.00
p_predicted[1107] 0.27 0.35 7003 1.00
p_predicted[1108] 0.26 0.34 6533 1.00
p_predicted[1109] 0.26 0.34 6456 1.00
p_predicted[1110] 0.20 0.26 5918 1.00
p_predicted[1111] 0.22 0.29 4097 1.00
p_predicted[1112] 0.20 0.26 4870 1.00
p_predicted[1113] 0.20 0.26 5524 1.00
p_predicted[1114] 0.18 0.24 6515 1.00
p_predicted[1115] 0.14 0.18 4015 1.00
p_predicted[1116] 0.14 0.18 4337 1.00
p_predicted[1117] 0.13 0.17 5653 1.00
p_predicted[1118] 0.13 0.17 5913 1.00
p_predicted[1119] 0.13 0.17 5113 1.00
p_predicted[1120] 0.13 0.17 5741 1.00
p_predicted[1121] 0.14 0.18 4903 1.00
p_predicted[1122] 0.13 0.17 6069 1.00
p_predicted[1123] 0.35 0.53 3247 1.00
p_predicted[1124] 0.28 0.38 10290 1.00
p_predicted[1125] 0.27 0.37 10055 1.00
p_predicted[1126] 0.21 0.29 9586 1.00
p_predicted[1127] 0.21 0.29 9906 1.00
p_predicted[1128] 0.09 0.27 9609 1.00
p_predicted[1129] 0.00 0.04 8986 1.00
p_predicted[1130] 0.00 0.04 8695 1.00
p_predicted[1131] 0.18 0.29 9941 1.00
p_predicted[1132] 0.13 0.20 9208 1.00
p_predicted[1133] 0.18 0.29 9941 1.00
p_predicted[1134] 0.15 0.24 6993 1.00
p_predicted[1135] 0.10 0.17 7127 1.00
p_predicted[1136] 0.15 0.24 6993 1.00
p_predicted[1137] 0.19 0.29 10531 1.00
p_predicted[1138] 0.14 0.21 9754 1.00
p_predicted[1139] 0.19 0.29 10531 1.00
p_predicted[1140] 0.15 0.24 6837 1.00
p_predicted[1141] 0.10 0.17 6971 1.00
p_predicted[1142] 0.15 0.24 6837 1.00
p_predicted[1143] 0.08 0.13 7781 1.00
p_predicted[1144] 0.06 0.11 8005 1.00
p_predicted[1145] 0.08 0.13 7781 1.00
p_predicted[1146] 0.07 0.11 7413 1.00
p_predicted[1147] 0.05 0.09 8686 1.00
p_predicted[1148] 0.07 0.11 7413 1.00
p_predicted[1149] 0.07 0.10 7877 1.00
p_predicted[1150] 0.05 0.08 8982 1.00
p_predicted[1151] 0.07 0.10 7877 1.00
p_predicted[1152] 0.09 0.15 5898 1.00
p_predicted[1153] 0.07 0.11 7738 1.00
p_predicted[1154] 0.09 0.15 5898 1.00
p_predicted[1155] 0.00 0.02 9141 1.00
p_predicted[1156] 0.00 0.02 9144 1.00
p_predicted[1157] 0.00 0.02 9127 1.00
p_predicted[1158] 0.00 0.02 9091 1.00
p_predicted[1159] 0.00 0.02 9110 1.00
p_predicted[1160] 0.00 0.02 9131 1.00
p_predicted[1161] 0.08 0.14 7827 1.00
p_predicted[1162] 0.06 0.12 8386 1.00
p_predicted[1163] 0.08 0.14 7827 1.00
p_predicted[1164] 0.06 0.11 8968 1.00
p_predicted[1165] 0.05 0.09 9576 1.00
p_predicted[1166] 0.06 0.11 8968 1.00
p_predicted[1167] 0.06 0.12 7152 1.00
p_predicted[1168] 0.05 0.10 8367 1.00
p_predicted[1169] 0.06 0.12 7152 1.00
p_predicted[1170] 0.90 0.97 10058 1.00
p_predicted[1171] 0.89 0.97 9386 1.00
p_predicted[1172] 0.88 0.97 9403 1.00
p_predicted[1173] 0.88 0.96 9457 1.00
p_predicted[1174] 0.89 0.97 9769 1.00
p_predicted[1175] 0.13 0.22 2982 1.00
p_predicted[1176] 0.16 0.24 6853 1.00
p_predicted[1177] 0.09 0.13 9843 1.00
p_predicted[1178] 0.11 0.17 5382 1.00
p_predicted[1179] 0.08 0.12 9947 1.00
p_predicted[1180] 0.07 0.11 6385 1.00
p_predicted[1181] 0.07 0.11 4479 1.00
p_predicted[1182] 0.69 0.88 9865 1.00
p_predicted[1183] 0.66 0.87 10078 1.00
p_predicted[1184] 0.67 0.87 10147 1.00
p_predicted[1185] 0.08 0.16 10228 1.00
p_predicted[1186] 0.06 0.11 10869 1.00
p_predicted[1187] 0.06 0.11 10887 1.00
p_predicted[1188] 0.06 0.11 10852 1.00
p_predicted[1189] 0.05 0.11 10567 1.00
p_predicted[1190] 0.08 0.16 10222 1.00
p_predicted[1191] 0.06 0.11 10865 1.00
p_predicted[1192] 0.06 0.11 10886 1.00
p_predicted[1193] 0.06 0.11 10887 1.00
p_predicted[1194] 0.05 0.11 10557 1.00
p_predicted[1195] 0.66 0.80 8962 1.00
p_predicted[1196] 0.66 0.80 8957 1.00
p_predicted[1197] 0.73 0.87 4630 1.00
p_predicted[1198] 0.64 0.77 8812 1.00
p_predicted[1199] 0.64 0.77 8567 1.00
p_predicted[1200] 0.64 0.77 8554 1.00
p_predicted[1201] 0.64 0.77 8543 1.00
p_predicted[1202] 0.64 0.77 8604 1.00
p_predicted[1203] 0.57 0.73 7813 1.00
p_predicted[1204] 0.57 0.73 8133 1.00
p_predicted[1205] 0.00 0.03 8572 1.00
p_predicted[1206] 0.00 0.03 8974 1.00
p_predicted[1207] 0.09 0.20 9712 1.00
p_predicted[1208] 0.09 0.20 9687 1.00
p_predicted[1209] 0.09 0.18 10044 1.00
p_predicted[1210] 0.07 0.13 10396 1.00
p_predicted[1211] 0.06 0.13 10345 1.00
p_predicted[1212] 0.06 0.12 9698 1.00
p_predicted[1213] 0.32 0.44 10874 1.00
p_predicted[1214] 0.31 0.44 10605 1.00
p_predicted[1215] 0.25 0.34 9931 1.00
p_predicted[1216] 0.25 0.34 9946 1.00
p_predicted[1217] 0.25 0.34 10159 1.00
p_predicted[1218] 0.11 0.24 11258 1.00
p_predicted[1219] 0.11 0.24 10536 1.00
p_predicted[1220] 0.11 0.24 10318 1.00
p_predicted[1221] 0.07 0.17 10782 1.00
p_predicted[1222] 0.07 0.17 10779 1.00
p_predicted[1223] 0.07 0.17 10782 1.00
p_predicted[1224] 0.07 0.17 10780 1.00
p_predicted[1225] 0.68 0.82 10447 1.00
p_predicted[1226] 0.68 0.80 9848 1.00
p_predicted[1227] 0.61 0.75 7680 1.00
p_predicted[1228] 0.61 0.75 7982 1.00
p_predicted[1229] 0.35 0.41 3616 1.00
p_predicted[1230] 0.35 0.41 3616 1.00
p_predicted[1231] 0.33 0.39 3968 1.00
p_predicted[1232] 0.33 0.39 3968 1.00
p_predicted[1233] 0.32 0.38 3586 1.00
p_predicted[1234] 0.32 0.38 3586 1.00
p_predicted[1235] 0.27 0.32 2204 1.00
p_predicted[1236] 0.27 0.32 2204 1.00
p_predicted[1237] 0.25 0.30 2736 1.00
p_predicted[1238] 0.25 0.30 2736 1.00
p_predicted[1239] 0.24 0.30 2833 1.00
p_predicted[1240] 0.24 0.30 2833 1.00
p_predicted[1241] 0.24 0.30 2838 1.00
p_predicted[1242] 0.24 0.30 2838 1.00
p_predicted[1243] 0.24 0.30 2826 1.00
p_predicted[1244] 0.24 0.30 2826 1.00
p_predicted[1245] 0.12 0.24 10206 1.00
p_predicted[1246] 0.08 0.16 10188 1.00
p_predicted[1247] 0.12 0.24 10206 1.00
p_predicted[1248] 0.13 0.24 9980 1.00
p_predicted[1249] 0.09 0.16 9741 1.00
p_predicted[1250] 0.13 0.24 9980 1.00
p_predicted[1251] 0.13 0.24 10108 1.00
p_predicted[1252] 0.09 0.16 9876 1.00
p_predicted[1253] 0.13 0.24 10108 1.00
p_predicted[1254] 0.13 0.24 10254 1.00
p_predicted[1255] 0.09 0.16 10044 1.00
p_predicted[1256] 0.13 0.24 10254 1.00
p_predicted[1257] 0.15 0.27 10454 1.00
p_predicted[1258] 0.10 0.18 10299 1.00
p_predicted[1259] 0.15 0.27 10454 1.00
p_predicted[1260] 0.11 0.22 7971 1.00
p_predicted[1261] 0.08 0.14 8033 1.00
p_predicted[1262] 0.11 0.22 7971 1.00
p_predicted[1263] 0.00 0.01 9402 1.00
p_predicted[1264] 0.00 0.01 9414 1.00
p_predicted[1265] 0.00 0.01 9458 1.00
p_predicted[1266] 0.00 0.01 9489 1.00
p_predicted[1267] 0.00 0.01 9291 1.00
p_predicted[1268] 0.09 0.20 9504 1.00
p_predicted[1269] 0.07 0.16 8591 1.00
p_predicted[1270] 0.00 0.02 9094 1.00
p_predicted[1271] 0.00 0.02 9072 1.00
p_predicted[1272] 0.00 0.02 9098 1.00
p_predicted[1273] 0.00 0.02 9173 1.00
p_predicted[1274] 0.09 0.14 8393 1.00
p_predicted[1275] 0.09 0.14 8393 1.00
p_predicted[1276] 0.06 0.10 8678 1.00
p_predicted[1277] 0.06 0.10 8678 1.00
p_predicted[1278] 0.05 0.13 7376 1.00
p_predicted[1279] 0.04 0.12 7488 1.00
p_predicted[1280] 0.04 0.11 7431 1.00
p_predicted[1281] 0.04 0.11 7417 1.00
p_predicted[1282] 0.04 0.10 7577 1.00
p_predicted[1283] 0.04 0.10 7577 1.00
p_predicted[1284] 0.04 0.10 7577 1.00
p_predicted[1285] 0.04 0.10 7579 1.00
p_predicted[1286] 0.04 0.10 7586 1.00
p_predicted[1287] 0.04 0.10 7587 1.00
p_predicted[1288] 0.04 0.10 7589 1.00
p_predicted[1289] 0.00 0.02 9125 1.00
p_predicted[1290] 0.00 0.02 9095 1.00
p_predicted[1291] 0.00 0.02 9104 1.00
p_predicted[1292] 0.00 0.02 9132 1.00
p_predicted[1293] 0.11 0.19 8916 1.00
p_predicted[1294] 0.12 0.19 8199 1.00
p_predicted[1295] 0.16 0.25 8611 1.00
p_predicted[1296] 0.17 0.26 8047 1.00
p_predicted[1297] 0.13 0.21 6271 1.00
p_predicted[1298] 0.43 0.61 3036 1.00
p_predicted[1299] 0.43 0.61 3036 1.00
p_predicted[1300] 0.33 0.46 9097 1.00
p_predicted[1301] 0.33 0.46 9097 1.00
p_predicted[1302] 0.34 0.46 10118 1.00
p_predicted[1303] 0.34 0.46 10118 1.00
p_predicted[1304] 0.27 0.37 10050 1.00
p_predicted[1305] 0.27 0.37 10050 1.00
p_predicted[1306] 0.26 0.35 9768 1.00
p_predicted[1307] 0.26 0.35 9768 1.00
p_predicted[1308] 0.25 0.33 9797 1.00
p_predicted[1309] 0.25 0.33 9797 1.00
p_predicted[1310] 0.24 0.33 9842 1.00
p_predicted[1311] 0.24 0.33 9842 1.00
p_predicted[1312] 0.00 0.02 9595 1.00
p_predicted[1313] 0.00 0.02 9409 1.00
p_predicted[1314] 0.00 0.02 9393 1.00
p_predicted[1315] 0.00 0.02 9457 1.00
p_predicted[1316] 0.00 0.02 9608 1.00
p_predicted[1317] 0.00 0.02 9617 1.00
p_predicted[1318] 0.00 0.02 9639 1.00
p_predicted[1319] 0.00 0.02 9644 1.00
p_predicted[1320] 0.00 0.02 9595 1.00
p_predicted[1321] 0.00 0.02 9409 1.00
p_predicted[1322] 0.00 0.02 9393 1.00
p_predicted[1323] 0.00 0.02 9457 1.00
p_predicted[1324] 0.00 0.02 9609 1.00
p_predicted[1325] 0.00 0.02 9617 1.00
p_predicted[1326] 0.00 0.02 9643 1.00
p_predicted[1327] 0.32 0.43 11381 1.00
p_predicted[1328] 0.26 0.35 9619 1.00
p_predicted[1329] 0.25 0.34 10419 1.00
p_predicted[1330] 0.00 0.02 9147 1.00
p_predicted[1331] 0.00 0.02 9150 1.00
p_predicted[1332] 0.00 0.02 9134 1.00
p_predicted[1333] 0.00 0.02 9105 1.00
p_predicted[1334] 0.00 0.02 9102 1.00
p_predicted[1335] 0.00 0.02 9098 1.00
p_predicted[1336] 0.00 0.02 9099 1.00
p_predicted[1337] 0.00 0.02 9099 1.00
p_predicted[1338] 0.00 0.02 9094 1.00
p_predicted[1339] 0.00 0.02 9129 1.00
p_predicted_default[1] 0.04 0.11 7586 1.00
p_predicted_default[2] 0.22 0.28 5221 1.00
p_predicted_default[3] 0.17 0.21 6092 1.00
p_predicted_default[4] 0.17 0.21 6092 1.00
p_predicted_default[5] 0.08 0.12 7831 1.00
p_predicted_default[6] 0.05 0.08 8538 1.00
p_predicted_default[7] 0.41 0.50 4458 1.00
p_predicted_default[8] 0.26 0.45 6797 1.00
p_predicted_default[9] 0.13 0.18 4976 1.00
p_predicted_default[10] 0.01 0.03 8288 1.00
p_predicted_default[11] 0.00 0.06 10261 1.00
p_predicted_default[12] 0.20 0.25 6535 1.00
p_predicted_default[13] 0.09 0.14 3573 1.00
p_predicted_default[14] 0.12 0.17 10260 1.00
p_predicted_default[15] 0.12 0.17 10260 1.00
p_predicted_default[16] 0.29 0.34 4790 1.00
p_predicted_default[17] 0.05 0.07 8681 1.00
p_predicted_default[18] 0.09 0.13 7069 1.00
p_predicted_default[19] 0.09 0.13 7739 1.00
p_predicted_default[20] 0.01 0.12 10392 1.00
p_predicted_default[21] 0.14 0.19 2993 1.00
p_predicted_default[22] 0.47 0.64 8893 1.00
p_predicted_default[23] 0.47 0.64 8927 1.00
p_predicted_default[24] 0.06 0.09 8915 1.00
p_predicted_default[25] 0.47 0.55 3820 1.00
p_predicted_default[26] 0.47 0.55 3820 1.00
p_predicted_default[27] 0.47 0.55 3820 1.00
p_predicted_default[28] 0.02 0.04 8894 1.00
p_predicted_default[29] 0.02 0.04 8894 1.00
p_predicted_default[30] 0.06 0.10 3448 1.00
p_predicted_default[31] 0.08 0.12 4526 1.00
p_predicted_default[32] 0.09 0.20 5744 1.00
p_predicted_default[33] 0.36 0.44 3075 1.00
p_predicted_default[34] 0.36 0.44 3075 1.00
p_predicted_default[35] 0.17 0.25 7658 1.00
p_predicted_default[36] 0.17 0.27 7253 1.00
p_predicted_default[37] 0.15 0.19 7998 1.00
p_predicted_default[38] 0.15 0.19 7998 1.00
p_predicted_default[39] 0.15 0.19 7998 1.00
p_predicted_default[40] 0.28 0.36 2579 1.00
p_predicted_default[41] 0.09 0.12 4753 1.00
p_predicted_default[42] 0.27 0.36 5154 1.00
p_predicted_default[43] 0.22 0.28 4085 1.00
p_predicted_default[44] 0.19 0.25 7211 1.00
p_predicted_default[45] 0.20 0.25 4541 1.00
p_predicted_default[46] 0.19 0.23 5313 1.00
p_predicted_default[47] 0.06 0.10 8727 1.00
p_predicted_default[48] 0.06 0.10 8727 1.00
p_predicted_default[49] 0.20 0.25 3964 1.00
p_predicted_default[50] 0.00 0.06 9721 1.00
p_predicted_default[51] 0.30 0.35 3648 1.00
p_predicted_default[52] 0.30 0.35 3648 1.00
p_predicted_default[53] 0.30 0.35 3648 1.00
p_predicted_default[54] 0.27 0.33 4054 1.00
p_predicted_default[55] 0.27 0.33 4054 1.00
p_predicted_default[56] 0.27 0.33 4054 1.00
p_predicted_default[57] 0.00 0.04 8999 1.00
p_predicted_default[58] 0.10 0.17 8476 1.00
p_predicted_default[59] 0.08 0.17 9928 1.00
p_predicted_default[60] 0.20 0.26 6467 1.00
p_predicted_default[61] 0.13 0.24 8176 1.00
p_predicted_default[62] 0.13 0.23 7372 1.00
p_predicted_default[63] 0.36 0.43 3181 1.00
p_predicted_default[64] 0.85 0.95 7640 1.00
p_predicted_default[65] 0.85 0.95 7640 1.00
p_predicted_default[66] 0.08 0.15 9872 1.00
p_predicted_default[67] 0.05 0.09 9282 1.00
p_predicted_default[68] 0.38 0.46 3290 1.00
p_predicted_default[69] 0.23 0.34 6911 1.00
p_predicted_default[70] 0.25 0.40 7978 1.00
p_predicted_default[71] 0.04 0.07 7830 1.00
p_predicted_default[72] 0.04 0.07 7830 1.00
p_predicted_default[73] 0.06 0.13 10736 1.00
p_predicted_default[74] 0.07 0.11 7705 1.00
p_predicted_default[75] 0.05 0.08 8841 1.00
p_predicted_default[76] 0.07 0.11 7705 1.00
p_predicted_default[77] 0.47 0.55 3435 1.00
p_predicted_default[78] 0.21 0.27 3172 1.00
p_predicted_default[79] 0.00 0.02 10319 1.00
p_predicted_default[80] 0.30 0.47 7432 1.00
p_predicted_default[81] 0.50 0.67 8571 1.00
p_predicted_default[82] 0.00 0.01 8808 1.00
p_predicted_default[83] 0.01 0.20 9786 1.00
p_predicted_default[84] 0.00 0.01 9232 1.00
p_predicted_default[85] 0.00 0.01 9174 1.00
p_predicted_default[86] 0.00 0.01 9214 1.00
p_predicted_default[87] 0.29 0.34 1858 1.00
p_predicted_default[88] 0.00 0.03 10329 1.00
p_predicted_default[89] 0.03 0.06 7988 1.00
p_predicted_default[90] 0.06 0.12 10622 1.00
p_predicted_default[91] 0.00 0.02 9467 1.00
p_predicted_default[92] 0.06 0.14 10180 1.00
p_predicted_default[93] 0.09 0.12 4407 1.00
p_predicted_default[94] 0.11 0.17 9849 1.00
p_predicted_default[95] 0.14 0.18 4400 1.00
p_predicted_default[96] 0.00 0.04 9016 1.00
p_predicted_default[97] 0.21 0.30 6801 1.00
p_predicted_default[98] 0.03 0.07 7601 1.00
p_predicted_default[99] 0.00 0.02 9378 1.00
p_predicted_default[100] 0.00 0.01 8757 1.00
p_predicted_default[101] 0.13 0.17 5991 1.00
p_predicted_default[102] 0.20 0.25 4707 1.00
p_predicted_default[103] 0.03 0.06 7572 1.00
p_predicted_default[104] 0.02 0.05 8537 1.00
p_predicted_default[105] 0.03 0.06 7572 1.00
p_predicted_default[106] 0.09 0.13 3401 1.00
p_predicted_default[107] 0.52 0.61 4122 1.00
p_predicted_default[108] 0.22 0.29 9498 1.00
p_predicted_default[109] 0.22 0.29 9498 1.00
p_predicted_default[110] 0.22 0.29 9498 1.00
p_predicted_default[111] 0.00 0.01 9288 1.00
p_predicted_default[112] 0.00 0.03 9463 1.00
p_predicted_default[113] 0.13 0.24 9423 1.00
p_predicted_default[114] 0.29 0.46 6353 1.00
p_predicted_default[115] 0.06 0.12 10653 1.00
p_predicted_default[116] 0.06 0.11 10826 1.00
p_predicted_default[117] 0.06 0.11 10880 1.00
p_predicted_default[118] 0.04 0.08 8448 1.00
p_predicted_default[119] 0.04 0.08 8407 1.00
p_predicted_default[120] 0.00 0.02 9384 1.00
p_predicted_default[121] 0.00 0.02 9384 1.00
p_predicted_default[122] 0.00 0.02 9400 1.00
p_predicted_default[123] 0.16 0.22 7769 1.00
p_predicted_default[124] 0.16 0.22 7769 1.00
p_predicted_default[125] 0.14 0.18 4147 1.00
p_predicted_default[126] 0.15 0.22 9759 1.00
p_predicted_default[127] 0.41 0.51 3007 1.00
p_predicted_default[128] 0.14 0.18 4015 1.00
p_predicted_default[129] 0.21 0.29 9586 1.00
p_predicted_default[130] 0.00 0.04 8695 1.00
p_predicted_default[131] 0.15 0.24 6993 1.00
p_predicted_default[132] 0.10 0.17 7127 1.00
p_predicted_default[133] 0.15 0.24 6993 1.00
p_predicted_default[134] 0.07 0.11 7413 1.00
p_predicted_default[135] 0.05 0.09 8686 1.00
p_predicted_default[136] 0.07 0.11 7413 1.00
p_predicted_default[137] 0.00 0.02 9110 1.00
p_predicted_default[138] 0.06 0.11 8968 1.00
p_predicted_default[139] 0.05 0.09 9576 1.00
p_predicted_default[140] 0.06 0.11 8968 1.00
p_predicted_default[141] 0.89 0.97 9769 1.00
p_predicted_default[142] 0.09 0.13 9843 1.00
p_predicted_default[143] 0.06 0.11 10869 1.00
p_predicted_default[144] 0.06 0.11 10865 1.00
p_predicted_default[145] 0.57 0.73 7813 1.00
p_predicted_default[146] 0.00 0.03 8974 1.00
p_predicted_default[147] 0.07 0.13 10396 1.00
p_predicted_default[148] 0.07 0.17 10782 1.00
p_predicted_default[149] 0.61 0.75 7680 1.00
p_predicted_default[150] 0.27 0.32 2204 1.00
p_predicted_default[151] 0.27 0.32 2204 1.00
p_predicted_default[152] 0.11 0.22 7971 1.00
p_predicted_default[153] 0.08 0.14 8033 1.00
p_predicted_default[154] 0.11 0.22 7971 1.00
p_predicted_default[155] 0.00 0.01 9291 1.00
p_predicted_default[156] 0.07 0.16 8591 1.00
p_predicted_default[157] 0.00 0.02 9173 1.00
p_predicted_default[158] 0.06 0.10 8678 1.00
p_predicted_default[159] 0.06 0.10 8678 1.00
p_predicted_default[160] 0.04 0.10 7577 1.00
p_predicted_default[161] 0.00 0.02 9104 1.00
p_predicted_default[162] 0.13 0.21 6271 1.00
p_predicted_default[163] 0.27 0.37 10050 1.00
p_predicted_default[164] 0.27 0.37 10050 1.00
p_predicted_default[165] 0.00 0.02 9608 1.00
p_predicted_default[166] 0.00 0.02 9609 1.00
p_predicted_default[167] 0.26 0.35 9619 1.00
p_predicted_default[168] 0.00 0.02 9129 1.00
p_predicted_intervention[1] 1.00 1.00 10008 1.00
p_predicted_intervention[2] 0.00 0.02 8327 1.00
p_predicted_intervention[3] 0.00 0.01 8920 1.00
p_predicted_intervention[4] 0.00 0.01 8920 1.00
p_predicted_intervention[5] 0.63 0.99 7292 1.00
p_predicted_intervention[6] 0.58 1.00 7728 1.00
p_predicted_intervention[7] 0.00 0.01 8681 1.00
p_predicted_intervention[8] 0.59 1.00 8868 1.00
p_predicted_intervention[9] 0.00 0.01 8759 1.00
p_predicted_intervention[10] 1.00 1.00 10008 1.00
p_predicted_intervention[11] 0.70 1.00 10732 1.00
p_predicted_intervention[12] 0.00 0.02 8261 1.00
p_predicted_intervention[13] 0.00 0.00 9315 1.00
p_predicted_intervention[14] 0.23 0.46 1410 1.00
p_predicted_intervention[15] 0.23 0.46 1410 1.00
p_predicted_intervention[16] 0.00 0.02 8423 1.00
p_predicted_intervention[17] 0.58 1.00 7717 1.00
p_predicted_intervention[18] 0.62 0.99 7318 1.00
p_predicted_intervention[19] 0.61 0.99 7310 1.00
p_predicted_intervention[20] 0.13 1.00 9799 1.00
p_predicted_intervention[21] 0.00 0.01 9004 1.00
p_predicted_intervention[22] 0.00 1.00 8443 1.00
p_predicted_intervention[23] 0.00 1.00 8442 1.00
p_predicted_intervention[24] 0.58 1.00 7768 1.00
p_predicted_intervention[25] 0.00 0.02 8426 1.00
p_predicted_intervention[26] 0.00 0.02 8426 1.00
p_predicted_intervention[27] 0.00 0.02 8426 1.00
p_predicted_intervention[28] 1.00 1.00 10008 1.00
p_predicted_intervention[29] 1.00 1.00 10008 1.00
p_predicted_intervention[30] 0.00 0.01 9188 1.00
p_predicted_intervention[31] 0.00 0.00 9243 1.00
p_predicted_intervention[32] 1.00 1.00 10008 1.00
p_predicted_intervention[33] 0.33 0.48 1211 1.00
p_predicted_intervention[34] 0.33 0.48 1211 1.00
p_predicted_intervention[35] 0.00 0.41 7758 1.00
p_predicted_intervention[36] 0.00 0.41 7980 1.00
p_predicted_intervention[37] 0.00 0.02 8747 1.00
p_predicted_intervention[38] 0.00 0.02 8747 1.00
p_predicted_intervention[39] 0.00 0.02 8747 1.00
p_predicted_intervention[40] 0.00 0.01 8713 1.00
p_predicted_intervention[41] 0.00 0.01 9001 1.00
p_predicted_intervention[42] 0.00 0.01 8764 1.00
p_predicted_intervention[43] 0.00 0.03 8186 1.00
p_predicted_intervention[44] 0.00 0.02 8243 1.00
p_predicted_intervention[45] 0.29 0.46 1238 1.00
p_predicted_intervention[46] 0.00 0.02 8671 1.00
p_predicted_intervention[47] 0.63 1.00 8955 1.00
p_predicted_intervention[48] 0.63 1.00 8955 1.00
p_predicted_intervention[49] 0.30 0.46 1230 1.00
p_predicted_intervention[50] 0.61 1.00 10507 1.00
p_predicted_intervention[51] 0.00 0.01 8686 1.00
p_predicted_intervention[52] 0.00 0.01 8686 1.00
p_predicted_intervention[53] 0.00 0.01 8686 1.00
p_predicted_intervention[54] 0.00 0.01 8834 1.00
p_predicted_intervention[55] 0.00 0.01 8834 1.00
p_predicted_intervention[56] 0.00 0.01 8834 1.00
p_predicted_intervention[57] 0.28 1.00 10566 1.00
p_predicted_intervention[58] 0.00 0.41 7877 1.00
p_predicted_intervention[59] 0.66 1.00 9063 1.00
p_predicted_intervention[60] 0.00 0.03 8215 1.00
p_predicted_intervention[61] 0.00 0.48 7584 1.00
p_predicted_intervention[62] 0.00 0.47 7840 1.00
p_predicted_intervention[63] 0.32 0.48 1215 1.00
p_predicted_intervention[64] 1.00 1.00 10008 1.00
p_predicted_intervention[65] 1.00 1.00 10008 1.00
p_predicted_intervention[66] 0.92 1.00 11126 1.00
p_predicted_intervention[67] 0.68 1.00 9093 1.00
p_predicted_intervention[68] 0.00 0.02 8608 1.00
p_predicted_intervention[69] 0.00 0.42 7825 1.00
p_predicted_intervention[70] 0.00 0.41 8017 1.00
p_predicted_intervention[71] 0.61 1.00 7660 1.00
p_predicted_intervention[72] 0.61 1.00 7660 1.00
p_predicted_intervention[73] 0.92 1.00 11159 1.00
p_predicted_intervention[74] 0.62 1.00 8966 1.00
p_predicted_intervention[75] 0.65 1.00 9012 1.00
p_predicted_intervention[76] 0.62 1.00 8966 1.00
p_predicted_intervention[77] 0.00 0.02 8406 1.00
p_predicted_intervention[78] 0.31 0.48 1219 1.00
p_predicted_intervention[79] 0.21 1.00 10456 1.00
p_predicted_intervention[80] 0.00 0.43 7737 1.00
p_predicted_intervention[81] 0.95 1.00 7053 1.00
p_predicted_intervention[82] 0.55 1.00 10542 1.00
p_predicted_intervention[83] 0.68 1.00 11124 1.00
p_predicted_intervention[84] 0.63 1.00 10666 1.00
p_predicted_intervention[85] 0.63 1.00 10668 1.00
p_predicted_intervention[86] 0.63 1.00 10668 1.00
p_predicted_intervention[87] 0.35 0.48 1138 1.00
p_predicted_intervention[88] 0.22 1.00 10476 1.00
p_predicted_intervention[89] 1.00 1.00 10008 1.00
p_predicted_intervention[90] 0.92 1.00 11231 1.00
p_predicted_intervention[91] 0.62 1.00 10684 1.00
p_predicted_intervention[92] 0.00 0.43 7750 1.00
p_predicted_intervention[93] 0.00 0.01 8682 1.00
p_predicted_intervention[94] 0.23 0.47 1422 1.00
p_predicted_intervention[95] 0.29 0.44 1188 1.00
p_predicted_intervention[96] 0.28 1.00 10568 1.00
p_predicted_intervention[97] 0.00 0.06 7507 1.00
p_predicted_intervention[98] 0.61 1.00 7626 1.00
p_predicted_intervention[99] 0.62 1.00 10679 1.00
p_predicted_intervention[100] 0.55 1.00 10543 1.00
p_predicted_intervention[101] 0.27 0.45 1257 1.00
p_predicted_intervention[102] 0.00 0.05 7566 1.00
p_predicted_intervention[103] 0.66 1.00 9081 1.00
p_predicted_intervention[104] 0.69 1.00 9115 1.00
p_predicted_intervention[105] 0.66 1.00 9081 1.00
p_predicted_intervention[106] 0.00 0.01 9117 1.00
p_predicted_intervention[107] 0.00 0.01 8676 1.00
p_predicted_intervention[108] 0.54 0.99 7019 1.00
p_predicted_intervention[109] 0.54 0.99 7019 1.00
p_predicted_intervention[110] 0.54 0.99 7019 1.00
p_predicted_intervention[111] 0.63 1.00 10665 1.00
p_predicted_intervention[112] 0.27 1.00 10605 1.00
p_predicted_intervention[113] 0.63 1.00 8974 1.00
p_predicted_intervention[114] 0.55 1.00 8855 1.00
p_predicted_intervention[115] 0.92 1.00 11224 1.00
p_predicted_intervention[116] 0.92 1.00 11194 1.00
p_predicted_intervention[117] 0.92 1.00 11172 1.00
p_predicted_intervention[118] 0.62 1.00 7527 1.00
p_predicted_intervention[119] 0.59 1.00 7633 1.00
p_predicted_intervention[120] 0.09 1.00 9325 1.00
p_predicted_intervention[121] 0.09 1.00 9325 1.00
p_predicted_intervention[122] 0.09 1.00 9333 1.00
p_predicted_intervention[123] 0.61 0.99 7081 1.00
p_predicted_intervention[124] 0.61 0.99 7081 1.00
p_predicted_intervention[125] 0.28 0.47 1241 1.00
p_predicted_intervention[126] 0.56 0.99 7364 1.00
p_predicted_intervention[127] 0.00 0.02 8629 1.00
p_predicted_intervention[128] 0.30 0.45 1184 1.00
p_predicted_intervention[129] 0.53 0.99 7024 1.00
p_predicted_intervention[130] 0.28 1.00 10560 1.00
p_predicted_intervention[131] 0.55 1.00 8785 1.00
p_predicted_intervention[132] 0.59 1.00 8854 1.00
p_predicted_intervention[133] 0.55 1.00 8785 1.00
p_predicted_intervention[134] 0.62 1.00 8966 1.00
p_predicted_intervention[135] 0.66 1.00 9016 1.00
p_predicted_intervention[136] 0.62 1.00 8966 1.00
p_predicted_intervention[137] 0.11 1.00 9396 1.00
p_predicted_intervention[138] 0.63 1.00 9049 1.00
p_predicted_intervention[139] 0.67 1.00 9090 1.00
p_predicted_intervention[140] 0.63 1.00 9049 1.00
p_predicted_intervention[141] 0.00 0.99 7401 1.00
p_predicted_intervention[142] 0.21 0.46 1473 1.00
p_predicted_intervention[143] 0.92 1.00 11182 1.00
p_predicted_intervention[144] 0.92 1.00 11184 1.00
p_predicted_intervention[145] 0.96 1.00 7117 1.00
p_predicted_intervention[146] 0.28 1.00 10613 1.00
p_predicted_intervention[147] 0.92 1.00 11184 1.00
p_predicted_intervention[148] 0.00 0.40 7699 1.00
p_predicted_intervention[149] 0.00 1.00 8476 1.00
p_predicted_intervention[150] 0.34 0.47 1145 1.00
p_predicted_intervention[151] 0.34 0.47 1145 1.00
p_predicted_intervention[152] 0.55 1.00 8773 1.00
p_predicted_intervention[153] 0.58 1.00 8842 1.00
p_predicted_intervention[154] 0.55 1.00 8773 1.00
p_predicted_intervention[155] 0.07 1.00 9286 1.00
p_predicted_intervention[156] 0.55 1.00 8745 1.00
p_predicted_intervention[157] 0.09 1.00 9348 1.00
p_predicted_intervention[158] 0.61 1.00 7494 1.00
p_predicted_intervention[159] 0.61 1.00 7494 1.00
p_predicted_intervention[160] 1.00 1.00 10008 1.00
p_predicted_intervention[161] 0.10 1.00 9406 1.00
p_predicted_intervention[162] 0.62 1.00 8913 1.00
p_predicted_intervention[163] 0.54 0.99 7124 1.00
p_predicted_intervention[164] 0.54 0.99 7124 1.00
p_predicted_intervention[165] 0.11 1.00 9363 1.00
p_predicted_intervention[166] 0.11 1.00 9363 1.00
p_predicted_intervention[167] 0.60 0.99 7157 1.00
p_predicted_intervention[168] 0.10 1.00 9383 1.00
predicted_difference[1] 0.99 1.00 7585 1.00
predicted_difference[2] -0.17 -0.12 6680 1.00
predicted_difference[3] -0.14 -0.11 7714 1.00
predicted_difference[4] -0.14 -0.11 7714 1.00
predicted_difference[5] 0.58 0.95 7291 1.00
predicted_difference[6] 0.54 0.97 7648 1.00
predicted_difference[7] -0.32 -0.24 5569 1.00
predicted_difference[8] 0.40 0.91 8837 1.00
predicted_difference[9] -0.09 -0.06 6554 1.00
predicted_difference[10] 1.00 1.00 8284 1.00
predicted_difference[11] 0.69 1.00 10696 1.00
predicted_difference[12] -0.15 -0.09 7453 1.00
predicted_difference[13] -0.06 -0.04 4400 1.00
predicted_difference[14] 0.13 0.36 1496 1.00
predicted_difference[15] 0.13 0.36 1496 1.00
predicted_difference[16] -0.23 -0.18 6403 1.00
predicted_difference[17] 0.54 0.97 7640 1.00
predicted_difference[18] 0.54 0.93 7320 1.00
predicted_difference[19] 0.54 0.93 7314 1.00
predicted_difference[20] 0.11 1.00 9632 1.00
predicted_difference[21] -0.10 -0.07 4059 1.00
predicted_difference[22] -0.24 0.59 8136 1.00
predicted_difference[23] -0.23 0.59 8143 1.00
predicted_difference[24] 0.53 0.96 7697 1.00
predicted_difference[25] -0.38 -0.29 4907 1.00
predicted_difference[26] -0.38 -0.29 4907 1.00
predicted_difference[27] -0.38 -0.29 4907 1.00
predicted_difference[28] 0.99 1.00 8894 1.00
predicted_difference[29] 0.99 1.00 8894 1.00
predicted_difference[30] -0.04 -0.02 5652 1.00
predicted_difference[31] -0.04 -0.03 5096 1.00
predicted_difference[32] 0.97 0.99 5743 1.00
predicted_difference[33] 0.00 0.13 1513 1.00
predicted_difference[34] 0.00 0.13 1513 1.00
predicted_difference[35] -0.09 0.27 6918 1.00
predicted_difference[36] -0.09 0.28 7356 1.00
predicted_difference[37] -0.11 -0.08 8073 1.00
predicted_difference[38] -0.11 -0.08 8073 1.00
predicted_difference[39] -0.11 -0.08 8073 1.00
predicted_difference[40] -0.21 -0.16 3768 1.00
predicted_difference[41] -0.06 -0.04 7344 1.00
predicted_difference[42] -0.20 -0.14 5883 1.00
predicted_difference[43] -0.16 -0.10 5959 1.00
predicted_difference[44] -0.14 -0.09 7758 1.00
predicted_difference[45] 0.11 0.27 1354 1.00
predicted_difference[46] -0.15 -0.12 7333 1.00
predicted_difference[47] 0.58 0.97 8965 1.00
predicted_difference[48] 0.58 0.97 8965 1.00
predicted_difference[49] 0.11 0.28 1351 1.00
predicted_difference[50] 0.59 1.00 10438 1.00
predicted_difference[51] -0.24 -0.19 5357 1.00
predicted_difference[52] -0.24 -0.19 5357 1.00
predicted_difference[53] -0.24 -0.19 5357 1.00
predicted_difference[54] -0.21 -0.16 5470 1.00
predicted_difference[55] -0.21 -0.16 5470 1.00
predicted_difference[56] -0.21 -0.16 5470 1.00
predicted_difference[57] 0.27 1.00 10546 1.00
predicted_difference[58] -0.04 0.32 7043 1.00
predicted_difference[59] 0.61 0.98 9265 1.00
predicted_difference[60] -0.15 -0.09 7419 1.00
predicted_difference[61] -0.05 0.35 6822 1.00
predicted_difference[62] -0.06 0.37 7134 1.00
predicted_difference[63] 0.00 0.13 1519 1.00
predicted_difference[64] 0.34 0.57 7640 1.00
predicted_difference[65] 0.34 0.57 7640 1.00
predicted_difference[66] 0.84 0.98 10957 1.00
predicted_difference[67] 0.65 0.99 9174 1.00
predicted_difference[68] -0.30 -0.22 4437 1.00
predicted_difference[69] -0.14 0.26 7382 1.00
predicted_difference[70] -0.13 0.29 8137 1.00
predicted_difference[71] 0.58 0.98 7625 1.00
predicted_difference[72] 0.58 0.98 7625 1.00
predicted_difference[73] 0.83 0.96 10991 1.00
predicted_difference[74] 0.57 0.96 8978 1.00
predicted_difference[75] 0.62 0.98 9044 1.00
predicted_difference[76] 0.57 0.96 8978 1.00
predicted_difference[77] -0.38 -0.30 4573 1.00
predicted_difference[78] 0.11 0.28 1349 1.00
predicted_difference[79] 0.20 1.00 10461 1.00
predicted_difference[80] -0.16 0.28 8292 1.00
predicted_difference[81] 0.52 0.76 7664 1.00
predicted_difference[82] 0.55 1.00 10537 1.00
predicted_difference[83] 0.63 1.00 10915 1.00
predicted_difference[84] 0.63 1.00 10661 1.00
predicted_difference[85] 0.63 1.00 10663 1.00
predicted_difference[86] 0.63 1.00 10663 1.00
predicted_difference[87] 0.09 0.20 1248 1.00
predicted_difference[88] 0.22 1.00 10501 1.00
predicted_difference[89] 0.99 1.00 7986 1.00
predicted_difference[90] 0.88 0.99 11153 1.00
predicted_difference[91] 0.62 1.00 10669 1.00
predicted_difference[92] -0.02 0.33 7246 1.00
predicted_difference[93] -0.06 -0.04 5850 1.00
predicted_difference[94] 0.14 0.38 1512 1.00
predicted_difference[95] 0.17 0.31 1248 1.00
predicted_difference[96] 0.27 1.00 10549 1.00
predicted_difference[97] -0.14 -0.04 7487 1.00
predicted_difference[98] 0.59 0.98 7564 1.00
predicted_difference[99] 0.62 1.00 10678 1.00
predicted_difference[100] 0.55 1.00 10539 1.00
predicted_difference[101] 0.16 0.34 1330 1.00
predicted_difference[102] -0.15 -0.07 6280 1.00
predicted_difference[103] 0.64 0.99 9124 1.00
predicted_difference[104] 0.67 0.99 9152 1.00
predicted_difference[105] 0.64 0.99 9124 1.00
predicted_difference[106] -0.06 -0.04 5467 1.00
predicted_difference[107] -0.42 -0.32 4466 1.00
predicted_difference[108] 0.34 0.79 6966 1.00
predicted_difference[109] 0.34 0.79 6966 1.00
predicted_difference[110] 0.34 0.79 6966 1.00
predicted_difference[111] 0.62 1.00 10660 1.00
predicted_difference[112] 0.27 1.00 10604 1.00
predicted_difference[113] 0.55 0.97 9158 1.00
predicted_difference[114] 0.34 0.88 8877 1.00
predicted_difference[115] 0.88 0.99 11147 1.00
predicted_difference[116] 0.88 0.99 11123 1.00
predicted_difference[117] 0.88 0.99 11104 1.00
predicted_difference[118] 0.60 0.99 7559 1.00
predicted_difference[119] 0.56 0.98 7657 1.00
predicted_difference[120] 0.08 1.00 9308 1.00
predicted_difference[121] 0.08 1.00 9308 1.00
predicted_difference[122] 0.08 1.00 9316 1.00
predicted_difference[123] 0.49 0.90 7210 1.00
predicted_difference[124] 0.49 0.90 7210 1.00
predicted_difference[125] 0.16 0.35 1324 1.00
predicted_difference[126] 0.41 0.83 7262 1.00
predicted_difference[127] -0.31 -0.22 3383 1.00
predicted_difference[128] 0.17 0.32 1247 1.00
predicted_difference[129] 0.34 0.79 6971 1.00
predicted_difference[130] 0.27 1.00 10537 1.00
predicted_difference[131] 0.41 0.87 8678 1.00
predicted_difference[132] 0.49 0.93 8793 1.00
predicted_difference[133] 0.41 0.87 8678 1.00
predicted_difference[134] 0.57 0.97 8978 1.00
predicted_difference[135] 0.62 0.98 9048 1.00
predicted_difference[136] 0.57 0.97 8978 1.00
predicted_difference[137] 0.10 1.00 9371 1.00
predicted_difference[138] 0.59 0.98 9112 1.00
predicted_difference[139] 0.65 0.99 9172 1.00
predicted_difference[140] 0.59 0.98 9112 1.00
predicted_difference[141] -0.62 0.12 7870 1.00
predicted_difference[142] 0.14 0.40 1533 1.00
predicted_difference[143] 0.88 0.99 11112 1.00
predicted_difference[144] 0.88 0.99 11114 1.00
predicted_difference[145] 0.44 0.69 7549 1.00
predicted_difference[146] 0.27 1.00 10603 1.00
predicted_difference[147] 0.84 0.97 11020 1.00
predicted_difference[148] -0.02 0.29 7316 1.00
predicted_difference[149] -0.39 0.58 8437 1.00
predicted_difference[150] 0.08 0.20 1251 1.00
predicted_difference[151] 0.08 0.20 1251 1.00
predicted_difference[152] 0.44 0.89 8767 1.00
predicted_difference[153] 0.51 0.94 8828 1.00
predicted_difference[154] 0.44 0.89 8767 1.00
predicted_difference[155] 0.06 1.00 9270 1.00
predicted_difference[156] 0.49 0.93 8810 1.00
predicted_difference[157] 0.09 1.00 9321 1.00
predicted_difference[158] 0.56 0.96 7453 1.00
predicted_difference[159] 0.56 0.96 7453 1.00
predicted_difference[160] 0.99 1.00 7575 1.00
predicted_difference[161] 0.10 1.00 9381 1.00
predicted_difference[162] 0.53 0.95 8918 1.00
predicted_difference[163] 0.30 0.73 7318 1.00
predicted_difference[164] 0.30 0.73 7318 1.00
predicted_difference[165] 0.10 1.00 9345 1.00
predicted_difference[166] 0.10 1.00 9345 1.00
predicted_difference[167] 0.38 0.79 7497 1.00
predicted_difference[168] 0.10 1.00 9357 1.00
lp__ -284.92 -237.52 459 1.00
Samples were drawn using NUTS(diag_e) at Sat Jan 11 22:10:04 2025.
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>
</section>
<section id="parameter-distributions" class="level1">
<h1>Parameter Distributions</h1>
<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><span class="co">#g1 &lt;- group_mcmc_areas("beta",beta_list,fit,1)</span></span>
<span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-3"><a href="#cb46-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-4"><a href="#cb46-4" aria-hidden="true" tabindex="-1"></a>gx <span class="ot">&lt;-</span> <span class="fu">c</span>()</span>
<span id="cb46-5"><a href="#cb46-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-6"><a href="#cb46-6" aria-hidden="true" tabindex="-1"></a><span class="co">#grab parameters for every category with more than 8 observations</span></span>
<span id="cb46-7"><a href="#cb46-7" 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="cb46-8"><a href="#cb46-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(i)</span>
<span id="cb46-9"><a href="#cb46-9" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb46-10"><a href="#cb46-10" aria-hidden="true" tabindex="-1"></a> <span class="co">#Print parameter distributions</span></span>
<span id="cb46-11"><a href="#cb46-11" 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="cb46-12"><a href="#cb46-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(</span>
<span id="cb46-13"><a href="#cb46-13" 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">$</span>name,<span class="st">".png"</span>)</span>
<span id="cb46-14"><a href="#cb46-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">plot=</span>gi<span class="sc">$</span>plot</span>
<span id="cb46-15"><a href="#cb46-15" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb46-16"><a href="#cb46-16" aria-hidden="true" tabindex="-1"></a> gx <span class="ot">&lt;-</span> <span class="fu">c</span>(gx,gi)</span>
<span id="cb46-17"><a href="#cb46-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-18"><a href="#cb46-18" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get Quantiles and means for parameters</span></span>
<span id="cb46-19"><a href="#cb46-19" aria-hidden="true" tabindex="-1"></a> table <span class="ot">&lt;-</span> <span class="fu">xtable</span>(gi<span class="sc">$</span>quantiles,</span>
<span id="cb46-20"><a href="#cb46-20" aria-hidden="true" tabindex="-1"></a> <span class="at">floating=</span><span class="cn">FALSE</span></span>
<span id="cb46-21"><a href="#cb46-21" aria-hidden="true" tabindex="-1"></a> ,<span class="at">latex.environments =</span> <span class="cn">NULL</span></span>
<span id="cb46-22"><a href="#cb46-22" aria-hidden="true" tabindex="-1"></a> ,<span class="at">booktabs =</span> <span class="cn">TRUE</span></span>
<span id="cb46-23"><a href="#cb46-23" aria-hidden="true" tabindex="-1"></a> ,<span class="at">zap=</span><span class="fu">getOption</span>(<span class="st">"digits"</span>)</span>
<span id="cb46-24"><a href="#cb46-24" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb46-25"><a href="#cb46-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">write_lines</span>(table,<span class="fu">paste0</span>(<span class="st">"./latex_output/DirectEffects/group_"</span>,gi<span class="sc">$</span>name,<span class="st">".tex"</span>))</span>
<span id="cb46-26"><a href="#cb46-26" 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>
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<pre><code>Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
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<pre><code>[1] 2</code></pre>
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<pre><code>Warning: Removed 2 rows containing missing values or values outside the scale range
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<pre><code>[1] 4</code></pre>
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<pre><code>Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
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<pre><code>[1] 5</code></pre>
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<pre><code>Saving 7 x 5 in image</code></pre>
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<pre><code>Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
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<pre><code>[1] 6</code></pre>
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<pre><code>Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
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<pre><code>[1] 7</code></pre>
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<pre><code>Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
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<pre><code>[1] 11</code></pre>
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<pre><code>Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
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<pre><code>[1] 12</code></pre>
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<pre><code>Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
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<pre><code>[1] 13</code></pre>
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<div class="cell">
<div class="sourceCode cell-code" id="cb73"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" aria-hidden="true" tabindex="-1"></a>px <span class="ot">&lt;-</span> <span class="fu">c</span>()</span>
<span id="cb73-2"><a href="#cb73-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb73-3"><a href="#cb73-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb73-4"><a href="#cb73-4" 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="cb73-5"><a href="#cb73-5" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb73-6"><a href="#cb73-6" aria-hidden="true" tabindex="-1"></a> <span class="co">#Print parameter distributions</span></span>
<span id="cb73-7"><a href="#cb73-7" 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="cb73-8"><a href="#cb73-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(</span>
<span id="cb73-9"><a href="#cb73-9" 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">$</span>name,<span class="st">".png"</span>)</span>
<span id="cb73-10"><a href="#cb73-10" aria-hidden="true" tabindex="-1"></a> ,<span class="at">plot=</span>pi<span class="sc">$</span>plot</span>
<span id="cb73-11"><a href="#cb73-11" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb73-12"><a href="#cb73-12" aria-hidden="true" tabindex="-1"></a> px <span class="ot">&lt;-</span> <span class="fu">c</span>(px,pi)</span>
<span id="cb73-13"><a href="#cb73-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb73-14"><a href="#cb73-14" aria-hidden="true" tabindex="-1"></a> <span class="co">#Get Quantiles and means for parameters</span></span>
<span id="cb73-15"><a href="#cb73-15" aria-hidden="true" tabindex="-1"></a> table <span class="ot">&lt;-</span> <span class="fu">xtable</span>(pi<span class="sc">$</span>quantiles,</span>
<span id="cb73-16"><a href="#cb73-16" aria-hidden="true" tabindex="-1"></a> <span class="at">floating=</span><span class="cn">FALSE</span></span>
<span id="cb73-17"><a href="#cb73-17" aria-hidden="true" tabindex="-1"></a> ,<span class="at">latex.environments =</span> <span class="cn">NULL</span></span>
<span id="cb73-18"><a href="#cb73-18" aria-hidden="true" tabindex="-1"></a> ,<span class="at">booktabs =</span> <span class="cn">TRUE</span></span>
<span id="cb73-19"><a href="#cb73-19" aria-hidden="true" tabindex="-1"></a> ,<span class="at">zap=</span><span class="fu">getOption</span>(<span class="st">"digits"</span>)</span>
<span id="cb73-20"><a href="#cb73-20" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb73-21"><a href="#cb73-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">write_lines</span>(table,<span class="fu">paste0</span>(<span class="st">"./latex_output/DirectEffects/parameters_"</span>,i,<span class="st">"_"</span>,pi<span class="sc">$</span>name,<span class="st">".tex"</span>))</span>
<span id="cb73-22"><a href="#cb73-22" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb73-23"><a href="#cb73-23" 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</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></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</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_vline()`).</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-stderr">
<pre><code>Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_vline()`).</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-stderr">
<pre><code>Warning: Removed 5 rows containing missing values or values outside the scale range
(`geom_vline()`).</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-stderr">
<pre><code>Warning: Removed 5 rows containing missing values or values outside the scale range
(`geom_vline()`).</code></pre>
</div>
</div>
<p>Note these have 95% outer CI and 80% inner (shaded)</p>
<div class="cell">
<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="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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-13-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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/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 id="counterfactuals" class="level1">
<h1>Counterfactuals</h1>
<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>generated_ib <span class="ot">&lt;-</span> <span class="fu">gqs</span>(</span>
<span id="cb87-2"><a href="#cb87-2" aria-hidden="true" tabindex="-1"></a> fit<span class="sc">@</span>stanmodel,</span>
<span id="cb87-3"><a href="#cb87-3" aria-hidden="true" tabindex="-1"></a> <span class="at">data=</span>counterfact_delay,</span>
<span id="cb87-4"><a href="#cb87-4" aria-hidden="true" tabindex="-1"></a> <span class="at">draws=</span><span class="fu">as.matrix</span>(fit),</span>
<span id="cb87-5"><a href="#cb87-5" aria-hidden="true" tabindex="-1"></a> <span class="at">seed=</span><span class="dv">11021585</span></span>
<span id="cb87-6"><a href="#cb87-6" 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="cb88"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" aria-hidden="true" tabindex="-1"></a>df_ib_p <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb88-2"><a href="#cb88-2" 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">$</span>p_prior)</span>
<span id="cb88-3"><a href="#cb88-3" aria-hidden="true" tabindex="-1"></a> ,<span class="at">p_predicted =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"p_predicted"</span>)<span class="sc">$</span>p_predicted)</span>
<span id="cb88-4"><a href="#cb88-4" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb88-5"><a href="#cb88-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb88-6"><a href="#cb88-6" aria-hidden="true" tabindex="-1"></a>df_ib_prior <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb88-7"><a href="#cb88-7" 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">$</span>mu_prior)</span>
<span id="cb88-8"><a href="#cb88-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">sigma_prior =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"sigma_prior"</span>)<span class="sc">$</span>sigma_prior)</span>
<span id="cb88-9"><a href="#cb88-9" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb88-10"><a href="#cb88-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb88-11"><a href="#cb88-11" aria-hidden="true" tabindex="-1"></a><span class="co">#p_prior</span></span>
<span id="cb88-12"><a href="#cb88-12" 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="cb88-13"><a href="#cb88-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb88-14"><a href="#cb88-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb88-15"><a href="#cb88-15" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Implied Prior Distribution P"</span></span>
<span id="cb88-16"><a href="#cb88-16" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">""</span></span>
<span id="cb88-17"><a href="#cb88-17" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb88-18"><a href="#cb88-18" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb88-19"><a href="#cb88-19" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-15-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb89"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb89-1"><a href="#cb89-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/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="cb91"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb91-1"><a href="#cb91-1" aria-hidden="true" tabindex="-1"></a><span class="co">#p_posterior</span></span>
<span id="cb91-2"><a href="#cb91-2" 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="cb91-3"><a href="#cb91-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb91-4"><a href="#cb91-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb91-5"><a href="#cb91-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Implied Posterior Distribution P"</span></span>
<span id="cb91-6"><a href="#cb91-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">""</span></span>
<span id="cb91-7"><a href="#cb91-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb91-8"><a href="#cb91-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb91-9"><a href="#cb91-9" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-15-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb92"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/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="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><span class="co">#mu_prior</span></span>
<span id="cb94-2"><a href="#cb94-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df_ib_prior) <span class="sc">+</span></span>
<span id="cb94-3"><a href="#cb94-3" 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="cb94-4"><a href="#cb94-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb94-5"><a href="#cb94-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Prior - Mu"</span></span>
<span id="cb94-6"><a href="#cb94-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"same prior for all Mu values"</span></span>
<span id="cb94-7"><a href="#cb94-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Mu"</span></span>
<span id="cb94-8"><a href="#cb94-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability"</span></span>
<span id="cb94-9"><a href="#cb94-9" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-15-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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">"./EffectsOfEnrollmentDelay/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="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="co">#sigma_posterior</span></span>
<span id="cb97-2"><a href="#cb97-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df_ib_prior) <span class="sc">+</span></span>
<span id="cb97-3"><a href="#cb97-3" 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="cb97-4"><a href="#cb97-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb97-5"><a href="#cb97-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Prior - Sigma"</span></span>
<span id="cb97-6"><a href="#cb97-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"same prior for all Sigma values"</span></span>
<span id="cb97-7"><a href="#cb97-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Sigma"</span></span>
<span id="cb97-8"><a href="#cb97-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability"</span></span>
<span id="cb97-9"><a href="#cb97-9" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-15-4.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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">"./EffectsOfEnrollmentDelay/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="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">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 1: E-BFMI = 0.178
Chain 2: E-BFMI = 0.189
E-BFMI below 0.2 indicates you may need to reparameterize your model.</code></pre>
</div>
</div>
<section id="intervention-delay-close-of-enrollment" class="level3">
<h3 class="anchored" data-anchor-id="intervention-delay-close-of-enrollment">Intervention: Delay close of enrollment</h3>
<div class="cell">
<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>counterfact_predicted_ib <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb107-2"><a href="#cb107-2" 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">$</span>p_predicted_default)</span>
<span id="cb107-3"><a href="#cb107-3" aria-hidden="true" tabindex="-1"></a> ,<span class="at">p_predicted_intervention =</span> <span class="fu">as.vector</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"p_predicted_intervention"</span>)<span class="sc">$</span>p_predicted_intervention)</span>
<span id="cb107-4"><a href="#cb107-4" 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="cb107-5"><a href="#cb107-5" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb107-6"><a href="#cb107-6" aria-hidden="true" tabindex="-1"></a></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="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="cb107-9"><a href="#cb107-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</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">"Predicted Distribution of 'p'"</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: None"</span></span>
<span id="cb107-13"><a href="#cb107-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</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></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-17-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb108"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb108-1"><a href="#cb108-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/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="cb110"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb110-1"><a href="#cb110-1" 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="cb110-2"><a href="#cb110-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb110-3"><a href="#cb110-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb110-4"><a href="#cb110-4" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of 'p'"</span></span>
<span id="cb110-5"><a href="#cb110-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: Delay close of enrollment"</span></span>
<span id="cb110-6"><a href="#cb110-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Probability Domain 'p'"</span></span>
<span id="cb110-7"><a href="#cb110-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb110-8"><a href="#cb110-8" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-17-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/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="cb113"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb113-1"><a href="#cb113-1" 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="cb113-2"><a href="#cb113-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>() <span class="sc">+</span> </span>
<span id="cb113-3"><a href="#cb113-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb113-4"><a href="#cb113-4" aria-hidden="true" tabindex="-1"></a> <span class="at">title=</span><span class="st">"Predicted Distribution of differences 'p'"</span></span>
<span id="cb113-5"><a href="#cb113-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle=</span><span class="st">"Intervention: Delay close of enrollment"</span></span>
<span id="cb113-6"><a href="#cb113-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x=</span><span class="st">"Difference in 'p' under treatment"</span></span>
<span id="cb113-7"><a href="#cb113-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y=</span><span class="st">"Probability Density"</span></span>
<span id="cb113-8"><a href="#cb113-8" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-17-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<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">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/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="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>get_category_count <span class="ot">&lt;-</span> <span class="cf">function</span>(tbl, id) {</span>
<span id="cb116-2"><a href="#cb116-2" aria-hidden="true" tabindex="-1"></a> result <span class="ot">&lt;-</span> tbl<span class="sc">$</span>n[tbl<span class="sc">$</span>category_id <span class="sc">==</span> id]</span>
<span id="cb116-3"><a href="#cb116-3" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span>(<span class="fu">length</span>(result) <span class="sc">==</span> <span class="dv">0</span>) <span class="dv">0</span> <span class="cf">else</span> result</span>
<span id="cb116-4"><a href="#cb116-4" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb116-5"><a href="#cb116-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb116-6"><a href="#cb116-6" aria-hidden="true" tabindex="-1"></a>category_names <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(beta_list<span class="sc">$</span>groups), </span>
<span id="cb116-7"><a href="#cb116-7" aria-hidden="true" tabindex="-1"></a> <span class="cf">function</span>(i) <span class="fu">sprintf</span>(<span class="st">"ICD-10 #%d: %s (n=%d)"</span>, </span>
<span id="cb116-8"><a href="#cb116-8" aria-hidden="true" tabindex="-1"></a> i, </span>
<span id="cb116-9"><a href="#cb116-9" aria-hidden="true" tabindex="-1"></a> beta_list<span class="sc">$</span>groups[i],</span>
<span id="cb116-10"><a href="#cb116-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">get_category_count</span>(category_count, i)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb117"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb117-1"><a href="#cb117-1" aria-hidden="true" tabindex="-1"></a>pddf_ib <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">extract</span>(generated_ib, <span class="at">pars=</span><span class="st">"predicted_difference"</span>)<span class="sc">$</span>predicted_difference) <span class="sc">|&gt;</span></span>
<span id="cb117-2"><a href="#cb117-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(X1<span class="sc">:</span>X168) <span class="co">#CHANGE_NOTE: moved from X169 to X168</span></span>
<span id="cb117-3"><a href="#cb117-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb117-4"><a href="#cb117-4" aria-hidden="true" tabindex="-1"></a><span class="co">#TODO: Fix Category names</span></span>
<span id="cb117-5"><a href="#cb117-5" 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">$</span>name))</span>
<span id="cb117-6"><a href="#cb117-6" aria-hidden="true" tabindex="-1"></a>pddf_ib[<span class="st">"category"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(pddf_ib<span class="sc">$</span>entry_idx, <span class="cf">function</span>(i) df<span class="sc">$</span>category_id[i])</span>
<span id="cb117-7"><a href="#cb117-7" aria-hidden="true" tabindex="-1"></a>pddf_ib[<span class="st">"category_name"</span>] <span class="ot">&lt;-</span> <span class="fu">sapply</span>(</span>
<span id="cb117-8"><a href="#cb117-8" aria-hidden="true" tabindex="-1"></a> pddf_ib<span class="sc">$</span>category, </span>
<span id="cb117-9"><a href="#cb117-9" aria-hidden="true" tabindex="-1"></a> <span class="cf">function</span>(i) category_names[i]</span>
<span id="cb117-10"><a href="#cb117-10" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb117-11"><a href="#cb117-11" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb117-12"><a href="#cb117-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb117-13"><a href="#cb117-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb117-14"><a href="#cb117-14" 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="cb117-15"><a href="#cb117-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="at">adjust=</span><span class="dv">1</span><span class="sc">/</span><span class="dv">5</span>) <span class="sc">+</span></span>
<span id="cb117-16"><a href="#cb117-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb117-17"><a href="#cb117-17" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences"</span></span>
<span id="cb117-18"><a href="#cb117-18" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: Delay close of enrollment"</span></span>
<span id="cb117-19"><a href="#cb117-19" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb117-20"><a href="#cb117-20" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Probability Density"</span></span>
<span id="cb117-21"><a href="#cb117-21" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb117-22"><a href="#cb117-22" 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-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-19-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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="co">#todo: add median, mean, 40/60 quantiles as well as </span></span>
<span id="cb118-2"><a href="#cb118-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_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="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="fu">ggplot</span>(pddf_ib, <span class="fu">aes</span>(<span class="at">x=</span>value,)) <span class="sc">+</span></span>
<span id="cb120-2"><a href="#cb120-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_density</span>(<span class="at">adjust=</span><span class="dv">1</span><span class="sc">/</span><span class="dv">5</span>) <span class="sc">+</span></span>
<span id="cb120-3"><a href="#cb120-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb120-4"><a href="#cb120-4" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span><span class="fu">factor</span>(</span>
<span id="cb120-5"><a href="#cb120-5" aria-hidden="true" tabindex="-1"></a> category_name, </span>
<span id="cb120-6"><a href="#cb120-6" aria-hidden="true" tabindex="-1"></a> <span class="at">levels=</span>category_names</span>
<span id="cb120-7"><a href="#cb120-7" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb120-8"><a href="#cb120-8" 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="cb120-9"><a href="#cb120-9" aria-hidden="true" tabindex="-1"></a> , <span class="at">ncol=</span><span class="dv">4</span>) <span class="sc">+</span></span>
<span id="cb120-10"><a href="#cb120-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb120-11"><a href="#cb120-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribution of predicted differences | By Group"</span></span>
<span id="cb120-12"><a href="#cb120-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">subtitle =</span> <span class="st">"Intervention: Delay close of enrollment"</span></span>
<span id="cb120-13"><a href="#cb120-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb120-14"><a href="#cb120-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Probability Density"</span></span>
<span id="cb120-15"><a href="#cb120-15" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb120-16"><a href="#cb120-16" 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="cb120-17"><a href="#cb120-17" 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-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-19-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<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><span class="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_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="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><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="cb123-2"><a href="#cb123-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">bins=</span><span class="dv">300</span>) <span class="sc">+</span></span>
<span id="cb123-3"><a href="#cb123-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb123-4"><a href="#cb123-4" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span><span class="fu">factor</span>(</span>
<span id="cb123-5"><a href="#cb123-5" aria-hidden="true" tabindex="-1"></a> category_name, </span>
<span id="cb123-6"><a href="#cb123-6" aria-hidden="true" tabindex="-1"></a> <span class="at">levels=</span>category_names</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="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">TRUE</span>)</span>
<span id="cb123-9"><a href="#cb123-9" aria-hidden="true" tabindex="-1"></a> , <span class="at">ncol=</span><span class="dv">4</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">"Histogram of predicted differences | By Group"</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: Delay close of enrollment"</span></span>
<span id="cb123-13"><a href="#cb123-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</span></span>
<span id="cb123-14"><a href="#cb123-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Predicted counts"</span></span>
<span id="cb123-15"><a href="#cb123-15" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb123-16"><a href="#cb123-16" 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="cb123-17"><a href="#cb123-17" 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-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-19-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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">"./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_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>
<div class="cell">
<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>p3 <span class="ot">&lt;-</span> <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="cb126-2"><a href="#cb126-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">bins=</span><span class="dv">500</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">"Distribution of predicted differences"</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: Delay close of enrollment"</span></span>
<span id="cb126-6"><a href="#cb126-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Difference in probability due to intervention"</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 class="at">caption =</span> <span class="st">"Vertical marks: 5/10/25/50/75/90/95th percentiles. Dot shows mean."</span></span>
<span id="cb126-9"><a href="#cb126-9" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span> </span>
<span id="cb126-10"><a href="#cb126-10" 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="cb126-11"><a href="#cb126-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb126-12"><a href="#cb126-12" aria-hidden="true" tabindex="-1"></a>stats <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb126-13"><a href="#cb126-13" aria-hidden="true" tabindex="-1"></a> <span class="at">p5 =</span> <span class="fu">quantile</span>(pddf_ib<span class="sc">$</span>value, <span class="fl">0.05</span>),</span>
<span id="cb126-14"><a href="#cb126-14" aria-hidden="true" tabindex="-1"></a> <span class="at">p10 =</span> <span class="fu">quantile</span>(pddf_ib<span class="sc">$</span>value, <span class="fl">0.10</span>),</span>
<span id="cb126-15"><a href="#cb126-15" aria-hidden="true" tabindex="-1"></a> <span class="at">q1 =</span> <span class="fu">quantile</span>(pddf_ib<span class="sc">$</span>value, <span class="fl">0.25</span>),</span>
<span id="cb126-16"><a href="#cb126-16" aria-hidden="true" tabindex="-1"></a> <span class="at">med =</span> <span class="fu">median</span>(pddf_ib<span class="sc">$</span>value),</span>
<span id="cb126-17"><a href="#cb126-17" aria-hidden="true" tabindex="-1"></a> <span class="at">mean =</span> <span class="fu">mean</span>(pddf_ib<span class="sc">$</span>value),</span>
<span id="cb126-18"><a href="#cb126-18" aria-hidden="true" tabindex="-1"></a> <span class="at">q3 =</span> <span class="fu">quantile</span>(pddf_ib<span class="sc">$</span>value, <span class="fl">0.75</span>),</span>
<span id="cb126-19"><a href="#cb126-19" aria-hidden="true" tabindex="-1"></a> <span class="at">p90 =</span> <span class="fu">quantile</span>(pddf_ib<span class="sc">$</span>value, <span class="fl">0.90</span>),</span>
<span id="cb126-20"><a href="#cb126-20" aria-hidden="true" tabindex="-1"></a> <span class="at">p95 =</span> <span class="fu">quantile</span>(pddf_ib<span class="sc">$</span>value, <span class="fl">0.95</span>),</span>
<span id="cb126-21"><a href="#cb126-21" aria-hidden="true" tabindex="-1"></a> <span class="at">max_height =</span> <span class="fu">max</span>(<span class="fu">ggplot_build</span>(p3)<span class="sc">$</span>data[[<span class="dv">1</span>]]<span class="sc">$</span>count),</span>
<span id="cb126-22"><a href="#cb126-22" aria-hidden="true" tabindex="-1"></a> <span class="at">y_offset =</span> <span class="sc">-</span><span class="fu">max</span>(<span class="fu">ggplot_build</span>(p3)<span class="sc">$</span>data[[<span class="dv">1</span>]]<span class="sc">$</span>count) <span class="sc">*</span> <span class="fl">0.05</span></span>
<span id="cb126-23"><a href="#cb126-23" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb126-24"><a href="#cb126-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb126-25"><a href="#cb126-25" aria-hidden="true" tabindex="-1"></a>p3 <span class="sc">+</span> </span>
<span id="cb126-26"><a href="#cb126-26" aria-hidden="true" tabindex="-1"></a> <span class="co"># Box</span></span>
<span id="cb126-27"><a href="#cb126-27" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_segment</span>(<span class="at">data =</span> <span class="fu">data.frame</span>(</span>
<span id="cb126-28"><a href="#cb126-28" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">c</span>(stats<span class="sc">$</span>q1, stats<span class="sc">$</span>q3, stats<span class="sc">$</span>med),</span>
<span id="cb126-29"><a href="#cb126-29" aria-hidden="true" tabindex="-1"></a> <span class="at">xend =</span> <span class="fu">c</span>(stats<span class="sc">$</span>q1, stats<span class="sc">$</span>q3, stats<span class="sc">$</span>med),</span>
<span id="cb126-30"><a href="#cb126-30" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>y_offset, <span class="dv">3</span>), </span>
<span id="cb126-31"><a href="#cb126-31" aria-hidden="true" tabindex="-1"></a> <span class="at">yend =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="dv">2</span>, <span class="dv">3</span>)</span>
<span id="cb126-32"><a href="#cb126-32" aria-hidden="true" tabindex="-1"></a> ), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">xend =</span> xend, <span class="at">y =</span> y, <span class="at">yend =</span> yend)) <span class="sc">+</span></span>
<span id="cb126-33"><a href="#cb126-33" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_segment</span>(<span class="at">data =</span> <span class="fu">data.frame</span>(</span>
<span id="cb126-34"><a href="#cb126-34" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>q1, <span class="dv">2</span>),</span>
<span id="cb126-35"><a href="#cb126-35" aria-hidden="true" tabindex="-1"></a> <span class="at">xend =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>q3, <span class="dv">2</span>),</span>
<span id="cb126-36"><a href="#cb126-36" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="fu">c</span>(stats<span class="sc">$</span>y_offset, stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="dv">2</span>),</span>
<span id="cb126-37"><a href="#cb126-37" aria-hidden="true" tabindex="-1"></a> <span class="at">yend =</span> <span class="fu">c</span>(stats<span class="sc">$</span>y_offset, stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="dv">2</span>)</span>
<span id="cb126-38"><a href="#cb126-38" aria-hidden="true" tabindex="-1"></a> ), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">xend =</span> xend, <span class="at">y =</span> y, <span class="at">yend =</span> yend)) <span class="sc">+</span></span>
<span id="cb126-39"><a href="#cb126-39" aria-hidden="true" tabindex="-1"></a> <span class="co"># Inner whiskers (Q1-&gt;P10, Q3-&gt;P90)</span></span>
<span id="cb126-40"><a href="#cb126-40" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_segment</span>(<span class="at">data =</span> <span class="fu">data.frame</span>(</span>
<span id="cb126-41"><a href="#cb126-41" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">c</span>(stats<span class="sc">$</span>q1, stats<span class="sc">$</span>q3),</span>
<span id="cb126-42"><a href="#cb126-42" aria-hidden="true" tabindex="-1"></a> <span class="at">xend =</span> <span class="fu">c</span>(stats<span class="sc">$</span>p10, stats<span class="sc">$</span>p90),</span>
<span id="cb126-43"><a href="#cb126-43" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.5</span>, <span class="dv">2</span>),</span>
<span id="cb126-44"><a href="#cb126-44" aria-hidden="true" tabindex="-1"></a> <span class="at">yend =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.5</span>, <span class="dv">2</span>)</span>
<span id="cb126-45"><a href="#cb126-45" aria-hidden="true" tabindex="-1"></a> ), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">xend =</span> xend, <span class="at">y =</span> y, <span class="at">yend =</span> yend)) <span class="sc">+</span></span>
<span id="cb126-46"><a href="#cb126-46" aria-hidden="true" tabindex="-1"></a> <span class="co"># Crossbars at P10/P90</span></span>
<span id="cb126-47"><a href="#cb126-47" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_segment</span>(<span class="at">data =</span> <span class="fu">data.frame</span>(</span>
<span id="cb126-48"><a href="#cb126-48" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">c</span>(stats<span class="sc">$</span>p10, stats<span class="sc">$</span>p90),</span>
<span id="cb126-49"><a href="#cb126-49" aria-hidden="true" tabindex="-1"></a> <span class="at">xend =</span> <span class="fu">c</span>(stats<span class="sc">$</span>p10, stats<span class="sc">$</span>p90),</span>
<span id="cb126-50"><a href="#cb126-50" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.3</span>,</span>
<span id="cb126-51"><a href="#cb126-51" aria-hidden="true" tabindex="-1"></a> <span class="at">yend =</span> stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.7</span></span>
<span id="cb126-52"><a href="#cb126-52" aria-hidden="true" tabindex="-1"></a> ), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">xend =</span> xend, <span class="at">y =</span> y, <span class="at">yend =</span> yend)) <span class="sc">+</span></span>
<span id="cb126-53"><a href="#cb126-53" aria-hidden="true" tabindex="-1"></a> <span class="co"># Outer whiskers (P10-&gt;P5, P90-&gt;P95)</span></span>
<span id="cb126-54"><a href="#cb126-54" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_segment</span>(<span class="at">data =</span> <span class="fu">data.frame</span>(</span>
<span id="cb126-55"><a href="#cb126-55" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">c</span>(stats<span class="sc">$</span>p10, stats<span class="sc">$</span>p90),</span>
<span id="cb126-56"><a href="#cb126-56" aria-hidden="true" tabindex="-1"></a> <span class="at">xend =</span> <span class="fu">c</span>(stats<span class="sc">$</span>p5, stats<span class="sc">$</span>p95),</span>
<span id="cb126-57"><a href="#cb126-57" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.5</span>, <span class="dv">2</span>),</span>
<span id="cb126-58"><a href="#cb126-58" aria-hidden="true" tabindex="-1"></a> <span class="at">yend =</span> <span class="fu">rep</span>(stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.5</span>, <span class="dv">2</span>)</span>
<span id="cb126-59"><a href="#cb126-59" aria-hidden="true" tabindex="-1"></a> ), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">xend =</span> xend, <span class="at">y =</span> y, <span class="at">yend =</span> yend)) <span class="sc">+</span></span>
<span id="cb126-60"><a href="#cb126-60" aria-hidden="true" tabindex="-1"></a> <span class="co"># Crossbars at P5/P95</span></span>
<span id="cb126-61"><a href="#cb126-61" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_segment</span>(<span class="at">data =</span> <span class="fu">data.frame</span>(</span>
<span id="cb126-62"><a href="#cb126-62" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">c</span>(stats<span class="sc">$</span>p5, stats<span class="sc">$</span>p95),</span>
<span id="cb126-63"><a href="#cb126-63" aria-hidden="true" tabindex="-1"></a> <span class="at">xend =</span> <span class="fu">c</span>(stats<span class="sc">$</span>p5, stats<span class="sc">$</span>p95),</span>
<span id="cb126-64"><a href="#cb126-64" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.3</span>,</span>
<span id="cb126-65"><a href="#cb126-65" aria-hidden="true" tabindex="-1"></a> <span class="at">yend =</span> stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.7</span></span>
<span id="cb126-66"><a href="#cb126-66" aria-hidden="true" tabindex="-1"></a> ), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">xend =</span> xend, <span class="at">y =</span> y, <span class="at">yend =</span> yend)) <span class="sc">+</span></span>
<span id="cb126-67"><a href="#cb126-67" aria-hidden="true" tabindex="-1"></a> <span class="co"># Mean dot</span></span>
<span id="cb126-68"><a href="#cb126-68" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">data =</span> <span class="fu">data.frame</span>(</span>
<span id="cb126-69"><a href="#cb126-69" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> stats<span class="sc">$</span>mean,</span>
<span id="cb126-70"><a href="#cb126-70" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> stats<span class="sc">$</span>y_offset <span class="sc">*</span> <span class="fl">1.5</span></span>
<span id="cb126-71"><a href="#cb126-71" aria-hidden="true" tabindex="-1"></a> ), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">y =</span> y))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-20-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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">"./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_histdiff_boxplot.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="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>(pddf_ib, <span class="fu">aes</span>(<span class="at">x=</span>value)) <span class="sc">+</span></span>
<span id="cb129-2"><a href="#cb129-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">stat_ecdf</span>(<span class="at">geom=</span><span class="st">'step'</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">"Cumulative distribution of predicted differences"</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: Delay close of enrollment"</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 probability of termination due to intervention"</span>,</span>
<span id="cb129-7"><a href="#cb129-7" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Cumulative Proportion"</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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-21-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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">"./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_cumulative_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>
<p>Get the % of differences in the spike around zero</p>
<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><span class="co"># get values around and above/below spike</span></span>
<span id="cb132-2"><a href="#cb132-2" aria-hidden="true" tabindex="-1"></a>width <span class="ot">&lt;-</span> <span class="fl">0.02</span></span>
<span id="cb132-3"><a href="#cb132-3" aria-hidden="true" tabindex="-1"></a>spike_band_centered_zero <span class="ot">&lt;-</span> <span class="fu">mean</span>( pddf_ib<span class="sc">$</span>value <span class="sc">&gt;=</span> <span class="sc">-</span>width<span class="sc">/</span><span class="dv">2</span> <span class="sc">&amp;</span> pddf_ib<span class="sc">$</span>value <span class="sc">&lt;=</span> width<span class="sc">/</span><span class="dv">2</span>)</span>
<span id="cb132-4"><a href="#cb132-4" aria-hidden="true" tabindex="-1"></a>above_spike_band <span class="ot">&lt;-</span> <span class="fu">mean</span>( pddf_ib<span class="sc">$</span>value <span class="sc">&gt;=</span> width<span class="sc">/</span><span class="dv">2</span>)</span>
<span id="cb132-5"><a href="#cb132-5" aria-hidden="true" tabindex="-1"></a>below_spike_band <span class="ot">&lt;-</span> <span class="fu">mean</span>( pddf_ib<span class="sc">$</span>value <span class="sc">&lt;=</span> <span class="sc">-</span>width<span class="sc">/</span><span class="dv">2</span>)</span>
<span id="cb132-6"><a href="#cb132-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb132-7"><a href="#cb132-7" aria-hidden="true" tabindex="-1"></a><span class="co"># get mass above and mass below zero</span></span>
<span id="cb132-8"><a href="#cb132-8" aria-hidden="true" tabindex="-1"></a>mass_below_zero <span class="ot">&lt;-</span> <span class="fu">mean</span>( pddf_ib<span class="sc">$</span>value <span class="sc">&lt;=</span> <span class="dv">0</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Looking at the spike around zero, we find that 13.09% of the probability mass is contained within the band from [-1,1]. Additionally, there was 33.4282738% of the probability above that representing those with a general increase in the probability of termination and 53.4817262% of the probability mass below the band representing a decrease in the probability of termination.</p>
<p>On average, if you keep the trial open instead of closing it, 0.6337363% of trials will see a decrease in the probability of termination, but, due to the high increase in probability of termination given termination was increased, the mean probability of termination increases by 0.0964726.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb133"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb133-1"><a href="#cb133-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 5%-iles</span></span>
<span id="cb133-2"><a href="#cb133-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb133-3"><a href="#cb133-3" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(pddf_ib<span class="sc">$</span>value)</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> Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.99850 -0.12919 -0.02259 0.09647 0.14531 1.00000 </code></pre>
</div>
<div class="sourceCode cell-code" id="cb135"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb135-1"><a href="#cb135-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create your quantiles</span></span>
<span id="cb135-2"><a href="#cb135-2" aria-hidden="true" tabindex="-1"></a>quants <span class="ot">&lt;-</span> <span class="fu">quantile</span>(pddf_ib<span class="sc">$</span>value, <span class="at">probs =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.05</span>), <span class="at">type=</span><span class="dv">4</span>)</span>
<span id="cb135-3"><a href="#cb135-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb135-4"><a href="#cb135-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert to a data frame</span></span>
<span id="cb135-5"><a href="#cb135-5" aria-hidden="true" tabindex="-1"></a>quant_df <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb135-6"><a href="#cb135-6" aria-hidden="true" tabindex="-1"></a> <span class="at">Percentile =</span> <span class="fu">names</span>(quants),</span>
<span id="cb135-7"><a href="#cb135-7" aria-hidden="true" tabindex="-1"></a> <span class="at">Value =</span> quants</span>
<span id="cb135-8"><a href="#cb135-8" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb135-9"><a href="#cb135-9" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(quant_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;"></th>
<th style="text-align: left;">Percentile</th>
<th style="text-align: right;">Value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">0%</td>
<td style="text-align: left;">0%</td>
<td style="text-align: right;">-0.9985020</td>
</tr>
<tr class="even">
<td style="text-align: left;">5%</td>
<td style="text-align: left;">5%</td>
<td style="text-align: right;">-0.3763454</td>
</tr>
<tr class="odd">
<td style="text-align: left;">10%</td>
<td style="text-align: left;">10%</td>
<td style="text-align: right;">-0.2639654</td>
</tr>
<tr class="even">
<td style="text-align: left;">15%</td>
<td style="text-align: left;">15%</td>
<td style="text-align: right;">-0.2053399</td>
</tr>
<tr class="odd">
<td style="text-align: left;">20%</td>
<td style="text-align: left;">20%</td>
<td style="text-align: right;">-0.1628793</td>
</tr>
<tr class="even">
<td style="text-align: left;">25%</td>
<td style="text-align: left;">25%</td>
<td style="text-align: right;">-0.1291890</td>
</tr>
<tr class="odd">
<td style="text-align: left;">30%</td>
<td style="text-align: left;">30%</td>
<td style="text-align: right;">-0.0980523</td>
</tr>
<tr class="even">
<td style="text-align: left;">35%</td>
<td style="text-align: left;">35%</td>
<td style="text-align: right;">-0.0734082</td>
</tr>
<tr class="odd">
<td style="text-align: left;">40%</td>
<td style="text-align: left;">40%</td>
<td style="text-align: right;">-0.0547123</td>
</tr>
<tr class="even">
<td style="text-align: left;">45%</td>
<td style="text-align: left;">45%</td>
<td style="text-align: right;">-0.0385514</td>
</tr>
<tr class="odd">
<td style="text-align: left;">50%</td>
<td style="text-align: left;">50%</td>
<td style="text-align: right;">-0.0225949</td>
</tr>
<tr class="even">
<td style="text-align: left;">55%</td>
<td style="text-align: left;">55%</td>
<td style="text-align: right;">-0.0045955</td>
</tr>
<tr class="odd">
<td style="text-align: left;">60%</td>
<td style="text-align: left;">60%</td>
<td style="text-align: right;">-0.0000394</td>
</tr>
<tr class="even">
<td style="text-align: left;">65%</td>
<td style="text-align: left;">65%</td>
<td style="text-align: right;">0.0010549</td>
</tr>
<tr class="odd">
<td style="text-align: left;">70%</td>
<td style="text-align: left;">70%</td>
<td style="text-align: right;">0.0509626</td>
</tr>
<tr class="even">
<td style="text-align: left;">75%</td>
<td style="text-align: left;">75%</td>
<td style="text-align: right;">0.1453046</td>
</tr>
<tr class="odd">
<td style="text-align: left;">80%</td>
<td style="text-align: left;">80%</td>
<td style="text-align: right;">0.3425234</td>
</tr>
<tr class="even">
<td style="text-align: left;">85%</td>
<td style="text-align: left;">85%</td>
<td style="text-align: right;">0.7084837</td>
</tr>
<tr class="odd">
<td style="text-align: left;">90%</td>
<td style="text-align: left;">90%</td>
<td style="text-align: right;">0.9250351</td>
</tr>
<tr class="even">
<td style="text-align: left;">95%</td>
<td style="text-align: left;">95%</td>
<td style="text-align: right;">0.9820456</td>
</tr>
<tr class="odd">
<td style="text-align: left;">100%</td>
<td style="text-align: left;">100%</td>
<td style="text-align: right;">1.0000000</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>There seems to be some trials that are highly suceptable to this enrollment delay. Specifically, there were some</p>
<div class="cell">
<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>n <span class="ot">=</span> <span class="fu">length</span>(counterfact_predicted_ib<span class="sc">$</span>p_predicted_intervention)</span>
<span id="cb136-2"><a href="#cb136-2" aria-hidden="true" tabindex="-1"></a>k <span class="ot">=</span> <span class="dv">100</span></span>
<span id="cb136-3"><a href="#cb136-3" aria-hidden="true" tabindex="-1"></a>simulated_terminations_intervention <span class="ot">&lt;-</span> <span class="fu">mean</span>(<span class="fu">rbinom</span>(n,k,<span class="fu">as.vector</span>(counterfact_predicted_ib<span class="sc">$</span>p_predicted_intervention)))</span>
<span id="cb136-4"><a href="#cb136-4" aria-hidden="true" tabindex="-1"></a>simulated_terminations_base <span class="ot">&lt;-</span><span class="fu">mean</span>(<span class="fu">rbinom</span>(n,k,<span class="fu">as.vector</span>(counterfact_predicted_ib<span class="sc">$</span>p_predicted_default)))</span>
<span id="cb136-5"><a href="#cb136-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb136-6"><a href="#cb136-6" aria-hidden="true" tabindex="-1"></a>simulated_percentages <span class="ot">&lt;-</span> (simulated_terminations_intervention <span class="sc">-</span> simulated_terminations_base)<span class="sc">/</span>k</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>The simulation above shows that this results in a percentage-point increase of about 9.6462744.</p>
</section>
</section>
<section id="diagnostics" class="level1">
<h1>Diagnostics</h1>
<div class="cell">
<div class="sourceCode cell-code" id="cb137"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb137-1"><a href="#cb137-1" aria-hidden="true" tabindex="-1"></a><span class="co">#trace plots</span></span>
<span id="cb137-2"><a href="#cb137-2" 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></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-25-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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">"./EffectsOfEnrollmentDelay/Images/diagnostics/trace_plot_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="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="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>) {</span>
<span id="cb140-2"><a href="#cb140-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb140-3"><a href="#cb140-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_rank_overlay</span>(</span>
<span id="cb140-4"><a href="#cb140-4" aria-hidden="true" tabindex="-1"></a> fit, </span>
<span id="cb140-5"><a href="#cb140-5" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb140-6"><a href="#cb140-6" 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="cb140-7"><a href="#cb140-7" 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="cb140-8"><a href="#cb140-8" 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="cb140-9"><a href="#cb140-9" 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="cb140-10"><a href="#cb140-10" aria-hidden="true" tabindex="-1"></a> ), </span>
<span id="cb140-11"><a href="#cb140-11" aria-hidden="true" tabindex="-1"></a> <span class="at">n_bins=</span><span class="dv">100</span></span>
<span id="cb140-12"><a href="#cb140-12" 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="cb140-13"><a href="#cb140-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_colour_ghibli_d</span>(<span class="st">"KikiMedium"</span>)</span>
<span id="cb140-14"><a href="#cb140-14" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb140-15"><a href="#cb140-15" aria-hidden="true" tabindex="-1"></a> mu_range <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-3</span>,<span class="st">"-"</span>,<span class="dv">4</span><span class="sc">*</span>i)</span>
<span id="cb140-16"><a href="#cb140-16" aria-hidden="true" tabindex="-1"></a> filename <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_mu_"</span>,mu_range,<span class="st">".png"</span>)</span>
<span id="cb140-17"><a href="#cb140-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(filename)</span>
<span id="cb140-18"><a href="#cb140-18" 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>Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-25-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-25-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-25-4.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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="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><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></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-26-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<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="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/traceplot_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 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="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>) {</span>
<span id="cb148-2"><a href="#cb148-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb148-3"><a href="#cb148-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_rank_overlay</span>(</span>
<span id="cb148-4"><a href="#cb148-4" aria-hidden="true" tabindex="-1"></a> fit, </span>
<span id="cb148-5"><a href="#cb148-5" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb148-6"><a href="#cb148-6" 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="cb148-7"><a href="#cb148-7" 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="cb148-8"><a href="#cb148-8" 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">-1</span>,<span class="st">"]"</span>),</span>
<span id="cb148-9"><a href="#cb148-9" 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="st">"]"</span>)</span>
<span id="cb148-10"><a href="#cb148-10" aria-hidden="true" tabindex="-1"></a> ), </span>
<span id="cb148-11"><a href="#cb148-11" aria-hidden="true" tabindex="-1"></a> <span class="at">n_bins=</span><span class="dv">100</span></span>
<span id="cb148-12"><a href="#cb148-12" 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="cb148-13"><a href="#cb148-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_colour_ghibli_d</span>(<span class="st">"KikiMedium"</span>)</span>
<span id="cb148-14"><a href="#cb148-14" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb148-15"><a href="#cb148-15" aria-hidden="true" tabindex="-1"></a> sigma_range <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-3</span>,<span class="st">"-"</span>,<span class="dv">4</span><span class="sc">*</span>i)</span>
<span id="cb148-16"><a href="#cb148-16" aria-hidden="true" tabindex="-1"></a> filename <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_sigma_"</span>,sigma_range,<span class="st">".png"</span>)</span>
<span id="cb148-17"><a href="#cb148-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(filename)</span>
<span id="cb148-18"><a href="#cb148-18" 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>Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-26-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-26-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-26-4.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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="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="co">#other diagnostics</span></span>
<span id="cb153-2"><a href="#cb153-2" aria-hidden="true" tabindex="-1"></a>logpost <span class="ot">&lt;-</span> <span class="fu">log_posterior</span>(fit)</span>
<span id="cb153-3"><a href="#cb153-3" aria-hidden="true" tabindex="-1"></a>nuts_prmts <span class="ot">&lt;-</span> <span class="fu">nuts_params</span>(fit)</span>
<span id="cb153-4"><a href="#cb153-4" 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="cb154"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb154-1"><a href="#cb154-1" aria-hidden="true" tabindex="-1"></a><span class="fu">color_scheme_set</span>(<span class="st">"darkgray"</span>)</span>
<span id="cb154-2"><a href="#cb154-2" 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="cb154-3"><a href="#cb154-3" 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 class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-28-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb155"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb155-1"><a href="#cb155-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_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>
<div class="cell">
<div class="sourceCode cell-code" id="cb157"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb157-1"><a href="#cb157-1" 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">3</span>) {</span>
<span id="cb157-2"><a href="#cb157-2" 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="cb157-3"><a href="#cb157-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb157-4"><a href="#cb157-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_pairs</span>(</span>
<span id="cb157-5"><a href="#cb157-5" aria-hidden="true" tabindex="-1"></a> posterior,</span>
<span id="cb157-6"><a href="#cb157-6" aria-hidden="true" tabindex="-1"></a> <span class="at">np =</span> nuts_prmts,</span>
<span id="cb157-7"><a href="#cb157-7" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb157-8"><a href="#cb157-8" aria-hidden="true" tabindex="-1"></a> mus,</span>
<span id="cb157-9"><a href="#cb157-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"lp__"</span></span>
<span id="cb157-10"><a href="#cb157-10" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb157-11"><a href="#cb157-11" 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="cb157-12"><a href="#cb157-12" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb157-13"><a href="#cb157-13" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb157-14"><a href="#cb157-14" aria-hidden="true" tabindex="-1"></a> mu_range <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-3</span>,<span class="st">"-"</span>,<span class="dv">4</span><span class="sc">*</span>i)</span>
<span id="cb157-15"><a href="#cb157-15" aria-hidden="true" tabindex="-1"></a> filename <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_mu_"</span>,mu_range,<span class="st">".png"</span>)</span>
<span id="cb157-16"><a href="#cb157-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(filename)</span>
<span id="cb157-17"><a href="#cb157-17" 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 class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-29-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-29-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-29-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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="cb161"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb161-1"><a href="#cb161-1" 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 class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-30-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb162"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb162-1"><a href="#cb162-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_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="cb164"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb164-1"><a href="#cb164-1" 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">3</span>) {</span>
<span id="cb164-2"><a href="#cb164-2" 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="cb164-3"><a href="#cb164-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb164-4"><a href="#cb164-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_pairs</span>(</span>
<span id="cb164-5"><a href="#cb164-5" aria-hidden="true" tabindex="-1"></a> posterior,</span>
<span id="cb164-6"><a href="#cb164-6" aria-hidden="true" tabindex="-1"></a> <span class="at">np =</span> nuts_prmts,</span>
<span id="cb164-7"><a href="#cb164-7" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb164-8"><a href="#cb164-8" aria-hidden="true" tabindex="-1"></a> params,</span>
<span id="cb164-9"><a href="#cb164-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"lp__"</span></span>
<span id="cb164-10"><a href="#cb164-10" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb164-11"><a href="#cb164-11" 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="cb164-12"><a href="#cb164-12" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb164-13"><a href="#cb164-13" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb164-14"><a href="#cb164-14" aria-hidden="true" tabindex="-1"></a> sigma_range <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-3</span>,<span class="st">"-"</span>,<span class="dv">4</span><span class="sc">*</span>i)</span>
<span id="cb164-15"><a href="#cb164-15" aria-hidden="true" tabindex="-1"></a> filename <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_sigma_"</span>,sigma_range,<span class="st">".png"</span>)</span>
<span id="cb164-16"><a href="#cb164-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(filename)</span>
<span id="cb164-17"><a href="#cb164-17" 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 class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-31-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-31-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-31-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</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="cb168"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb168-1"><a href="#cb168-1" 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="cb168-2"><a href="#cb168-2" 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">3</span>) {</span>
<span id="cb168-3"><a href="#cb168-3" 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="cb168-4"><a href="#cb168-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>(</span>
<span id="cb168-5"><a href="#cb168-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">mcmc_pairs</span>(</span>
<span id="cb168-6"><a href="#cb168-6" aria-hidden="true" tabindex="-1"></a> posterior,</span>
<span id="cb168-7"><a href="#cb168-7" aria-hidden="true" tabindex="-1"></a> <span class="at">np =</span> nuts_prmts,</span>
<span id="cb168-8"><a href="#cb168-8" aria-hidden="true" tabindex="-1"></a> <span class="at">pars=</span><span class="fu">c</span>(</span>
<span id="cb168-9"><a href="#cb168-9" aria-hidden="true" tabindex="-1"></a> params,</span>
<span id="cb168-10"><a href="#cb168-10" aria-hidden="true" tabindex="-1"></a> <span class="st">"lp__"</span></span>
<span id="cb168-11"><a href="#cb168-11" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb168-12"><a href="#cb168-12" 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="cb168-13"><a href="#cb168-13" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb168-14"><a href="#cb168-14" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb168-15"><a href="#cb168-15" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb168-16"><a href="#cb168-16" aria-hidden="true" tabindex="-1"></a> beta_range <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">"k_"</span>,k,<span class="st">"_i_"</span>,<span class="dv">4</span><span class="sc">*</span>i<span class="dv">-3</span>,<span class="st">"-"</span>,<span class="dv">4</span><span class="sc">*</span>i)</span>
<span id="cb168-17"><a href="#cb168-17" aria-hidden="true" tabindex="-1"></a> filename <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">"./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_beta_"</span>,beta_range,<span class="st">".png"</span>)</span>
<span id="cb168-18"><a href="#cb168-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggsave</span>(filename)</span>
<span id="cb168-19"><a href="#cb168-19" 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">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-2.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-3.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-4.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-5.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-6.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-7.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-8.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-9.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-10.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-11.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-12.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-13.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-14.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-15.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-16.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-17.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-18.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-19.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-20.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-21.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-22.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-23.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-24.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-25.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-26.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="EffectsOfEnrollmentDelay_files/figure-html/unnamed-chunk-32-27.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
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</div>
</section>
<section id="todo" class="level1">
<h1>TODO</h1>
<ul class="task-list">
<li><label><input type="checkbox">Double check data flow. (Write summary of this in human readable form)</label>
<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><label><input type="checkbox">work on LOO validation of model</label></li>
</ul>
</section>
</main>
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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";
div.style.left = 0;
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;
};
// Handle positioning of the toggle
window.addEventListener(
"resize",
throttle(() => {
elRect = undefined;
if (selectedAnnoteEl) {
selectCodeLines(selectedAnnoteEl);
}
}, 10)
);
function throttle(fn, ms) {
let throttle = false;
let timer;
return (...args) => {
if(!throttle) { // first call gets through
fn.apply(this, args);
throttle = true;
} else { // all the others get throttled
if(timer) clearTimeout(timer); // cancel #2
timer = setTimeout(() => {
fn.apply(this, args);
timer = throttle = false;
}, ms);
}
};
}
// 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>