diff --git a/Julia-turing models/Logistic_WorkingParamRecovery.jl b/Julia-turing models/Logistic_WorkingParamRecovery.jl deleted file mode 100644 index 7c997c6..0000000 --- a/Julia-turing models/Logistic_WorkingParamRecovery.jl +++ /dev/null @@ -1,1745 +0,0 @@ -### A Pluto.jl notebook ### -# v0.19.19 - -using Markdown -using InteractiveUtils - -# ╔═╡ c3854198-646b-11ed-1d90-077419a6a860 -using Turing,StatsFuns,Distributions,StatsPlots,FillArrays - -# ╔═╡ f6fc4578-43c5-4fb7-a24c-2567aa5b33c1 -using Random - -# ╔═╡ 735ae519-019e-48c6-8b73-7982fa60501a -md""" -This model is designed to recover parameters through logistic regression. - -Note that recovery depends on specifying generating the data properly. -""" - -# ╔═╡ bd9391b7-5240-46ca-be26-357292ffaddb -begin #creating synthetic data - #n = 100_000 #successfully tested with n=100_000. It recovered the values quite well. - n = 1000 - β = [1 2 -3] - α = 0 - k=length(β) - ϵ = randn(n) - X = rand(Uniform(0,1),(n,k)) - - B = α .+ (X*β') - lB=logistic.(B) - y = rand.(Bernoulli.(lB)) -end - -# ╔═╡ 9bf20953-ae30-4468-943a-ddc2898da9b3 -maximum(B),minimum(B),mean(B),std(B) - -# ╔═╡ 7fa29a18-6988-49b8-b2b4-b41a31de5757 -histogram(B) - -# ╔═╡ 9ea0c03d-65f5-41db-b455-299cb7461e51 -plot(lB,y,seriestype=:scatter,legend=nothing) - -# ╔═╡ 0696416b-72ba-46a0-ac54-0f8afbba554b -@model function Logit(y,X) - n,k = size(X) - β ~ MvNormal(zeros(k),1) - σ ~ Exponential(1) - α ~ Normal(0,1) - - μ = α .+ X*β - p = logistic.(μ) - - y .~ Bernoulli.(p) -end - -# ╔═╡ a2632230-b891-48b3-ba05-d34dc6076101 -# ╠═╡ disabled = true -#=╠═╡ -MvNormal(zeros(k),1) - ╠═╡ =# - -# ╔═╡ af054878-09ed-4c67-8655-5ff0bd0d1193 -# ╠═╡ disabled = true -#=╠═╡ -BernoulliLogit - ╠═╡ =# - -# ╔═╡ 3f4aa70d-ffce-44fe-8520-831a5a94fe47 -testmodel = Logit(y,X) - -# ╔═╡ 989b69de-e187-48f2-8454-8d86b232d895 -chain = sample(testmodel,NUTS(),2000) - 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-[[deps.x264_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" -uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" -version = "2021.5.5+0" - -[[deps.x265_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" -uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" -version = "3.5.0+0" - -[[deps.xkbcommon_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] -git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" -uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+0" -""" - -# ╔═╡ Cell order: -# ╠═c3854198-646b-11ed-1d90-077419a6a860 -# ╠═735ae519-019e-48c6-8b73-7982fa60501a -# ╠═f6fc4578-43c5-4fb7-a24c-2567aa5b33c1 -# ╠═bd9391b7-5240-46ca-be26-357292ffaddb -# ╠═9bf20953-ae30-4468-943a-ddc2898da9b3 -# ╠═7fa29a18-6988-49b8-b2b4-b41a31de5757 -# ╠═9ea0c03d-65f5-41db-b455-299cb7461e51 -# ╠═0696416b-72ba-46a0-ac54-0f8afbba554b -# ╠═a2632230-b891-48b3-ba05-d34dc6076101 -# ╠═af054878-09ed-4c67-8655-5ff0bd0d1193 -# ╠═3f4aa70d-ffce-44fe-8520-831a5a94fe47 -# ╠═989b69de-e187-48f2-8454-8d86b232d895 -# ╠═8da7994b-ac63-4583-a360-d49cc8b04d35 -# ╟─00000000-0000-0000-0000-000000000001 -# ╟─00000000-0000-0000-0000-000000000002 diff --git a/Julia-turing models/Model.jl b/Julia-turing models/Model.jl deleted file mode 100644 index 60d94d7..0000000 --- a/Julia-turing models/Model.jl +++ /dev/null @@ -1,62 +0,0 @@ - -using Turing, StatsFuns -using Distributions, StatsPlots - -using FillArrays - -begin #creating synthetic data - βs = [1 2 3]' - x = Matrix([1:10 1:2:20 1:3:30]) - X = (x .- mean(x,dims=1)) ./ std(x,dims=1) - t = [x for x=1:0.5:5.5] - s = rand([1,2],10) - σ= [2 3] - rand_draw1 = randn(10) - - y = x*βs .+ σ[s] -end - -@model function JointDurationStateModel( - DeviationFromExpectedDuration, - ConclusionStatus, - SnapshotState, - CurrentDuration, -) - # get dimensions - n,k = size(SnapshotState) - - #hyperpriors priors - - #Heirarchal parameters - #β ~ MvNormal(Fill(0,k),2) - η ~ MvNormal(Fill(0,k),2) - - #Direct Priors - #σ_DFED ~ filldist(Exponential(1),2) #TODO: check implication of this form - - #model - #μ = SnapshotState * β - p = StatsFuns.logistic.(SnapshotState * η) - - #estimate ConclusionStatus model - ConclusionStatus .~ Bernoulli(p) - #Estimate DFED model - #= - for i in eachindex(ConclusionStatus) - DeviationFromExpectedDuration ~ Normal( - μ[ConclusionStatus[i]], - σ_DFED[ConclusionStatus[i]] - ) - end - =# -end - -model = JointDurationStateModel(y,s,X,t) -prior = JointDurationStateModel(fill(missing,size(y)),fill(missing,size(s)),X,t) - -chain = sample(model,NUTS(0.85),2000) - - - -plot(chain) - diff --git a/Julia-turing models/MultivariateExample.jl b/Julia-turing models/MultivariateExample.jl deleted file mode 100644 index 4ceb812..0000000 --- a/Julia-turing models/MultivariateExample.jl +++ /dev/null @@ -1,1757 +0,0 @@ -### A Pluto.jl notebook ### -# v0.19.14 - -using Markdown -using InteractiveUtils - -# ╔═╡ add4bb20-1da8-45e7-a40f-1b41c149d5f0 -using Turing,StatsFuns, Distributions,StatsPlots,FillArrays - -# ╔═╡ c39be064-648a-11ed-0677-ebe624104c69 -md""" -# Model Description - -s: Completion status -t: time remaining -τ: Time elapsed -x₁: current status -x₂: market competitors -x₃: other value (placeholder) - -p,μ,σ = f(τ,X) -A,B = f(μ,σ) -(s,t) ~ (bernoulli(p), Gamma(A,B)) -""" - -# ╔═╡ fb1ce990-915e-46eb-88a6-ac3c2bc55f4e -begin #test params - n = 100 -end - -# ╔═╡ a84568ec-f443-4b36-83ba-baf5aceae209 -function dummy_builder(x) - features = unique(x) - z = zeros(Int,(length(x),length(features))) - for (i,item) in enumerate(x), (j,feature) in enumerate(features) - if item == feature - z[i,j] = 1 - end - end - z -end - -# ╔═╡ d75ac31e-9524-4ad7-b350-0a6dabdf22d2 -begin - τ = rand(Exponential(2),n) - status = rand(["active","not recruiting","suspended"],n) - x₁ = dummy_builder(status) - x₂ = rand([0 1 2 3 4],n) - x₃ = rand([1 2 3],n) - - X = hcat(τ, x₁, x₂, x₃) -end - -# ╔═╡ 8735884f-efdc-4421-a205-06b68946b564 -begin #build statuses - ξ = [-5 6 -7 3 -2 4]' - Ξ = logistic.(X * ξ) - s = rand.(Bernoulli.(Ξ)) -end - -# ╔═╡ 7cee4dad-17b4-4cf8-8fa7-631722c4f9bf -begin #Build Durations - β = [1 2 3 4 5 1]' - ν = [6 5 4 3 2 1]' - α = [-1,1] - - - μ = X * β .+ [α[x+1] for x in s] - η = (X * ν).^2 - - - t = rand.(Normal.(μ,η)) -end - -# ╔═╡ f0687986-ac5f-47c7-ad79-07318d770d17 -plot(Ξ,s,seriestype=:scatter, legend=nothing) - -# ╔═╡ fd7264ce-00af-49a3-91c9-2277bad2b3e9 -@model function MultiVariate(s,t,X) - n,k=size(X) - - #model S - ξ ~ MvNormal(zeros(k),2) - σₛ ~ Exponential(1) - - μ = X*ξ - p = logistic.(μ) - - s .~ Bernoulli.(p) - - #model t - β ~ MvNormal(zeros(k),2) - ν ~ MvNormal(zeros(k),2) - α ~ MvNormal([0,0], 2) - - μ = X * β .+ [α[x+1] for x in s] - η = (X * ν).^2 - - - t .~ Normal.(μ,η) -end - -# ╔═╡ a9362616-b992-4100-b000-48c8ae7b4d4b -a =[1,2] - -# ╔═╡ edad9362-fbd3-445a-857d-9687c1adbddd -testmod = MultiVariate(s,t,X) - -# ╔═╡ d30d54df-260e-4826-89e3-de5434f8a730 -chain = sample(testmod,Gibbs(NUTS(),PG(20,:α)),1000) - -# ╔═╡ 846a1840-5734-4ed3-9d9c-01f063cd0057 -plot(chain) - -# ╔═╡ 00000000-0000-0000-0000-000000000001 -PLUTO_PROJECT_TOML_CONTENTS = """ -[deps] -Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" -FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b" -StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c" -StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" -Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" - -[compat] -Distributions = "~0.25.77" -FillArrays = "~0.13.5" -StatsFuns = "~1.0.1" -StatsPlots = "~0.15.4" -Turing = "~0.22.0" -""" - 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-[[deps.x264_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" -uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" -version = "2021.5.5+0" - -[[deps.x265_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" -uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" -version = "3.5.0+0" - -[[deps.xkbcommon_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] -git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" -uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+0" -""" - -# ╔═╡ Cell order: -# ╟─c39be064-648a-11ed-0677-ebe624104c69 -# ╠═add4bb20-1da8-45e7-a40f-1b41c149d5f0 -# ╠═fb1ce990-915e-46eb-88a6-ac3c2bc55f4e -# ╠═d75ac31e-9524-4ad7-b350-0a6dabdf22d2 -# ╟─a84568ec-f443-4b36-83ba-baf5aceae209 -# ╠═7cee4dad-17b4-4cf8-8fa7-631722c4f9bf -# ╠═8735884f-efdc-4421-a205-06b68946b564 -# ╠═f0687986-ac5f-47c7-ad79-07318d770d17 -# ╠═fd7264ce-00af-49a3-91c9-2277bad2b3e9 -# ╠═a9362616-b992-4100-b000-48c8ae7b4d4b -# ╠═edad9362-fbd3-445a-857d-9687c1adbddd -# ╠═d30d54df-260e-4826-89e3-de5434f8a730 -# ╠═846a1840-5734-4ed3-9d9c-01f063cd0057 -# ╟─00000000-0000-0000-0000-000000000001 -# ╟─00000000-0000-0000-0000-000000000002 diff --git a/Julia-turing models/PlutoModel.jl b/Julia-turing models/PlutoModel.jl deleted file mode 100644 index 5b56c12..0000000 --- a/Julia-turing models/PlutoModel.jl +++ /dev/null @@ -1,1799 +0,0 @@ -### A Pluto.jl notebook ### -# v0.19.19 - -using Markdown -using InteractiveUtils - -# ╔═╡ c3854198-646b-11ed-1d90-077419a6a860 -using Turing,StatsFuns,Distributions,StatsPlots,FillArrays - -# ╔═╡ 54680695-4d31-4dd9-8b3c-c0af5153abef -using LinearAlgebra - -# ╔═╡ f6fc4578-43c5-4fb7-a24c-2567aa5b33c1 -using Random - -# ╔═╡ 735ae519-019e-48c6-8b73-7982fa60501a -md""" -So it seems like I am having issues where I can't get the herarcheal parameters to work. -""" - -# ╔═╡ 166031a9-d200-48f2-93f7-0e535d0a78b7 -begin - n=1000 - enrollment_ratio = rand([0.2,0.5,0.7,1,1.1],n) - marketing = rand([0,1,2,1,5,7],n) - - x = hcat(enrollment_ratio,marketing) - k = size(x,2) - - g=2 #number of categories - category = rand(1:g,n) - - M = reshape(1:(k*g),g,k) .* 0.1 - B = M .+ randn(g,k) - - mu = -1 .+ sum(x .* B[category,:],dims=2) - p = logistic.(mu) - y = rand.(Bernoulli.(p)) -end - -# ╔═╡ 2dc7ff65-08ed-4234-b997-50d999e54b32 -M - -# ╔═╡ 2654dac7-6ed8-4625-bb62-07ef771869b6 - - -# ╔═╡ b9fb5248-fff9-4b8e-8801-71a797e4c31e - - -# ╔═╡ ec27451b-252f-406a-8d60-376f4dc79da1 - - -# ╔═╡ ef5d40d7-a9a8-40e4-8004-f40dbd58f837 - - -# ╔═╡ 0100a4ef-45d6-4ea3-b27c-93889bc09827 - - -# ╔═╡ 69e8107d-88d1-42d9-9a9b-93dbbf28bbe8 -histogram(p,bins=20) - -# ╔═╡ 069c6761-ed63-4a16-aeaf-2fc4db029dfe -histogram(y) - -# ╔═╡ be6c4cd5-a45d-42ea-845b-d7815c1e93f8 -@model function hielogit(y, cat, x,g, ::Type{T} = Float64) where {T} - n,k = size(x) - k = k+1 - x1 = ones(n) - x = hcat(x1,x) - μ ~ MvNormal(k,1) #hyperparam - - σ ~ filldist(Exponential(0.2),g) - - β = Vector{Vector{T}}(undef,g) - for i in 1:g - β[i] ~ MvNormal(μ,σ[i]) - end - - for i in 1:n - p = sum(x[1,:] .* β[cat[i]]) - y[i] ~ BernoulliLogit(p) - end -end - -# ╔═╡ 630d8bca-346d-49df-b62a-ebdd7530dd83 - - -# ╔═╡ e5c05073-7686-4053-8593-3953d8f18f16 - - -# ╔═╡ 153ae545-5baf-4c53-a76c-f22a72994096 - - -# ╔═╡ f113c911-64ab-4998-8a1c-7b17af4b2e9a -hl = hielogit(y,category,x,g) - -# ╔═╡ 37f3dadf-d07b-49c7-82fb-5ce49edc4476 -chains_of_jacob_marley = sample(hl, NUTS(),2000) - -# ╔═╡ 887d70f4-3592-4cd9-b290-66e07a4821f9 -plot(chains_of_jacob_marley) - -# ╔═╡ 09bf1ae5-3b22-4c40-a99b-7eab5aafd76d -chains_of_jacob_marley.name_map - -# ╔═╡ a4ba8cc8-e187-4841-ae2e-dbc94b1bfc76 -mean(chains_of_jacob_marley.value.data[:,1:11,1],dims=1) - -# ╔═╡ 25bf4f46-0051-4d06-8582-38dd4ac3ecdf - - -# ╔═╡ be600b5b-745b-4750-b534-a5a40461344a -chains_of_jacob_marley.value.axes[2].val[1:11] - -# ╔═╡ c518e08f-0a34-4622-bcf4-24e3f2235ed0 - - -# ╔═╡ 00000000-0000-0000-0000-000000000001 -PLUTO_PROJECT_TOML_CONTENTS = """ -[deps] -Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" -FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b" -LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" -Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" -StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c" -StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" -Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" - -[compat] -Distributions = "~0.25.77" -FillArrays = "~0.13.5" -StatsFuns = "~1.0.1" -StatsPlots = "~0.15.4" -Turing = "~0.22.0" -""" - -# ╔═╡ 00000000-0000-0000-0000-000000000002 -PLUTO_MANIFEST_TOML_CONTENTS = """ -# This file is machine-generated - 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-[[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] -uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.1.1+0" - -[[deps.libfdk_aac_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" -uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" -version = "2.0.2+0" - -[[deps.libpng_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" -uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" -version = "1.6.38+0" - -[[deps.libvorbis_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] -git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" -uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" -version = "1.3.7+1" - -[[deps.nghttp2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.48.0+0" - -[[deps.p7zip_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -version = "17.4.0+0" - -[[deps.x264_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" -uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" -version = "2021.5.5+0" - -[[deps.x265_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" -uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" -version = "3.5.0+0" - -[[deps.xkbcommon_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] -git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" -uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+0" -""" - -# ╔═╡ Cell order: -# ╠═c3854198-646b-11ed-1d90-077419a6a860 -# ╠═54680695-4d31-4dd9-8b3c-c0af5153abef -# ╠═735ae519-019e-48c6-8b73-7982fa60501a -# ╠═f6fc4578-43c5-4fb7-a24c-2567aa5b33c1 -# ╠═166031a9-d200-48f2-93f7-0e535d0a78b7 -# ╠═2dc7ff65-08ed-4234-b997-50d999e54b32 -# ╠═2654dac7-6ed8-4625-bb62-07ef771869b6 -# ╠═b9fb5248-fff9-4b8e-8801-71a797e4c31e -# ╠═ec27451b-252f-406a-8d60-376f4dc79da1 -# ╠═ef5d40d7-a9a8-40e4-8004-f40dbd58f837 -# ╠═0100a4ef-45d6-4ea3-b27c-93889bc09827 -# ╠═69e8107d-88d1-42d9-9a9b-93dbbf28bbe8 -# ╠═069c6761-ed63-4a16-aeaf-2fc4db029dfe -# ╠═be6c4cd5-a45d-42ea-845b-d7815c1e93f8 -# ╠═630d8bca-346d-49df-b62a-ebdd7530dd83 -# ╠═e5c05073-7686-4053-8593-3953d8f18f16 -# ╠═153ae545-5baf-4c53-a76c-f22a72994096 -# ╠═f113c911-64ab-4998-8a1c-7b17af4b2e9a -# ╠═37f3dadf-d07b-49c7-82fb-5ce49edc4476 -# ╠═887d70f4-3592-4cd9-b290-66e07a4821f9 -# ╠═09bf1ae5-3b22-4c40-a99b-7eab5aafd76d -# ╠═a4ba8cc8-e187-4841-ae2e-dbc94b1bfc76 -# ╠═25bf4f46-0051-4d06-8582-38dd4ac3ecdf -# ╠═be600b5b-745b-4750-b534-a5a40461344a -# ╠═c518e08f-0a34-4622-bcf4-24e3f2235ed0 -# ╟─00000000-0000-0000-0000-000000000001 -# ╟─00000000-0000-0000-0000-000000000002 diff --git a/r-analysis/EffectsOfEnrollmentDelay.html b/r-analysis/EffectsOfEnrollmentDelay.html index 34cc532..676ecae 100644 --- a/r-analysis/EffectsOfEnrollmentDelay.html +++ b/r-analysis/EffectsOfEnrollmentDelay.html @@ -2,7 +2,7 @@ - + @@ -23,7 +23,7 @@ ul.task-list li input[type="checkbox"] { } /* CSS for syntax highlighting */ pre > code.sourceCode { white-space: pre; position: relative; } -pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } +pre > code.sourceCode > span { line-height: 1.25; } pre > code.sourceCode > span:empty { height: 1.2em; } .sourceCode { overflow: visible; } code.sourceCode > span { color: inherit; text-decoration: inherit; } @@ -34,7 +34,7 @@ div.sourceCode { overflow: auto; } } @media print { pre > code.sourceCode { white-space: pre-wrap; } -pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; } +pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; } } pre.numberSource code { counter-reset: source-line 0; } @@ -99,12 +99,15 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin + +

Setup

-
library(bayesplot)
+
library(knitr)
+library(bayesplot)
This is bayesplot version 1.11.1
@@ -135,10 +138,10 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
-✔ dplyr     1.1.3     ✔ readr     2.1.4
-✔ forcats   1.0.0     ✔ stringr   1.5.0
-✔ lubridate 1.9.2     ✔ tibble    3.2.1
-✔ purrr     1.0.2     ✔ tidyr     1.3.0
+✔ 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 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
@@ -186,188 +189,190 @@ The following object is masked from 'package:tidyr':
 

 
Loading required package: DBI

 

-
driver <- dbDriver("PostgreSQL")
+
host <- 'aact_db-restored-2025-01-07'
 
-get_data <- function(driver) {
+driver <- dbDriver("PostgreSQL")
 
-con <- dbConnect(
-    driver,
-    user='root',
-    password='root',
-    dbname='aact_db',
-    host='will-office'
-    )
-on.exit(dbDisconnect(con))
-
-query <- dbSendQuery(
-    con,
-#    "select * from formatted_data_with_planned_enrollment;"
-"
-select 
-    fdqpe.nct_id
-    --,fdqpe.start_date
-    --,fdqpe.current_enrollment
-    --,fdqpe.enrollment_category
-    ,fdqpe.current_status 
-    ,fdqpe.earliest_date_observed 
-    ,fdqpe.elapsed_duration
-    ,fdqpe.n_brands as identical_brands
-    ,ntbtu.brand_name_count 
-    ,fdqpe.category_id
-    ,fdqpe.final_status
-    ,fdqpe.h_sdi_val
-    --,fdqpe.h_sdi_u95
-    --,fdqpe.h_sdi_l95
-    ,fdqpe.hm_sdi_val
-    --,fdqpe.hm_sdi_u95
-    --,fdqpe.hm_sdi_l95
-    ,fdqpe.m_sdi_val
-    --,fdqpe.m_sdi_u95
-    --,fdqpe.m_sdi_l95
-    ,fdqpe.lm_sdi_val
-    --,fdqpe.lm_sdi_u95
-    --,fdqpe.lm_sdi_l95
-    ,fdqpe.l_sdi_val
-    --,fdqpe.l_sdi_u95
-    --,fdqpe.l_sdi_l95
-from formatted_data_with_planned_enrollment fdqpe
-    join \"Formularies\".nct_to_brands_through_uspdc ntbtu
-        on fdqpe.nct_id = ntbtu.nct_id 
-order by fdqpe.nct_id, fdqpe.earliest_date_observed 
-;
-"
-    )
-df <- fetch(query, n = -1)
-df <- na.omit(df)
-
-query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
-n_categories <- fetch(query2, n = -1)
-
-return(list(data=df,ncat=n_categories))
-}
-
-
-get_counterfact_base <- function(driver) {
+get_data <- function(driver) {
+
+con <- dbConnect(
+    driver,
+    user='root',
+    password='root',
+    dbname='aact_db',
+    host=host
+    )
+on.exit(dbDisconnect(con))
+
+query <- dbSendQuery(
+    con,
+#    "select * from formatted_data_with_planned_enrollment;"
+"
+select 
+    fdqpe.nct_id
+    --,fdqpe.start_date
+    --,fdqpe.current_enrollment
+    --,fdqpe.enrollment_category
+    ,fdqpe.current_status 
+    ,fdqpe.earliest_date_observed 
+    ,fdqpe.elapsed_duration
+    ,fdqpe.n_brands as identical_brands
+    ,ntbtu.brand_name_counts 
+    ,fdqpe.category_id
+    ,fdqpe.final_status
+    ,fdqpe.h_sdi_val
+    --,fdqpe.h_sdi_u95
+    --,fdqpe.h_sdi_l95
+    ,fdqpe.hm_sdi_val
+    --,fdqpe.hm_sdi_u95
+    --,fdqpe.hm_sdi_l95
+    ,fdqpe.m_sdi_val
+    --,fdqpe.m_sdi_u95
+    --,fdqpe.m_sdi_l95
+    ,fdqpe.lm_sdi_val
+    --,fdqpe.lm_sdi_u95
+    --,fdqpe.lm_sdi_l95
+    ,fdqpe.l_sdi_val
+    --,fdqpe.l_sdi_u95
+    --,fdqpe.l_sdi_l95
+from formatted_data_with_planned_enrollment fdqpe
+    join \"Formularies\".nct_to_brand_counts_through_uspdc ntbtu
+        on fdqpe.nct_id = ntbtu.nct_id 
+order by fdqpe.nct_id, fdqpe.earliest_date_observed 
+;
+"
+    )
+df <- fetch(query, n = -1)
+df <- na.omit(df)
+
+query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
+n_categories <- fetch(query2, n = -1)
+
+return(list(data=df,ncat=n_categories))
+}
+
 
-con <- dbConnect(
-    driver,
-    user='root',
-    password='root',
-    dbname='aact_db',
-    host='will-office'
-    )
-on.exit(dbDisconnect(con))
-
-query <- dbSendQuery(
-    con,
-    "
-    with cte as (
-    --get last recruiting state
-    select fd.nct_id, max(fd.earliest_date_observed),min(fd2.earliest_date_observed) as tmstmp
-    from formatted_data fd 
-        join formatted_data fd2 
-        on fd.nct_id=fd2.nct_id and fd.earliest_date_observed < fd2.earliest_date_observed 
-    where fd.current_status = 'Recruiting'
-        and fd2.current_status = 'Active, not recruiting'
-    group by fd.nct_id 
-    )
-    select 
-        fdqpe.nct_id
-        --,fdqpe.start_date
-        --,fdqpe.current_enrollment
-        --,fdqpe.enrollment_category
-        ,fdqpe.current_status 
-        ,fdqpe.earliest_date_observed 
-        ,fdqpe.elapsed_duration
-        ,fdqpe.n_brands as identical_brands
-        ,ntbtu.brand_name_count 
-        ,fdqpe.category_id
-        ,fdqpe.final_status
-        ,fdqpe.h_sdi_val
-        --,fdqpe.h_sdi_u95
-        --,fdqpe.h_sdi_l95
-        ,fdqpe.hm_sdi_val
-        --,fdqpe.hm_sdi_u95
-        --,fdqpe.hm_sdi_l95
-        ,fdqpe.m_sdi_val
-        --,fdqpe.m_sdi_u95
-        --,fdqpe.m_sdi_l95
-        ,fdqpe.lm_sdi_val
-        --,fdqpe.lm_sdi_u95
-        --,fdqpe.lm_sdi_l95
-        ,fdqpe.l_sdi_val
-        --,fdqpe.l_sdi_u95
-        --,fdqpe.l_sdi_l95
-    from formatted_data_with_planned_enrollment fdqpe
-        join \"Formularies\".nct_to_brands_through_uspdc ntbtu
-            on fdqpe.nct_id = ntbtu.nct_id 
-        join cte 
-            on fdqpe.nct_id = cte.nct_id 
-                and fdqpe.earliest_date_observed = cte.tmstmp
-    order by fdqpe.nct_id, fdqpe.earliest_date_observed 
-    ;
-    "
-    )
-df <- fetch(query, n = -1)
-df <- na.omit(df)
-
-query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
-n_categories <- fetch(query2, n = -1)
-
-return(list(data=df,ncat=n_categories))
-}
-
-
-d <- get_data(driver)
-df <- d$data
-n_categories <- d$ncat
-
-cf <- get_counterfact_base(driver)
-df_counterfact_base <- cf$data
-
-
+get_counterfact_base <- function(driver) {
+
+con <- dbConnect(
+    driver,
+    user='root',
+    password='root',
+    dbname='aact_db',
+    host=host
+    )
+on.exit(dbDisconnect(con))
+
+query <- dbSendQuery(
+    con,
+    "
+    with cte as (
+    --get last recruiting state
+    select fd.nct_id, max(fd.earliest_date_observed),min(fd2.earliest_date_observed) as tmstmp
+    from formatted_data fd 
+        join formatted_data fd2 
+        on fd.nct_id=fd2.nct_id and fd.earliest_date_observed < fd2.earliest_date_observed 
+    where fd.current_status = 'Recruiting'
+        and fd2.current_status = 'Active, not recruiting'
+    group by fd.nct_id 
+    )
+    select 
+        fdqpe.nct_id
+        --,fdqpe.start_date
+        --,fdqpe.current_enrollment
+        --,fdqpe.enrollment_category
+        ,fdqpe.current_status 
+        ,fdqpe.earliest_date_observed 
+        ,fdqpe.elapsed_duration
+        ,fdqpe.n_brands as identical_brands
+        ,ntbtu.brand_name_counts 
+        ,fdqpe.category_id
+        ,fdqpe.final_status
+        ,fdqpe.h_sdi_val
+        --,fdqpe.h_sdi_u95
+        --,fdqpe.h_sdi_l95
+        ,fdqpe.hm_sdi_val
+        --,fdqpe.hm_sdi_u95
+        --,fdqpe.hm_sdi_l95
+        ,fdqpe.m_sdi_val
+        --,fdqpe.m_sdi_u95
+        --,fdqpe.m_sdi_l95
+        ,fdqpe.lm_sdi_val
+        --,fdqpe.lm_sdi_u95
+        --,fdqpe.lm_sdi_l95
+        ,fdqpe.l_sdi_val
+        --,fdqpe.l_sdi_u95
+        --,fdqpe.l_sdi_l95
+    from formatted_data_with_planned_enrollment fdqpe
+        join \"Formularies\".nct_to_brand_counts_through_uspdc ntbtu
+            on fdqpe.nct_id = ntbtu.nct_id 
+        join cte 
+            on fdqpe.nct_id = cte.nct_id 
+                and fdqpe.earliest_date_observed = cte.tmstmp
+    order by fdqpe.nct_id, fdqpe.earliest_date_observed 
+    ;
+    "
+    )
+df <- fetch(query, n = -1)
+df <- na.omit(df)
+
+query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
+n_categories <- fetch(query2, n = -1)
+
+return(list(data=df,ncat=n_categories))
+}
+
+
+d <- get_data(driver)
+df <- d$data
+n_categories <- d$ncat
+
+cf <- get_counterfact_base(driver)
+df_counterfact_base <- cf$data
 
-################ Format Data ###########################
+
 
-data_formatter <- function(df) {
-categories <- df["category_id"]
-
-x <- df["elapsed_duration"]
-x["identical_brands"] <- asinh(df$identical_brands)
-x["brand_name_counts"] <- asinh(df$brand_name_count)
-x["h_sdi_val"] <- asinh(df$h_sdi_val)
-x["hm_sdi_val"] <- asinh(df$hm_sdi_val)
-x["m_sdi_val"] <- asinh(df$m_sdi_val)
-x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
-x["l_sdi_val"] <- asinh(df$l_sdi_val)
-
-
-#Setup fixed effects
-x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
-x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
-x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) 
-x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
-
-
-y <- ifelse(df["final_status"]=="Terminated",1,0)
+################ Format Data ###########################
+
+data_formatter <- function(df) {
+categories <- df["category_id"]
+
+x <- df["elapsed_duration"]
+x["identical_brands"] <- asinh(df$identical_brands)
+x["brand_name_counts"] <- asinh(df$brand_name_count)
+x["h_sdi_val"] <- asinh(df$h_sdi_val)
+x["hm_sdi_val"] <- asinh(df$hm_sdi_val)
+x["m_sdi_val"] <- asinh(df$m_sdi_val)
+x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
+x["l_sdi_val"] <- asinh(df$l_sdi_val)
+
+
+#Setup fixed effects
+x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
+x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
+x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) 
+x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
+
 
-#get category list
+y <- ifelse(df["final_status"]=="Terminated",1,0)
 
-
-return(list(x=x,y=y))
-}
-
-train <- data_formatter(df)
-counterfact_base <- data_formatter(df_counterfact_base)
-
-categories <- df$category_id
+#get category list
+
+
+return(list(x=x,y=y))
+}
+
+train <- data_formatter(df)
+counterfact_base <- data_formatter(df_counterfact_base)
 
-x <- train$x
-y <- train$y
-
-x_cf_base <- counterfact_base$x
-y_cf_base <- counterfact_base$y
-cf_categories <- df_counterfact_base$category_id

+categories <- df$category_id
+
+x <- train$x
+y <- train$y
+
+x_cf_base <- counterfact_base$x
+y_cf_base <- counterfact_base$y
+cf_categories <- df_counterfact_base$category_id
@@ -383,7 +388,7 @@ The following object is masked from 'package:tidyr': ,"m_sdi_val" ,"lm_sdi_val" ,"l_sdi_val" - ,"status_NYR" + ,"status_NYR"# TODO: may need to remove ,"status_EBI" ,"status_Rec" ,"status_ANR" @@ -470,66 +475,68 @@ The following object is masked from 'package:tidyr': rename=TRUE, filter=NULL ) { - #get all parameter names - params <- get_parameters(stem,class_list) - - #filter down to parameters of interest - params <- filter(params,groups == group_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr - #get group name for title - group_name <- class_list$groups[group_id] - #create area plot with appropriate title - p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) + - geom_vline(xintercept=0,color="grey",alpha=0.75) - - d <- pivot_longer(filtdata, everything()) |> - group_by(name) |> - summarize( - mean=mean(value) - ,q025 = quantile(value,probs = 0.025) - ,q975 = quantile(value,probs = 0.975) - ,q05 = quantile(value,probs = 0.05) - ,q95 = quantile(value,probs = 0.95) - ) - return(list(plot=p,quantiles=d,name=group_name)) -} - -parameter_mcmc_areas <- function( - stem,# = "beta" - class_list,# = beta_list - stanfit,# = fit - parameter_id,# = 2 - rename=TRUE - ) { - #get all parameter names - params <- get_parameters(stem,class_list) - #filter down to parameters of interest - params <- filter(params,parameters == parameter_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$groups_hr - #get group name for title - parameter_name <- class_list$parameters[parameter_id] - #create area plot with appropriate title - p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(parameter_name,"Parameter Distribution") - - d <- pivot_longer(filtdata, everything()) |> - group_by(name) |> - summarize( - mean=mean(value) - ,q025 = quantile(value,probs = 0.025) - ,q975 = quantile(value,probs = 0.975) - ,q05 = quantile(value,probs = 0.05) - ,q95 = quantile(value,probs = 0.95) - ) - return(list(plot=p,quantiles=d,name=parameter_name)) -} + + #get all parameter names + params <- get_parameters(stem,class_list) + + #filter down to parameters of interest + params <- filter(params,groups == group_id) + #Get dataframe with only the rows of interest + filtdata <- as.data.frame(stanfit)[params$param_name] + #rename columns + if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr + #get group name for title + group_name <- class_list$groups[group_id] + #create area plot with appropriate title + p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + + ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) + + geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750) + + d <- pivot_longer(filtdata, everything()) |> + group_by(name) |> + summarize( + mean=mean(value) + ,q025 = quantile(value,probs = 0.025) + ,q975 = quantile(value,probs = 0.975) + ,q05 = quantile(value,probs = 0.05) + ,q95 = quantile(value,probs = 0.95) + ) + return(list(plot=p,quantiles=d,name=group_name)) +} + +parameter_mcmc_areas <- function( + stem,# = "beta" + class_list,# = beta_list + stanfit,# = fit + parameter_id,# = 2 + rename=TRUE + ) { + #get all parameter names + params <- get_parameters(stem,class_list) + #filter down to parameters of interest + params <- filter(params,parameters == parameter_id) + #Get dataframe with only the rows of interest + filtdata <- as.data.frame(stanfit)[params$param_name] + #rename columns + if (rename) dimnames(filtdata)[[2]] <- params$groups_hr + #get group name for title + parameter_name <- class_list$parameters[parameter_id] + #create area plot with appropriate title + p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + + ggtitle(parameter_name,"Parameter Distribution") + + geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750) + + d <- pivot_longer(filtdata, everything()) |> + group_by(name) |> + summarize( + mean=mean(value) + ,q025 = quantile(value,probs = 0.025) + ,q975 = quantile(value,probs = 0.975) + ,q05 = quantile(value,probs = 0.05) + ,q95 = quantile(value,probs = 0.95) + ) + return(list(plot=p,quantiles=d,name=parameter_name)) +}

Plan: select all snapshots that are the first to have closed enrollment (Rec -> ANR)

@@ -565,7 +572,7 @@ The following object is masked from 'package:tidyr': seed = 11021585 )
-
Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
+
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
@@ -581,306 +588,458 @@ https://mc-stan.org/misc/warnings.html#bfmi-low category_count <- group_trials_by_category |> group_by(category_id) |> count()
+
+
################################# DATA EXPLORATION ############################
+driver <- dbDriver("PostgreSQL")
+
+con <- dbConnect(
+    driver,
+    user='root',
+    password='root',
+    dbname='aact_db',
+    host=host
+    )
+#Plot histogram of count of snapshots
+df3 <- dbGetQuery(
+    con,
+    "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe 
+    group by nct_id,final_status ;"
+    )
+#df3 <- fetch(query3, n = -1)
+
+ggplot(data=df3, aes(x=count, fill=final_status)) + 
+    geom_histogram(binwidth=1) +
+    ggtitle("Histogram of snapshots per trial (matched trials)") +
+    xlab("Snapshots per trial")
+
+
+
+

+
+
+
+
ggsave("./Images/HistSnapshots.png")
+
+
Saving 7 x 5 in image
+
+
#Plot duration for terminated vs completed
+df4 <- dbGetQuery(
+    con,
+    "
+    select 
+        nct_id, 
+        start_date , 
+        primary_completion_date, 
+        overall_status ,
+        primary_completion_date - start_date as duration
+    from ctgov.studies s 
+    where nct_id in (select distinct nct_id from http.download_status ds)
+    ;"
+    )
+#df4 <- fetch(query4, n = -1)
+
+ggplot(data=df4, aes(x=duration,fill=overall_status)) +
+    geom_histogram()+
+    ggtitle("Histogram of trial durations") +
+    xlab("duration")+
+    facet_wrap(~overall_status)
+
+
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
+
+
+
+

+
+
+
+
ggsave("./Images/HistTrialDurations_Faceted.png")
+
+
Saving 7 x 5 in image
+`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
+
df5 <- dbGetQuery(
+    con,
+    "
+    with cte1 as (
+    select 
+        nct_id, 
+        start_date , 
+        primary_completion_date, 
+        overall_status ,
+        primary_completion_date - start_date as duration
+    from ctgov.studies s 
+    where nct_id in (select distinct nct_id from http.download_status ds)
+    ), cte2 as (
+    select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe
+    group by nct_id
+    )
+    select a.nct_id, a.overall_status, a.duration,b.snapshot_count
+    from cte1 as a
+        join cte2 as b
+            on a.nct_id=b.nct_id
+    ;"
+    )
+df5$overall_status <- as.factor(df5$overall_status)
+
+ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
+    geom_jitter() +
+    ggtitle("Comparison of duration, status, and snapshot_count") +
+    xlab("duration") +
+    ylab("snapshot count")
+
+
+
+

+
+
+
+
ggsave("./Images/SnapshotsVsDurationVsTermination.png")
+
+
Saving 7 x 5 in image
+
+
dbDisconnect(con)
+
+
[1] TRUE
+
+
#get number of trials and snapshots in each category
+group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
+group_trials_by_category <- as.data.frame(group_trials_by_category)
+
+ggplot(data = group_trials_by_category, aes(x=category_id)) +
+    geom_bar(binwidth=1,color="black",fill="seagreen") +
+    scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + 
+    labs(
+        title="bar chart of trial categories"
+        ,x="Category ID"
+        ,y="Count"
+    )
+
+
Warning in geom_bar(binwidth = 1, color = "black", fill = "seagreen"): Ignoring
+unknown parameters: `binwidth`
+
+
+
+
+

+
+
+
+
ggsave("./Images/CategoryCounts.png")
+
+
Saving 7 x 5 in image
+
+
summary(df5)
+
+
    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
+
+

Fit Results

-
################################# ANALYZE #####################################
-print(fit)
+
################################# ANALYZE #####################################
+print(fit)
Inference for Stan model: anon_model.
 4 chains, each with iter=5000; warmup=2500; thin=1; 
 post-warmup draws per chain=2500, total post-warmup draws=10000.
 
                                  mean se_mean    sd    2.5%     25%     50%
-mu[1]                           -0.02    0.00  0.05   -0.12   -0.06   -0.03
-mu[2]                            0.00    0.00  0.05   -0.10   -0.04    0.00
+mu[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   -0.06   -0.03
-mu[7]                           -0.01    0.00  0.05   -0.11   -0.04   -0.01
-mu[8]                            0.00    0.00  0.05   -0.09   -0.03    0.00
-mu[9]                            0.00    0.00  0.05   -0.10   -0.04    0.00
+mu[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]                           0.00    0.00  0.05   -0.09   -0.03    0.00
-mu[12]                          -0.03    0.00  0.05   -0.13   -0.06   -0.03
-sigma[1]                         0.27    0.00  0.12    0.07    0.19    0.26
-sigma[2]                         0.91    0.00  0.19    0.57    0.78    0.90
-sigma[3]                         0.66    0.00  0.18    0.34    0.54    0.65
-sigma[4]                         0.31    0.00  0.09    0.15    0.24    0.30
-sigma[5]                         0.18    0.00  0.09    0.05    0.12    0.17
-sigma[6]                         0.19    0.00  0.09    0.06    0.12    0.18
-sigma[7]                         0.18    0.00  0.09    0.05    0.12    0.17
-sigma[8]                         0.17    0.00  0.08    0.05    0.11    0.16
-sigma[9]                         0.32    0.01  0.15    0.08    0.21    0.30
-sigma[10]                        0.19    0.00  0.10    0.05    0.12    0.18
-sigma[11]                        0.23    0.00  0.12    0.06    0.14    0.21
-sigma[12]                        0.28    0.00  0.13    0.09    0.19    0.27
-beta[1,1]                       -0.10    0.00  0.25   -0.65   -0.24   -0.09
-beta[1,2]                       -0.42    0.00  0.42   -1.23   -0.71   -0.42
-beta[1,3]                        0.68    0.00  0.40   -0.07    0.41    0.67
+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]                        0.05    0.00  0.18   -0.29   -0.07    0.03
-beta[1,7]                        0.07    0.00  0.17   -0.24   -0.04    0.06
-beta[1,8]                        0.06    0.00  0.15   -0.23   -0.04    0.05
-beta[1,9]                        0.32    0.01  0.38   -0.24    0.06    0.25
-beta[1,10]                      -0.03    0.00  0.22   -0.53   -0.14   -0.02
-beta[1,11]                       0.02    0.00  0.22   -0.43   -0.10    0.02
-beta[1,12]                      -0.22    0.00  0.27   -0.82   -0.37   -0.19
-beta[2,1]                       -0.41    0.01  0.26   -0.99   -0.58   -0.39
-beta[2,2]                       -1.24    0.00  0.27   -1.78   -1.43   -1.24
-beta[2,3]                        0.47    0.00  0.20    0.08    0.34    0.47
-beta[2,4]                        0.25    0.00  0.22   -0.14    0.10    0.23
-beta[2,5]                       -0.09    0.00  0.18   -0.51   -0.20   -0.08
-beta[2,6]                       -0.12    0.00  0.19   -0.55   -0.23   -0.11
-beta[2,7]                       -0.07    0.00  0.17   -0.46   -0.17   -0.06
-beta[2,8]                        0.05    0.00  0.16   -0.25   -0.05    0.04
-beta[2,9]                       -0.48    0.01  0.40   -1.43   -0.71   -0.41
-beta[2,10]                       0.00    0.00  0.23   -0.48   -0.12    0.00
-beta[2,11]                      -0.14    0.00  0.21   -0.61   -0.26   -0.12
-beta[2,12]                      -0.36    0.01  0.27   -0.96   -0.53   -0.33
-beta[3,1]                       -0.03    0.00  0.30   -0.65   -0.19   -0.03
-beta[3,2]                       -0.12    0.01  0.93   -2.03   -0.71   -0.11
-beta[3,3]                       -0.10    0.01  0.69   -1.52   -0.52   -0.09
-beta[3,4]                       -0.19    0.00  0.29   -0.80   -0.37   -0.18
-beta[3,5]                       -0.10    0.00  0.20   -0.56   -0.20   -0.08
-beta[3,6]                       -0.10    0.00  0.21   -0.57   -0.21   -0.08
-beta[3,7]                       -0.08    0.00  0.20   -0.52   -0.18   -0.06
-beta[3,8]                       -0.06    0.00  0.19   -0.50   -0.16   -0.04
-beta[3,9]                        0.00    0.00  0.34   -0.70   -0.19    0.00
-beta[3,10]                       0.00    0.00  0.22   -0.48   -0.11    0.00
-beta[3,11]                       0.00    0.00  0.26   -0.57   -0.13    0.00
-beta[3,12]                      -0.03    0.00  0.31   -0.67   -0.20   -0.04
-beta[4,1]                       -0.04    0.00  0.29   -0.62   -0.20   -0.04
-beta[4,2]                       -0.43    0.00  0.57   -1.59   -0.80   -0.42
-beta[4,3]                       -0.67    0.01  0.55   -1.85   -1.02   -0.63
-beta[4,4]                        0.06    0.00  0.25   -0.42   -0.10    0.06
-beta[4,5]                       -0.03    0.00  0.17   -0.39   -0.13   -0.03
-beta[4,6]                       -0.08    0.00  0.19   -0.49   -0.18   -0.07
-beta[4,7]                        0.00    0.00  0.18   -0.37   -0.10    0.00
-beta[4,8]                        0.07    0.00  0.18   -0.26   -0.04    0.06
-beta[4,9]                       -0.13    0.00  0.34   -0.91   -0.30   -0.10
-beta[4,10]                      -0.01    0.00  0.22   -0.49   -0.12   -0.01
-beta[4,11]                       0.21    0.01  0.30   -0.23    0.01    0.15
-beta[4,12]                      -0.21    0.01  0.31   -0.95   -0.37   -0.17
-beta[5,1]                       -0.10    0.00  0.29   -0.75   -0.26   -0.08
-beta[5,2]                       -1.41    0.01  0.92   -3.40   -1.98   -1.35
-beta[5,3]                        0.26    0.01  0.67   -1.01   -0.18    0.23
-beta[5,4]                        0.02    0.00  0.24   -0.45   -0.13    0.02
+beta[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   -0.15   -0.04
-beta[5,7]                        0.05    0.00  0.19   -0.29   -0.06    0.04
-beta[5,8]                        0.09    0.00  0.19   -0.24   -0.03    0.07
-beta[5,9]                        0.01    0.00  0.33   -0.67   -0.17    0.00
-beta[5,10]                      -0.01    0.00  0.22   -0.49   -0.12   -0.01
-beta[5,11]                       0.08    0.00  0.26   -0.38   -0.07    0.05
-beta[5,12]                      -0.18    0.00  0.31   -0.89   -0.33   -0.14
-beta[6,1]                       -0.06    0.00  0.29   -0.67   -0.22   -0.06
-beta[6,2]                        2.93    0.01  0.88    1.33    2.32    2.89
-beta[6,3]                        0.21    0.00  0.34   -0.48   -0.02    0.21
-beta[6,4]                       -0.48    0.00  0.28   -1.09   -0.65   -0.46
-beta[6,5]                       -0.14    0.00  0.20   -0.59   -0.25   -0.12
-beta[6,6]                       -0.07    0.00  0.19   -0.48   -0.18   -0.06
-beta[6,7]                       -0.01    0.00  0.18   -0.39   -0.12   -0.01
-beta[6,8]                        0.05    0.00  0.18   -0.29   -0.06    0.04
-beta[6,9]                        0.02    0.00  0.33   -0.65   -0.17    0.01
-beta[6,10]                       0.00    0.00  0.22   -0.47   -0.12    0.00
-beta[6,11]                      -0.10    0.00  0.26   -0.70   -0.22   -0.07
-beta[6,12]                       0.04    0.00  0.30   -0.52   -0.14    0.02
-beta[7,1]                       -0.14    0.00  0.31   -0.84   -0.30   -0.10
-beta[7,2]                       -0.18    0.01  0.53   -1.24   -0.52   -0.17
-beta[7,3]                        1.42    0.01  0.73    0.17    0.91    1.36
-beta[7,4]                       -0.08    0.00  0.25   -0.58   -0.24   -0.08
-beta[7,5]                       -0.11    0.00  0.19   -0.54   -0.22   -0.09
-beta[7,6]                       -0.12    0.00  0.20   -0.57   -0.23   -0.10
-beta[7,7]                       -0.08    0.00  0.19   -0.50   -0.18   -0.06
-beta[7,8]                       -0.06    0.00  0.19   -0.50   -0.16   -0.05
-beta[7,9]                        0.05    0.00  0.34   -0.62   -0.14    0.04
-beta[7,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[7,11]                      -0.03    0.00  0.24   -0.58   -0.15   -0.02
-beta[7,12]                      -0.06    0.00  0.29   -0.68   -0.23   -0.06
-beta[8,1]                       -0.03    0.00  0.31   -0.67   -0.20   -0.03
-beta[8,2]                        0.01    0.01  0.94   -1.87   -0.59    0.01
-beta[8,3]                        0.00    0.01  0.69   -1.42   -0.42    0.00
-beta[8,4]                       -0.04    0.00  0.32   -0.70   -0.24   -0.04
-beta[8,5]                       -0.04    0.00  0.20   -0.45   -0.15   -0.04
-beta[8,6]                       -0.03    0.00  0.22   -0.49   -0.15   -0.03
-beta[8,7]                       -0.01    0.00  0.21   -0.44   -0.12   -0.01
-beta[8,8]                        0.00    0.00  0.20   -0.41   -0.10    0.00
-beta[8,9]                        0.00    0.00  0.35   -0.74   -0.19    0.00
-beta[8,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[8,11]                       0.00    0.00  0.25   -0.53   -0.13    0.00
-beta[8,12]                      -0.04    0.00  0.31   -0.69   -0.21   -0.04
-beta[9,1]                       -0.04    0.00  0.30   -0.68   -0.20   -0.04
-beta[9,2]                       -0.64    0.01  0.83   -2.39   -1.16   -0.60
-beta[9,3]                       -0.56    0.01  0.60   -1.84   -0.93   -0.52
-beta[9,4]                        0.02    0.00  0.26   -0.52   -0.15    0.02
-beta[9,5]                        0.03    0.00  0.19   -0.32   -0.09    0.02
-beta[9,6]                        0.10    0.00  0.21   -0.24   -0.04    0.07
-beta[9,7]                        0.11    0.00  0.20   -0.23   -0.02    0.08
-beta[9,8]                        0.10    0.00  0.19   -0.23   -0.02    0.07
-beta[9,9]                        0.05    0.00  0.34   -0.63   -0.14    0.03
-beta[9,10]                       0.00    0.00  0.23   -0.47   -0.12    0.00
-beta[9,11]                      -0.05    0.00  0.26   -0.63   -0.17   -0.03
-beta[9,12]                      -0.01    0.00  0.31   -0.64   -0.18   -0.02
-beta[10,1]                      -0.03    0.00  0.30   -0.64   -0.19   -0.03
-beta[10,2]                      -0.24    0.01  0.89   -2.03   -0.80   -0.22
-beta[10,3]                      -0.11    0.01  0.68   -1.53   -0.52   -0.09
-beta[10,4]                      -0.22    0.00  0.29   -0.85   -0.39   -0.20
-beta[10,5]                      -0.11    0.00  0.20   -0.56   -0.21   -0.09
-beta[10,6]                      -0.11    0.00  0.21   -0.59   -0.22   -0.09
-beta[10,7]                      -0.09    0.00  0.20   -0.55   -0.19   -0.07
-beta[10,8]                      -0.07    0.00  0.19   -0.51   -0.16   -0.05
-beta[10,9]                      -0.01    0.00  0.35   -0.73   -0.19   -0.01
-beta[10,10]                      0.00    0.00  0.22   -0.46   -0.12    0.00
+beta[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  0.32   -0.68   -0.21   -0.04
-beta[11,1]                      -0.03    0.00  0.30   -0.65   -0.20   -0.03
-beta[11,2]                      -0.15    0.01  0.90   -2.00   -0.71   -0.14
-beta[11,3]                      -0.08    0.01  0.68   -1.47   -0.50   -0.07
-beta[11,4]                      -0.27    0.00  0.28   -0.88   -0.44   -0.25
+beta[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  0.21   -0.62   -0.23   -0.10
-beta[11,7]                      -0.09    0.00  0.21   -0.58   -0.20   -0.07
-beta[11,8]                      -0.07    0.00  0.19   -0.52   -0.18   -0.05
-beta[11,9]                       0.00    0.00  0.35   -0.75   -0.19    0.00
-beta[11,10]                     -0.01    0.00  0.22   -0.48   -0.12    0.00
+beta[11,6]                      -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]                     -0.04    0.00  0.32   -0.70   -0.21   -0.04
-beta[12,1]                      -0.17    0.00  0.29   -0.85   -0.32   -0.14
-beta[12,2]                      -0.71    0.01  0.84   -2.48   -1.25   -0.68
-beta[12,3]                       0.34    0.01  0.62   -0.81   -0.06    0.32
-beta[12,4]                      -0.19    0.00  0.24   -0.70   -0.33   -0.17
-beta[12,5]                      -0.06    0.00  0.18   -0.46   -0.16   -0.06
-beta[12,6]                       0.01    0.00  0.19   -0.36   -0.11    0.00
-beta[12,7]                       0.02    0.00  0.18   -0.33   -0.09    0.01
-beta[12,8]                       0.05    0.00  0.18   -0.30   -0.06    0.04
-beta[12,9]                       0.04    0.00  0.33   -0.63   -0.14    0.02
-beta[12,10]                      0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[12,11]                      0.05    0.00  0.26   -0.43   -0.09    0.04
-beta[12,12]                     -0.14    0.00  0.30   -0.82   -0.30   -0.12
-beta[13,1]                       0.10    0.00  0.30   -0.41   -0.08    0.06
-beta[13,2]                       1.52    0.01  0.55    0.53    1.15    1.49
-beta[13,3]                      -1.42    0.01  0.55   -2.60   -1.76   -1.37
-beta[13,4]                      -0.09    0.00  0.24   -0.58   -0.24   -0.08
-beta[13,5]                      -0.07    0.00  0.18   -0.46   -0.17   -0.06
-beta[13,6]                      -0.03    0.00  0.18   -0.41   -0.14   -0.03
-beta[13,7]                       0.01    0.00  0.18   -0.36   -0.10    0.00
-beta[13,8]                       0.01    0.00  0.18   -0.35   -0.09    0.01
-beta[13,9]                      -0.07    0.00  0.32   -0.80   -0.24   -0.05
-beta[13,10]                      0.00    0.00  0.22   -0.46   -0.11    0.00
-beta[13,11]                      0.12    0.00  0.26   -0.33   -0.04    0.08
-beta[13,12]                     -0.21    0.01  0.31   -0.94   -0.38   -0.17
-beta[14,1]                      -0.03    0.00  0.31   -0.67   -0.19   -0.03
-beta[14,2]                      -0.29    0.01  0.91   -2.12   -0.87   -0.27
-beta[14,3]                      -0.17    0.01  0.66   -1.56   -0.58   -0.15
-beta[14,4]                      -0.19    0.00  0.30   -0.83   -0.36   -0.17
-beta[14,5]                      -0.10    0.00  0.20   -0.55   -0.20   -0.08
-beta[14,6]                      -0.09    0.00  0.21   -0.57   -0.20   -0.07
-beta[14,7]                      -0.07    0.00  0.20   -0.53   -0.18   -0.05
-beta[14,8]                      -0.05    0.00  0.19   -0.49   -0.14   -0.03
-beta[14,9]                      -0.01    0.00  0.35   -0.74   -0.20   -0.01
-beta[14,10]                      0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[14,11]                      0.01    0.00  0.26   -0.53   -0.13    0.01
-beta[14,12]                     -0.03    0.00  0.31   -0.69   -0.21   -0.03
-beta[15,1]                      -0.03    0.00  0.29   -0.62   -0.19   -0.03
-beta[15,2]                      -0.01    0.01  0.95   -1.91   -0.60   -0.01
-beta[15,3]                      -0.01    0.01  0.68   -1.38   -0.43    0.00
-beta[15,4]                      -0.04    0.00  0.33   -0.70   -0.24   -0.04
-beta[15,5]                      -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[15,6]                      -0.03    0.00  0.22   -0.49   -0.15   -0.03
-beta[15,7]                      -0.01    0.00  0.21   -0.44   -0.12   -0.01
-beta[15,8]                       0.00    0.00  0.20   -0.42   -0.10    0.00
-beta[15,9]                      -0.01    0.00  0.35   -0.74   -0.20   -0.01
-beta[15,10]                      0.00    0.00  0.22   -0.48   -0.11    0.00
-beta[15,11]                      0.00    0.00  0.26   -0.55   -0.13    0.00
-beta[15,12]                     -0.03    0.00  0.32   -0.68   -0.20   -0.04
-beta[16,1]                      -0.02    0.00  0.31   -0.65   -0.19   -0.02
-beta[16,2]                       0.00    0.01  0.95   -1.90   -0.59    0.00
-beta[16,3]                       0.00    0.01  0.69   -1.40   -0.42    0.00
-beta[16,4]                      -0.04    0.00  0.33   -0.69   -0.24   -0.05
-beta[16,5]                      -0.04    0.00  0.21   -0.48   -0.15   -0.04
-beta[16,6]                      -0.03    0.00  0.21   -0.47   -0.15   -0.03
-beta[16,7]                      -0.01    0.00  0.21   -0.44   -0.12   -0.01
-beta[16,8]                       0.00    0.00  0.20   -0.42   -0.10    0.00
-beta[16,9]                       0.00    0.00  0.36   -0.75   -0.19   -0.01
-beta[16,10]                      0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[16,11]                      0.00    0.00  0.26   -0.55   -0.13    0.00
-beta[16,12]                     -0.03    0.00  0.31   -0.68   -0.20   -0.03
-beta[17,1]                      -0.03    0.00  0.31   -0.68   -0.20   -0.03
-beta[17,2]                      -0.16    0.01  0.91   -1.99   -0.73   -0.14
-beta[17,3]                      -0.10    0.01  0.70   -1.53   -0.52   -0.08
-beta[17,4]                      -0.22    0.00  0.29   -0.86   -0.38   -0.20
-beta[17,5]                      -0.11    0.00  0.20   -0.56   -0.21   -0.09
-beta[17,6]                      -0.11    0.00  0.20   -0.57   -0.22   -0.09
-beta[17,7]                      -0.09    0.00  0.20   -0.53   -0.19   -0.07
-beta[17,8]                      -0.07    0.00  0.19   -0.51   -0.17   -0.05
-beta[17,9]                      -0.01    0.00  0.35   -0.72   -0.19   -0.01
-beta[17,10]                      0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[17,11]                      0.00    0.00  0.26   -0.55   -0.13    0.00
-beta[17,12]                     -0.03    0.00  0.32   -0.68   -0.21   -0.04
-beta[18,1]                      -0.03    0.00  0.31   -0.67   -0.19   -0.03
-beta[18,2]                      -0.13    0.01  0.93   -2.01   -0.72   -0.12
-beta[18,3]                      -0.06    0.01  0.69   -1.47   -0.47   -0.04
-beta[18,4]                      -0.19    0.00  0.29   -0.82   -0.36   -0.17
-beta[18,5]                      -0.09    0.00  0.20   -0.54   -0.20   -0.07
-beta[18,6]                      -0.09    0.00  0.20   -0.54   -0.20   -0.07
-beta[18,7]                      -0.06    0.00  0.20   -0.51   -0.17   -0.05
-beta[18,8]                      -0.05    0.00  0.19   -0.48   -0.15   -0.03
-beta[18,9]                      -0.01    0.00  0.35   -0.74   -0.19   -0.01
-beta[18,10]                      0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[18,11]                      0.00    0.00  0.27   -0.56   -0.13    0.00
+beta[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  0.29   -0.64   -0.19   -0.03
-beta[19,2]                       0.00    0.01  0.95   -1.88   -0.59    0.00
-beta[19,3]                       0.00    0.01  0.70   -1.39   -0.43    0.00
-beta[19,4]                      -0.05    0.00  0.33   -0.70   -0.25   -0.05
-beta[19,5]                      -0.03    0.00  0.21   -0.45   -0.14   -0.04
-beta[19,6]                      -0.03    0.00  0.21   -0.47   -0.15   -0.03
-beta[19,7]                      -0.01    0.00  0.21   -0.45   -0.13   -0.01
-beta[19,8]                       0.00    0.00  0.20   -0.41   -0.10    0.00
-beta[19,9]                       0.00    0.00  0.35   -0.74   -0.19   -0.01
-beta[19,10]                      0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[19,11]                      0.00    0.00  0.26   -0.54   -0.13    0.00
-beta[19,12]                     -0.03    0.00  0.31   -0.68   -0.20   -0.03
-beta[20,1]                      -0.02    0.00  0.31   -0.64   -0.19   -0.02
-beta[20,2]                      -0.02    0.01  0.91   -1.82   -0.61   -0.02
-beta[20,3]                       0.00    0.01  0.68   -1.36   -0.43    0.00
-beta[20,4]                      -0.04    0.00  0.33   -0.70   -0.25   -0.05
-beta[20,5]                      -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[20,6]                      -0.03    0.00  0.22   -0.49   -0.15   -0.03
-beta[20,7]                      -0.01    0.00  0.21   -0.45   -0.13   -0.01
-beta[20,8]                       0.00    0.00  0.19   -0.40   -0.10    0.00
-beta[20,9]                       0.00    0.00  0.35   -0.73   -0.19    0.00
-beta[20,10]                     -0.01    0.00  0.23   -0.49   -0.11   -0.01
-beta[20,11]                      0.00    0.00  0.26   -0.53   -0.13    0.00
-beta[20,12]                     -0.03    0.00  0.31   -0.65   -0.20   -0.03
-beta[21,1]                      -0.03    0.00  0.31   -0.65   -0.19   -0.03
-beta[21,2]                       0.00    0.01  0.92   -1.84   -0.58   -0.01
-beta[21,3]                       0.00    0.01  0.68   -1.39   -0.44   -0.01
-beta[21,4]                      -0.04    0.00  0.32   -0.68   -0.24   -0.05
-beta[21,5]                      -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[21,6]                      -0.03    0.00  0.22   -0.49   -0.14   -0.03
-beta[21,7]                      -0.01    0.00  0.21   -0.43   -0.13   -0.01
-beta[21,8]                       0.00    0.00  0.20   -0.41   -0.11    0.00
-beta[21,9]                       0.00    0.00  0.35   -0.73   -0.19   -0.01
-beta[21,10]                      0.00    0.00  0.22   -0.47   -0.11    0.00
-beta[21,11]                      0.01    0.00  0.26   -0.53   -0.13    0.00
-beta[21,12]                     -0.03    0.00  0.31   -0.67   -0.20   -0.03
-beta[22,1]                      -0.02    0.00  0.31   -0.65   -0.19   -0.03
-beta[22,2]                      -0.01    0.01  0.92   -1.86   -0.60   -0.01
-beta[22,3]                       0.00    0.01  0.69   -1.35   -0.43    0.00
-beta[22,4]                      -0.04    0.00  0.33   -0.71   -0.24   -0.04
-beta[22,5]                      -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[22,6]                      -0.03    0.00  0.22   -0.47   -0.15   -0.03
-beta[22,7]                      -0.01    0.00  0.21   -0.43   -0.13   -0.02
-beta[22,8]                       0.01    0.00  0.20   -0.41   -0.10    0.01
-beta[22,9]                      -0.01    0.00  0.35   -0.75   -0.20   -0.01
-beta[22,10]                      0.00    0.00  0.22   -0.46   -0.11    0.00
-beta[22,11]                      0.00    0.00  0.26   -0.53   -0.13    0.00
-beta[22,12]                     -0.03    0.00  0.30   -0.64   -0.20   -0.04
+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
@@ -889,1061 +1048,1061 @@ mu_prior[5]                      0.00    0.00  0.05   -0.10   -0.03    0.00
 mu_prior[6]                      0.00    0.00  0.05   -0.10   -0.03    0.00
 mu_prior[7]                      0.00    0.00  0.05   -0.10   -0.03    0.00
 mu_prior[8]                      0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[9]                      0.00    0.00  0.05   -0.10   -0.03    0.00
+mu_prior[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    0.05    0.13    0.18
-sigma_prior[3]                   0.20    0.00  0.10    0.05    0.12    0.18
-sigma_prior[4]                   0.20    0.00  0.10    0.06    0.13    0.18
+sigma_prior[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    0.06    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    0.06    0.13    0.18
-sigma_prior[11]                  0.20    0.00  0.10    0.05    0.13    0.18
-sigma_prior[12]                  0.20    0.00  0.10    0.06    0.13    0.18
-p_prior[1]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[2]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[3]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[4]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[5]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[6]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[7]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[8]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[9]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[10]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[11]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[12]                      0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[13]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[14]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[15]                      0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[16]                      0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[17]                      0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[18]                      0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[19]                      0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[20]                      0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[21]                      0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[22]                      0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[23]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[24]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[25]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[26]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[27]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[28]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[29]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[30]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[31]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[32]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[33]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[34]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[35]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[36]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[37]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[38]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[39]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[40]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[41]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[42]                      0.50    0.00  0.44    0.00    0.01    0.50
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-p_prior[45]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[46]                      0.50    0.00  0.44    0.00    0.01    0.50
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-p_prior[56]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[57]                      0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[58]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[59]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[60]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[61]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[62]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[63]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[64]                      0.50    0.00  0.45    0.00    0.00    0.50
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-p_prior[71]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[72]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[73]                      0.50    0.00  0.43    0.00    0.02    0.49
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-p_prior[75]                      0.50    0.00  0.43    0.00    0.02    0.50
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-p_prior[77]                      0.50    0.00  0.45    0.00    0.00    0.50
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-p_prior[89]                      0.50    0.00  0.44    0.00    0.01    0.51
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+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
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 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
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 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
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 p_prior[111]                     0.50    0.00  0.44    0.00    0.01    0.50
 p_prior[112]                     0.50    0.00  0.44    0.00    0.01    0.50
 p_prior[113]                     0.50    0.00  0.44    0.00    0.01    0.50
 p_prior[114]                     0.50    0.00  0.44    0.00    0.01    0.50
 p_prior[115]                     0.50    0.00  0.44    0.00    0.01    0.50
 p_prior[116]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[117]                     0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[118]                     0.50    0.00  0.43    0.00    0.02    0.49
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 p_prior[737]                     0.50    0.00  0.44    0.00    0.01    0.49
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 p_prior[825]                     0.50    0.00  0.44    0.00    0.01    0.50
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 p_prior[849]                     0.50    0.00  0.19    0.14    0.37    0.50
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 p_prior[996]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[997]                     0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[998]                     0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[999]                     0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[1000]                    0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[1001]                    0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[1002]                    0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[1003]                    0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[1004]                    0.50    0.00  0.45    0.00    0.01    0.48
-p_prior[1005]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1006]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1007]                    0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1008]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1009]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1010]                    0.50    0.00  0.44    0.00    0.01    0.47
-p_prior[1011]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1012]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1013]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1014]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1015]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1016]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1017]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1018]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1019]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1020]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1021]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1022]                    0.50    0.00  0.44    0.00    0.01    0.50
+p_prior[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    0.50
-p_prior[1025]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1026]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1027]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1028]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1029]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1030]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1031]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1032]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1033]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1034]                    0.50    0.00  0.44    0.00    0.01    0.49
+p_prior[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  0.44    0.00    0.01    0.50
-p_prior[1037]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1038]                    0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1039]                    0.50    0.00  0.44    0.00    0.01    0.50
+p_prior[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
@@ -1951,275 +2110,275 @@ p_prior[1043]                    0.50    0.00  0.44    0.00    0.01    0.49
 p_prior[1044]                    0.50    0.00  0.45    0.00    0.00    0.49
 p_prior[1045]                    0.50    0.00  0.44    0.00    0.01    0.49
 p_prior[1046]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1047]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1048]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1049]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1050]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1051]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1052]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1053]                    0.50    0.00  0.43    0.00    0.02    0.50
+p_prior[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    0.50
-p_prior[1057]                    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    0.02    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    0.50
-p_prior[1066]                    0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1067]                    0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1068]                    0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1069]                    0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1070]                    0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1071]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1072]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1073]                    0.50    0.00  0.44    0.00    0.01    0.49
+p_prior[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    0.49
-p_prior[1077]                    0.50    0.00  0.42    0.00    0.03    0.49
+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  0.42    0.00    0.03    0.50
-p_prior[1081]                    0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1082]                    0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1083]                    0.50    0.00  0.42    0.00    0.03    0.50
+p_prior[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    0.22    0.41    0.50
-p_prior[1086]                    0.50    0.00  0.14    0.22    0.40    0.50
-p_prior[1087]                    0.50    0.00  0.15    0.22    0.40    0.50
-p_prior[1088]                    0.50    0.00  0.15    0.21    0.40    0.50
-p_prior[1089]                    0.50    0.00  0.16    0.20    0.39    0.50
-p_prior[1090]                    0.50    0.00  0.16    0.20    0.39    0.50
-p_prior[1091]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1092]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1093]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1094]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1095]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1096]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1097]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1098]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1099]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1100]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1101]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1102]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1103]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1104]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1105]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1106]                    0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1107]                    0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1108]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1109]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1110]                    0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1111]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1112]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1113]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1114]                    0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1115]                    0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1116]                    0.50    0.00  0.12    0.26    0.42    0.50
-p_prior[1117]                    0.50    0.00  0.12    0.26    0.42    0.50
-p_prior[1118]                    0.50    0.00  0.12    0.26    0.42    0.50
-p_prior[1119]                    0.50    0.00  0.13    0.26    0.42    0.50
-p_prior[1120]                    0.50    0.00  0.13    0.26    0.42    0.50
-p_prior[1121]                    0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1122]                    0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1123]                    0.50    0.00  0.13    0.25    0.42    0.50
-p_prior[1124]                    0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1125]                    0.50    0.00  0.13    0.25    0.42    0.50
-p_prior[1126]                    0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1127]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1128]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1129]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1130]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1131]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1132]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1133]                    0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[1134]                    0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[1135]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1136]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1137]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1138]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1139]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1140]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1141]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1142]                    0.50    0.00  0.45    0.00    0.00    0.49
+p_prior[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
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+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  0.44    0.00    0.01    0.49
-p_prior[1145]                    0.50    0.00  0.45    0.00    0.00    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  0.44    0.00    0.01    0.48
-p_prior[1148]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1149]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1150]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1151]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1152]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1153]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1154]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1155]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1156]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1157]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1158]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1159]                    0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1160]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1161]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1162]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1163]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1164]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1165]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1166]                    0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1167]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1168]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1169]                    0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1170]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1171]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1172]                    0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1173]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1174]                    0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1175]                    0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1176]                    0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1177]                    0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1178]                    0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1179]                    0.50    0.00  0.19    0.15    0.37    0.50
-p_prior[1180]                    0.50    0.00  0.19    0.14    0.37    0.50
-p_prior[1181]                    0.50    0.00  0.19    0.14    0.36    0.50
-p_prior[1182]                    0.50    0.00  0.19    0.13    0.36    0.50
-p_prior[1183]                    0.50    0.00  0.19    0.13    0.36    0.50
-p_prior[1184]                    0.50    0.00  0.20    0.12    0.35    0.50
-p_prior[1185]                    0.50    0.00  0.21    0.12    0.35    0.50
+p_prior[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    0.50
-p_prior[1190]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1191]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1192]                    0.50    0.00  0.44    0.00    0.01    0.50
+p_prior[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    0.50
-p_prior[1195]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1196]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1197]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1198]                    0.50    0.00  0.44    0.00    0.01    0.49
+p_prior[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    0.48
-p_prior[1204]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1205]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1206]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1207]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1208]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1209]                    0.50    0.00  0.45    0.00    0.00    0.51
-p_prior[1210]                    0.50    0.00  0.45    0.00    0.00    0.51
+p_prior[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  0.44    0.00    0.01    0.50
-p_prior[1214]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1215]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1216]                    0.50    0.00  0.44    0.00    0.01    0.50
+p_prior[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  0.42    0.00    0.03    0.50
-p_prior[1219]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1220]                    0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1221]                    0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1222]                    0.50    0.00  0.45    0.00    0.00    0.51
+p_prior[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    0.50
-p_prior[1225]                    0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1226]                    0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1227]                    0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1228]                    0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1229]                    0.50    0.00  0.45    0.00    0.01    0.49
-p_prior[1230]                    0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1231]                    0.50    0.00  0.45    0.00    0.01    0.48
-p_prior[1232]                    0.50    0.00  0.45    0.00    0.01    0.48
+p_prior[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  0.09    0.32    0.44    0.50
-p_prior[1238]                    0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1239]                    0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1240]                    0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1241]                    0.50    0.00  0.10    0.31    0.44    0.50
-p_prior[1242]                    0.50    0.00  0.10    0.31    0.44    0.50
+p_prior[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  0.10    0.30    0.43    0.50
-p_prior[1246]                    0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1247]                    0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1248]                    0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1249]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1250]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1251]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1252]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1253]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1254]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1255]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1256]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1257]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1258]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1259]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1260]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1261]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1262]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1263]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1264]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1265]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1266]                    0.50    0.00  0.44    0.00    0.01    0.49
+p_prior[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
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+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
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+p_prior[1258]                    0.50    0.00  0.45    0.00    0.00    0.48
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+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    0.50
-p_prior[1269]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1270]                    0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1271]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1272]                    0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1273]                    0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1274]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1275]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1276]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1277]                    0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1278]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1279]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1280]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1281]                    0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1282]                    0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1283]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1284]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1285]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1286]                    0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1287]                    0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1288]                    0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1289]                    0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1290]                    0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1291]                    0.50    0.00  0.43    0.00    0.01    0.48
-p_prior[1292]                    0.50    0.00  0.43    0.00    0.01    0.49
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-p_prior[1296]                    0.50    0.00  0.43    0.00    0.02    0.50
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+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
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+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  0.45    0.00    0.00    0.48
-p_prior[1299]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1300]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1301]                    0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1302]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1303]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1304]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1305]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1306]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1307]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1308]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1309]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1310]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1311]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1312]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1313]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1314]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1315]                    0.50    0.00  0.42    0.00    0.02    0.50
+p_prior[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
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+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
@@ -2231,86 +2390,82 @@ p_prior[1323]                    0.50    0.00  0.43    0.00    0.02    0.50
 p_prior[1324]                    0.50    0.00  0.43    0.00    0.02    0.50
 p_prior[1325]                    0.50    0.00  0.43    0.00    0.02    0.50
 p_prior[1326]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1327]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1328]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1329]                    0.50    0.00  0.43    0.00    0.02    0.50
+p_prior[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  0.42    0.00    0.02    0.50
-p_prior[1332]                    0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1333]                    0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1334]                    0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1335]                    0.50    0.00  0.43    0.00    0.02    0.49
+p_prior[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_prior[1340]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1341]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1342]                    0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1343]                    0.50    0.00  0.43    0.00    0.02    0.50
 p_predicted[1]                   0.22    0.00  0.07    0.10    0.17    0.21
 p_predicted[2]                   0.22    0.00  0.07    0.10    0.17    0.21
-p_predicted[3]                   0.22    0.00  0.07    0.11    0.17    0.21
-p_predicted[4]                   0.19    0.00  0.06    0.09    0.15    0.19
-p_predicted[5]                   0.19    0.00  0.06    0.09    0.15    0.19
-p_predicted[6]                   0.19    0.00  0.06    0.09    0.15    0.18
+p_predicted[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]                   0.38    0.00  0.08    0.23    0.32    0.37
-p_predicted[10]                  0.39    0.00  0.08    0.25    0.34    0.39
-p_predicted[11]                  0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[12]                  0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[13]                  0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[14]                  0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[15]                  0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[16]                  0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[17]                  0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[18]                  0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[19]                  0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[20]                  0.02    0.00  0.02    0.00    0.01    0.01
+p_predicted[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    0.01    0.04    0.07
-p_predicted[23]                  0.38    0.00  0.04    0.30    0.35    0.38
-p_predicted[24]                  0.36    0.00  0.04    0.28    0.33    0.36
-p_predicted[25]                  0.33    0.00  0.05    0.24    0.29    0.33
-p_predicted[26]                  0.27    0.00  0.04    0.20    0.25    0.27
-p_predicted[27]                  0.26    0.00  0.04    0.19    0.23    0.26
-p_predicted[28]                  0.24    0.00  0.04    0.16    0.21    0.24
-p_predicted[29]                  0.23    0.00  0.04    0.15    0.20    0.23
-p_predicted[30]                  0.33    0.00  0.04    0.25    0.30    0.33
-p_predicted[31]                  0.33    0.00  0.04    0.25    0.30    0.33
-p_predicted[32]                  0.31    0.00  0.04    0.24    0.28    0.31
-p_predicted[33]                  0.31    0.00  0.04    0.24    0.28    0.31
-p_predicted[34]                  0.29    0.00  0.04    0.22    0.26    0.29
-p_predicted[35]                  0.29    0.00  0.04    0.22    0.26    0.29
-p_predicted[36]                  0.28    0.00  0.04    0.21    0.25    0.28
-p_predicted[37]                  0.28    0.00  0.04    0.21    0.25    0.28
-p_predicted[38]                  0.21    0.00  0.03    0.15    0.18    0.20
-p_predicted[39]                  0.21    0.00  0.03    0.15    0.18    0.20
-p_predicted[40]                  0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[41]                  0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[42]                  0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[43]                  0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[44]                  0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[45]                  0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[46]                  0.14    0.00  0.02    0.10    0.12    0.14
-p_predicted[47]                  0.14    0.00  0.02    0.10    0.12    0.14
-p_predicted[48]                  0.14    0.00  0.02    0.09    0.12    0.14
-p_predicted[49]                  0.14    0.00  0.02    0.09    0.12    0.14
-p_predicted[50]                  0.11    0.00  0.06    0.04    0.07    0.10
+p_predicted[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
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 p_predicted[52]                  0.08    0.00  0.04    0.03    0.06    0.08
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 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
@@ -2318,110 +2473,110 @@ 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
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 p_predicted[172]                 0.35    0.00  0.04    0.27    0.32    0.35
 p_predicted[173]                 0.26    0.00  0.04    0.19    0.23    0.26
-p_predicted[174]                 0.12    0.00  0.05    0.05    0.09    0.12
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 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
@@ -2433,10 +2588,10 @@ p_predicted[182]                 0.04    0.00  0.02    0.01    0.02    0.03
 p_predicted[183]                 0.04    0.00  0.02    0.01    0.02    0.03
 p_predicted[184]                 0.03    0.00  0.02    0.01    0.02    0.03
 p_predicted[185]                 0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted[186]                 0.03    0.00  0.02    0.01    0.02    0.02
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-p_predicted[188]                 0.14    0.00  0.06    0.05    0.09    0.13
-p_predicted[189]                 0.10    0.00  0.04    0.04    0.08    0.10
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+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
@@ -2444,10 +2599,10 @@ 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
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 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
@@ -2455,310 +2610,310 @@ 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
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 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
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 p_predicted[513]                 0.01    0.00  0.02    0.00    0.00    0.00
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@@ -2766,245 +2921,245 @@ p_predicted[515]                 0.01    0.00  0.02    0.00    0.00    0.00
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 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
@@ -3140,45 +3295,45 @@ 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
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 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
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 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
@@ -3187,43 +3342,43 @@ 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
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 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
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 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
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+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
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 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
@@ -3236,55 +3391,55 @@ p_predicted[985]                 0.00    0.00  0.00    0.00    0.00    0.00
 p_predicted[986]                 0.00    0.00  0.00    0.00    0.00    0.00
 p_predicted[987]                 0.00    0.00  0.00    0.00    0.00    0.00
 p_predicted[988]                 0.00    0.00  0.00    0.00    0.00    0.00
-p_predicted[989]                 0.00    0.00  0.00    0.00    0.00    0.00
-p_predicted[990]                 0.00    0.00  0.00    0.00    0.00    0.00
-p_predicted[991]                 0.00    0.00  0.00    0.00    0.00    0.00
-p_predicted[992]                 0.00    0.00  0.00    0.00    0.00    0.00
-p_predicted[993]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[994]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[995]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[996]                 0.93    0.00  0.10    0.62    0.91    0.97
-p_predicted[997]                 0.93    0.00  0.09    0.66    0.91    0.97
-p_predicted[998]                 0.94    0.00  0.09    0.67    0.93    0.98
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+p_predicted[993]                 0.91    0.00  0.10    0.63    0.88    0.95
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 p_predicted[999]                 0.00    0.00  0.00    0.00    0.00    0.00
-p_predicted[1000]                0.00    0.00  0.00    0.00    0.00    0.00
-p_predicted[1001]                0.00    0.00  0.00    0.00    0.00    0.00
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-p_predicted[1006]                0.17    0.00  0.09    0.04    0.10    0.15
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-p_predicted[1008]                0.14    0.00  0.08    0.04    0.08    0.12
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-p_predicted[1010]                0.33    0.00  0.12    0.13    0.24    0.31
-p_predicted[1011]                0.36    0.00  0.15    0.12    0.25    0.35
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 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
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-p_predicted[1016]                0.07    0.00  0.04    0.01    0.04    0.06
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-p_predicted[1021]                0.04    0.00  0.03    0.01    0.02    0.03
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+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    0.03
-p_predicted[1036]                0.04    0.00  0.03    0.00    0.02    0.03
-p_predicted[1037]                0.04    0.00  0.03    0.00    0.02    0.03
+p_predicted[1035]                0.04    0.00  0.03    0.01    0.02    0.04
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+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
@@ -3292,4322 +3447,4304 @@ 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
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-p_predicted[1050]                0.03    0.00  0.02    0.00    0.01    0.02
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-p_predicted[1052]                0.10    0.00  0.06    0.02    0.05    0.09
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 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
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 p_predicted[1092]                0.18    0.00  0.06    0.08    0.14    0.18
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 p_predicted[1094]                0.18    0.00  0.06    0.08    0.14    0.18
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 p_predicted[1097]                0.18    0.00  0.06    0.08    0.13    0.17
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-p_predicted[1103]                0.13    0.00  0.04    0.06    0.10    0.12
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-p_predicted[1106]                0.43    0.00  0.07    0.30    0.38    0.43
-p_predicted[1107]                0.43    0.00  0.07    0.30    0.38    0.43
-p_predicted[1108]                0.42    0.00  0.07    0.29    0.37    0.42
-p_predicted[1109]                0.36    0.00  0.08    0.23    0.31    0.36
-p_predicted[1110]                0.22    0.00  0.06    0.12    0.18    0.21
-p_predicted[1111]                0.25    0.00  0.06    0.16    0.21    0.24
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 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
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-predicted_difference[131]        0.22    0.00  0.38   -0.02    0.00    0.00
-predicted_difference[132]        0.44    0.00  0.43   -0.01    0.00    0.27
-predicted_difference[133]        0.44    0.00  0.44   -0.01    0.00    0.27
-predicted_difference[134]        0.44    0.00  0.43   -0.01    0.00    0.27
-predicted_difference[135]        0.39    0.00  0.44   -0.09   -0.04    0.22
-predicted_difference[136]        0.41    0.00  0.45   -0.08   -0.03    0.22
-predicted_difference[137]        0.39    0.00  0.44   -0.09   -0.04    0.22
-predicted_difference[138]        0.91    0.00  0.22    0.01    0.96    0.98
-predicted_difference[139]        0.40    0.00  0.44   -0.09   -0.03    0.24
-predicted_difference[140]        0.42    0.00  0.46   -0.08   -0.02    0.24
-predicted_difference[141]        0.40    0.00  0.44   -0.09   -0.03    0.24
-predicted_difference[142]       -0.70    0.00  0.25   -0.97   -0.87   -0.77
-predicted_difference[143]        0.09    0.00  0.13   -0.07    0.00    0.06
-predicted_difference[144]        0.20    0.00  0.41   -0.11   -0.06   -0.03
-predicted_difference[145]        0.20    0.00  0.41   -0.11   -0.06   -0.03
-predicted_difference[146]        0.02    0.00  0.44   -0.67   -0.41    0.07
-predicted_difference[147]        0.23    0.00  0.40   -0.01    0.00    0.00
-predicted_difference[148]        0.20    0.00  0.39   -0.08   -0.04   -0.02
-predicted_difference[149]       -0.03    0.00  0.12   -0.14   -0.06   -0.04
-predicted_difference[150]       -0.44    0.00  0.33   -0.81   -0.64   -0.54
-predicted_difference[151]        0.04    0.00  0.09   -0.13   -0.03    0.03
-predicted_difference[152]        0.04    0.00  0.09   -0.13   -0.03    0.03
-predicted_difference[153]        0.29    0.00  0.39   -0.16   -0.07    0.10
-predicted_difference[154]        0.34    0.00  0.42   -0.13   -0.06    0.12
-predicted_difference[155]        0.29    0.00  0.39   -0.16   -0.07    0.10
-predicted_difference[156]        0.90    0.00  0.23   -0.02    0.95    0.97
-predicted_difference[157]        0.37    0.00  0.41   -0.05   -0.02    0.15
-predicted_difference[158]        0.91    0.00  0.23    0.00    0.96    0.98
-predicted_difference[159]        0.25    0.00  0.38   -0.09   -0.04    0.03
-predicted_difference[160]        0.25    0.00  0.38   -0.09   -0.04    0.03
-predicted_difference[161]        0.99    0.00  0.01    0.98    0.99    1.00
-predicted_difference[162]        0.91    0.00  0.22    0.01    0.96    0.98
-predicted_difference[163]        0.28    0.00  0.46   -0.29   -0.13    0.08
-predicted_difference[164]        0.07    0.00  0.32   -0.28   -0.18   -0.08
-predicted_difference[165]        0.07    0.00  0.32   -0.28   -0.18   -0.08
-predicted_difference[166]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[167]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[168]        0.11    0.00  0.35   -0.30   -0.18   -0.04
-predicted_difference[169]        0.91    0.00  0.22    0.01    0.96    0.98
-lp__                          -332.26    1.49 35.27 -400.64 -356.27 -332.64
+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 10715 1.00
-mu[2]                            0.03    0.09 18058 1.00
-mu[3]                            0.03    0.10 16182 1.00
-mu[4]                           -0.01    0.05 10873 1.00
-mu[5]                            0.00    0.06  7404 1.00
-mu[6]                            0.00    0.06  7602 1.00
-mu[7]                            0.02    0.08  8196 1.00
-mu[8]                            0.03    0.09  8765 1.00
-mu[9]                            0.03    0.09 13199 1.00
-mu[10]                           0.03    0.09  9486 1.00
-mu[11]                           0.04    0.10 10276 1.00
-mu[12]                           0.00    0.06  9725 1.00
-sigma[1]                         0.35    0.55   638 1.00
-sigma[2]                         1.04    1.31  2882 1.00
-sigma[3]                         0.77    1.04  1982 1.00
-sigma[4]                         0.36    0.51  1845 1.00
-sigma[5]                         0.23    0.39   867 1.01
-sigma[6]                         0.24    0.40   666 1.00
-sigma[7]                         0.23    0.38   779 1.00
-sigma[8]                         0.22    0.37   766 1.01
-sigma[9]                         0.41    0.65   582 1.01
-sigma[10]                        0.25    0.43   544 1.01
-sigma[11]                        0.29    0.49   551 1.01
-sigma[12]                        0.36    0.57   677 1.01
-beta[1,1]                        0.05    0.37  9066 1.00
-beta[1,2]                       -0.14    0.39  8483 1.00
-beta[1,3]                        0.96    1.48  7083 1.00
-beta[1,4]                       -0.38   -0.24  7191 1.00
-beta[1,5]                        0.10    0.39  6927 1.00
-beta[1,6]                        0.15    0.44  6029 1.00
-beta[1,7]                        0.17    0.43  6011 1.00
-beta[1,8]                        0.15    0.39  6231 1.00
-beta[1,9]                        0.53    1.24  1679 1.00
-beta[1,10]                       0.09    0.40 10766 1.00
-beta[1,11]                       0.14    0.49 10642 1.00
-beta[1,12]                      -0.04    0.24  3475 1.00
-beta[2,1]                       -0.21    0.02  1215 1.00
-beta[2,2]                       -1.06   -0.72  4682 1.00
-beta[2,3]                        0.61    0.87  7252 1.00
-beta[2,4]                        0.38    0.71  4991 1.00
-beta[2,5]                        0.02    0.24  5798 1.00
-beta[2,6]                       -0.01    0.21  4610 1.00
-beta[2,7]                        0.04    0.25  6419 1.00
-beta[2,8]                        0.14    0.40  6623 1.00
-beta[2,9]                       -0.17    0.09  1075 1.01
-beta[2,10]                       0.11    0.49 12027 1.00
-beta[2,11]                       0.00    0.21  1907 1.00
-beta[2,12]                      -0.16    0.08  1447 1.00
-beta[3,1]                        0.13    0.62 12260 1.00
-beta[3,2]                        0.48    1.68 17366 1.00
-beta[3,3]                        0.33    1.23 15895 1.00
-beta[3,4]                        0.00    0.35  9815 1.00
-beta[3,5]                        0.02    0.26  6149 1.00
-beta[3,6]                        0.03    0.28  7530 1.00
-beta[3,7]                        0.04    0.28  6650 1.00
-beta[3,8]                        0.05    0.28  7512 1.00
-beta[3,9]                        0.19    0.73 13777 1.00
-beta[3,10]                       0.11    0.47 13043 1.00
-beta[3,11]                       0.13    0.54 10910 1.00
-beta[3,12]                       0.13    0.63 13387 1.00
-beta[4,1]                        0.12    0.56 12720 1.00
-beta[4,2]                       -0.04    0.68 13634 1.00
-beta[4,3]                       -0.28    0.33  7149 1.00
-beta[4,4]                        0.22    0.58  9664 1.00
-beta[4,5]                        0.07    0.33 11536 1.00
-beta[4,6]                        0.04    0.28  7322 1.00
-beta[4,7]                        0.11    0.39 11201 1.00
-beta[4,8]                        0.17    0.47  6889 1.00
-beta[4,9]                        0.07    0.47  6613 1.00
-beta[4,10]                       0.11    0.45 12593 1.00
-beta[4,11]                       0.35    0.95  1730 1.00
-beta[4,12]                      -0.01    0.32  3528 1.00
-beta[5,1]                        0.07    0.46  9932 1.00
-beta[5,2]                       -0.77    0.20  6836 1.00
-beta[5,3]                        0.67    1.69 13393 1.00
-beta[5,4]                        0.18    0.51  9419 1.00
-beta[5,5]                        0.08    0.37  9987 1.00
-beta[5,6]                        0.06    0.31  7781 1.00
-beta[5,7]                        0.16    0.48  7046 1.00
-beta[5,8]                        0.18    0.52  4719 1.00
-beta[5,9]                        0.19    0.72 13846 1.00
-beta[5,10]                       0.10    0.44 10366 1.00
-beta[5,11]                       0.21    0.70  4859 1.00
-beta[5,12]                       0.01    0.37  4764 1.00
-beta[6,1]                        0.11    0.52 12623 1.00
-beta[6,2]                        3.50    4.82  4633 1.00
-beta[6,3]                        0.44    0.86  7594 1.00
-beta[6,4]                       -0.28    0.01  4684 1.00
-beta[6,5]                       -0.01    0.21  4154 1.00
-beta[6,6]                        0.05    0.31  8138 1.00
-beta[6,7]                        0.09    0.38  8766 1.00
-beta[6,8]                        0.14    0.45  6377 1.00
-beta[6,9]                        0.21    0.72 13286 1.00
-beta[6,10]                       0.11    0.46 11752 1.00
-beta[6,11]                       0.06    0.35  4346 1.00
-beta[6,12]                       0.21    0.71  7940 1.00
-beta[7,1]                        0.05    0.42  6783 1.00
-beta[7,2]                        0.17    0.85 10672 1.00
-beta[7,3]                        1.87    3.03  3550 1.00
-beta[7,4]                        0.08    0.42  8663 1.00
-beta[7,5]                        0.00    0.22  6178 1.00
-beta[7,6]                        0.01    0.22  4993 1.00
-beta[7,7]                        0.04    0.26  6144 1.00
-beta[7,8]                        0.05    0.27  5876 1.00
-beta[7,9]                        0.23    0.80 13090 1.00
-beta[7,10]                       0.11    0.45 12114 1.00
-beta[7,11]                       0.11    0.44 11395 1.00
-beta[7,12]                       0.10    0.52 13796 1.00
-beta[8,1]                        0.14    0.62 12389 1.00
-beta[8,2]                        0.60    1.88 15778 1.00
-beta[8,3]                        0.42    1.41 14503 1.00
-beta[8,4]                        0.15    0.63 16403 1.00
-beta[8,5]                        0.07    0.39 10728 1.00
-beta[8,6]                        0.09    0.44 11903 1.00
-beta[8,7]                        0.10    0.42 11984 1.00
-beta[8,8]                        0.11    0.43 12574 1.00
-beta[8,9]                        0.18    0.75 13906 1.00
-beta[8,10]                       0.11    0.45 12990 1.00
-beta[8,11]                       0.13    0.52 12317 1.00
-beta[8,12]                       0.13    0.61 12390 1.00
-beta[9,1]                        0.12    0.57 12798 1.00
-beta[9,2]                       -0.09    0.94 11941 1.00
-beta[9,3]                       -0.15    0.52  7824 1.00
-beta[9,4]                        0.18    0.55  8193 1.00
-beta[9,5]                        0.14    0.48  6798 1.00
-beta[9,6]                        0.21    0.57  2987 1.00
-beta[9,7]                        0.22    0.58  2913 1.00
-beta[9,8]                        0.19    0.55  4076 1.00
-beta[9,9]                        0.22    0.79 12585 1.00
-beta[9,10]                       0.11    0.47 12564 1.00
-beta[9,11]                       0.09    0.44  5830 1.00
-beta[9,12]                       0.16    0.65 11484 1.00
-beta[10,1]                       0.14    0.60  9477 1.00
-beta[10,2]                       0.35    1.48 14736 1.00
-beta[10,3]                       0.32    1.22 15226 1.00
-beta[10,4]                      -0.03    0.31  8348 1.00
-beta[10,5]                       0.01    0.24  5835 1.00
-beta[10,6]                       0.02    0.25  5457 1.00
-beta[10,7]                       0.04    0.27  5433 1.00
-beta[10,8]                       0.05    0.27  6003 1.00
-beta[10,9]                       0.17    0.72 12171 1.00
-beta[10,10]                      0.11    0.46 10585 1.00
-beta[10,11]                      0.13    0.55 11180 1.00
-beta[10,12]                      0.14    0.61 11090 1.00
-beta[11,1]                       0.13    0.59 12005 1.00
-beta[11,2]                       0.43    1.63 14648 1.00
-beta[11,3]                       0.34    1.26 14895 1.00
-beta[11,4]                      -0.08    0.24  8131 1.00
-beta[11,5]                       0.00    0.23  4133 1.00
-beta[11,6]                       0.01    0.24  3814 1.00
-beta[11,7]                       0.03    0.27  5187 1.00
-beta[11,8]                       0.05    0.27  5711 1.00
-beta[11,9]                       0.18    0.72 13678 1.00
-beta[11,10]                      0.11    0.45 12344 1.00
-beta[11,11]                      0.13    0.52 11520 1.00
-beta[11,12]                      0.14    0.63 13890 1.00
-beta[12,1]                       0.01    0.31  5371 1.00
-beta[12,2]                      -0.13    0.80 11749 1.00
-beta[12,3]                       0.72    1.65 11757 1.00
-beta[12,4]                      -0.03    0.26  8842 1.00
-beta[12,5]                       0.05    0.30 10169 1.00
-beta[12,6]                       0.12    0.42  8064 1.00
-beta[12,7]                       0.13    0.42  9868 1.00
-beta[12,8]                       0.15    0.46  8561 1.00
-beta[12,9]                       0.21    0.77 11619 1.00
-beta[12,10]                      0.11    0.47 11603 1.00
-beta[12,11]                      0.18    0.62  9231 1.00
-beta[12,12]                      0.04    0.42  6645 1.00
-beta[13,1]                       0.25    0.80  5796 1.00
-beta[13,2]                       1.87    2.69  5568 1.00
-beta[13,3]                      -1.02   -0.46  3462 1.00
-beta[13,4]                       0.07    0.37 11350 1.00
-beta[13,5]                       0.04    0.30  8940 1.00
-beta[13,6]                       0.07    0.34 11574 1.00
-beta[13,7]                       0.11    0.40 11356 1.00
-beta[13,8]                       0.11    0.38 13561 1.00
-beta[13,9]                       0.11    0.55 11082 1.00
-beta[13,10]                      0.11    0.45 10950 1.00
-beta[13,11]                      0.24    0.74  3425 1.00
-beta[13,12]                     -0.01    0.31  3809 1.00
-beta[14,1]                       0.14    0.62 13510 1.00
-beta[14,2]                       0.30    1.47 16134 1.00
-beta[14,3]                       0.25    1.10 14412 1.00
-beta[14,4]                       0.01    0.36  9865 1.00
-beta[14,5]                       0.02    0.27  6691 1.00
-beta[14,6]                       0.03    0.28  6210 1.00
-beta[14,7]                       0.05    0.29  5885 1.00
-beta[14,8]                       0.07    0.30  6243 1.00
-beta[14,9]                       0.18    0.73 14036 1.00
-beta[14,10]                      0.11    0.46 11993 1.00
-beta[14,11]                      0.14    0.54 12182 1.00
-beta[14,12]                      0.14    0.61 11473 1.00
-beta[15,1]                       0.14    0.57 12226 1.00
-beta[15,2]                       0.58    1.89 15238 1.00
-beta[15,3]                       0.42    1.33 15261 1.00
-beta[15,4]                       0.16    0.64 17472 1.00
-beta[15,5]                       0.08    0.38 12029 1.00
-beta[15,6]                       0.09    0.44 14032 1.00
-beta[15,7]                       0.10    0.42 13617 1.00
-beta[15,8]                       0.11    0.40 12142 1.00
-beta[15,9]                       0.18    0.71 13177 1.00
-beta[15,10]                      0.11    0.47 10330 1.00
-beta[15,11]                      0.14    0.56 12983 1.00
-beta[15,12]                      0.14    0.66 11426 1.00
-beta[16,1]                       0.14    0.62 13668 1.00
-beta[16,2]                       0.60    1.90 17002 1.00
-beta[16,3]                       0.43    1.40 17319 1.00
-beta[16,4]                       0.15    0.61 16222 1.00
-beta[16,5]                       0.07    0.39 10587 1.00
-beta[16,6]                       0.09    0.42 12017 1.00
-beta[16,7]                       0.10    0.44 12360 1.00
-beta[16,8]                       0.11    0.41 11787 1.00
-beta[16,9]                       0.18    0.75 14589 1.00
-beta[16,10]                      0.11    0.47 12036 1.00
-beta[16,11]                      0.14    0.55 13347 1.00
-beta[16,12]                      0.14    0.63 12584 1.00
-beta[17,1]                       0.13    0.59 11039 1.00
-beta[17,2]                       0.43    1.63 15229 1.00
-beta[17,3]                       0.34    1.28 15210 1.00
-beta[17,4]                      -0.03    0.31  9408 1.00
-beta[17,5]                       0.02    0.25  6863 1.00
-beta[17,6]                       0.02    0.25  5013 1.00
-beta[17,7]                       0.04    0.26  5727 1.00
-beta[17,8]                       0.05    0.27  5150 1.00
-beta[17,9]                       0.18    0.74 12974 1.00
-beta[17,10]                      0.11    0.44 12960 1.00
-beta[17,11]                      0.14    0.53 11285 1.00
-beta[17,12]                      0.13    0.64 10869 1.00
-beta[18,1]                       0.13    0.62 11414 1.00
-beta[18,2]                       0.49    1.72 14231 1.00
-beta[18,3]                       0.36    1.33 17391 1.00
-beta[18,4]                       0.00    0.35  9838 1.00
-beta[18,5]                       0.03    0.26  8023 1.00
-beta[18,6]                       0.03    0.28  7663 1.00
-beta[18,7]                       0.06    0.31  7575 1.00
-beta[18,8]                       0.07    0.30  8218 1.00
-beta[18,9]                       0.18    0.72 12556 1.00
-beta[18,10]                      0.11    0.46 11149 1.00
-beta[18,11]                      0.14    0.56 10909 1.00
-beta[18,12]                      0.14    0.64 12515 1.00
-beta[19,1]                       0.14    0.60 11312 1.00
-beta[19,2]                       0.60    1.88 14824 1.00
-beta[19,3]                       0.43    1.42 15500 1.00
-beta[19,4]                       0.15    0.62 16133 1.00
-beta[19,5]                       0.07    0.38 11028 1.00
-beta[19,6]                       0.09    0.40 13577 1.00
-beta[19,7]                       0.10    0.41 13373 1.00
-beta[19,8]                       0.11    0.40 12737 1.00
-beta[19,9]                       0.19    0.74 13159 1.00
-beta[19,10]                      0.11    0.46 12985 1.00
-beta[19,11]                      0.14    0.54 11927 1.00
-beta[19,12]                      0.13    0.61 12493 1.00
-beta[20,1]                       0.14    0.64 12527 1.00
-beta[20,2]                       0.58    1.79 14600 1.00
-beta[20,3]                       0.42    1.34 14157 1.00
-beta[20,4]                       0.15    0.60 14624 1.00
-beta[20,5]                       0.07    0.39 12107 1.00
-beta[20,6]                       0.08    0.43 11456 1.00
-beta[20,7]                       0.10    0.42 14420 1.00
-beta[20,8]                       0.11    0.40 12973 1.00
-beta[20,9]                       0.18    0.74 13938 1.00
-beta[20,10]                      0.11    0.47 11705 1.00
-beta[20,11]                      0.14    0.55 12037 1.00
-beta[20,12]                      0.14    0.63 10747 1.00
-beta[21,1]                       0.14    0.60 12020 1.00
-beta[21,2]                       0.59    1.82 16537 1.00
-beta[21,3]                       0.43    1.35 16669 1.00
-beta[21,4]                       0.15    0.61 14566 1.00
-beta[21,5]                       0.08    0.39  8745 1.00
-beta[21,6]                       0.09    0.43 12785 1.00
-beta[21,7]                       0.10    0.42 11216 1.00
-beta[21,8]                       0.11    0.41 13303 1.00
-beta[21,9]                       0.19    0.70 13673 1.00
-beta[21,10]                      0.11    0.45 11517 1.00
-beta[21,11]                      0.14    0.56 12222 1.00
-beta[21,12]                      0.14    0.63 13741 1.00
-beta[22,1]                       0.14    0.63 12411 1.00
-beta[22,2]                       0.58    1.86 16164 1.00
-beta[22,3]                       0.43    1.40 16086 1.00
-beta[22,4]                       0.16    0.62 16735 1.00
-beta[22,5]                       0.07    0.39 11099 1.00
-beta[22,6]                       0.09    0.44 14591 1.00
-beta[22,7]                       0.10    0.42  9810 1.00
-beta[22,8]                       0.11    0.41 12341 1.00
-beta[22,9]                       0.18    0.74 14158 1.00
-beta[22,10]                      0.11    0.47 11335 1.00
-beta[22,11]                      0.14    0.54 12673 1.00
-beta[22,12]                      0.13    0.59 13421 1.00
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-mu_prior[2]                      0.03    0.10  9705 1.00
-mu_prior[3]                      0.03    0.10  9157 1.00
-mu_prior[4]                      0.03    0.10  9624 1.00
-mu_prior[5]                      0.03    0.10  9532 1.00
-mu_prior[6]                      0.03    0.10  9869 1.00
-mu_prior[7]                      0.03    0.10  9627 1.00
-mu_prior[8]                      0.03    0.10 10070 1.00
-mu_prior[9]                      0.03    0.10  9363 1.00
-mu_prior[10]                     0.03    0.10  9753 1.00
-mu_prior[11]                     0.03    0.10 10068 1.00
-mu_prior[12]                     0.03    0.10  9961 1.00
-sigma_prior[1]                   0.26    0.44  9535 1.00
-sigma_prior[2]                   0.26    0.44  9783 1.00
-sigma_prior[3]                   0.25    0.44 10247 1.00
-sigma_prior[4]                   0.26    0.44  9757 1.00
-sigma_prior[5]                   0.26    0.43  9352 1.00
-sigma_prior[6]                   0.25    0.44  9706 1.00
-sigma_prior[7]                   0.26    0.44  9938 1.00
-sigma_prior[8]                   0.25    0.43  9889 1.00
-sigma_prior[9]                   0.25    0.45 10163 1.00
-sigma_prior[10]                  0.25    0.44 10090 1.00
-sigma_prior[11]                  0.25    0.44  9750 1.00
-sigma_prior[12]                  0.25    0.44  9493 1.00
-p_prior[1]                       0.99    1.00 10222 1.00
-p_prior[2]                       0.99    1.00 10216 1.00
-p_prior[3]                       0.99    1.00 10210 1.00
-p_prior[4]                       0.99    1.00 10214 1.00
-p_prior[5]                       0.99    1.00 10213 1.00
-p_prior[6]                       0.99    1.00 10212 1.00
-p_prior[7]                       0.99    1.00 10213 1.00
-p_prior[8]                       0.99    1.00 10208 1.00
-p_prior[9]                       0.99    1.00  9824 1.00
-p_prior[10]                      0.99    1.00  9829 1.00
-p_prior[11]                      0.98    1.00  9946 1.00
-p_prior[12]                      0.98    1.00  9910 1.00
-p_prior[13]                      0.98    1.00  9907 1.00
-p_prior[14]                      0.98    1.00  9906 1.00
-p_prior[15]                      0.98    1.00  9910 1.00
-p_prior[16]                      0.98    1.00  9904 1.00
-p_prior[17]                      0.98    1.00  9904 1.00
-p_prior[18]                      0.98    1.00  9904 1.00
-p_prior[19]                      0.98    1.00  9903 1.00
-p_prior[20]                      0.98    1.00  9905 1.00
-p_prior[21]                      0.99    1.00 10227 1.00
-p_prior[22]                      0.99    1.00 10208 1.00
-p_prior[23]                      0.99    1.00  9892 1.00
-p_prior[24]                      0.99    1.00  9884 1.00
-p_prior[25]                      0.99    1.00  9869 1.00
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-p_prior[27]                      0.99    1.00  9821 1.00
-p_prior[28]                      0.99    1.00  9813 1.00
-p_prior[29]                      0.99    1.00  9809 1.00
-p_prior[30]                      0.99    1.00  9900 1.00
-p_prior[31]                      0.99    1.00  9900 1.00
-p_prior[32]                      0.99    1.00  9893 1.00
-p_prior[33]                      0.99    1.00  9893 1.00
-p_prior[34]                      0.99    1.00  9886 1.00
-p_prior[35]                      0.99    1.00  9886 1.00
-p_prior[36]                      0.99    1.00  9882 1.00
-p_prior[37]                      0.99    1.00  9882 1.00
-p_prior[38]                      0.99    1.00  9898 1.00
-p_prior[39]                      0.99    1.00  9898 1.00
-p_prior[40]                      0.99    1.00  9847 1.00
-p_prior[41]                      0.99    1.00  9847 1.00
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-p_prior[43]                      0.99    1.00  9848 1.00
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-p_prior[50]                      0.98    1.00 10201 1.00
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-p_prior[53]                      0.98    1.00 10183 1.00
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-p_prior[59]                      0.99    1.00 10182 1.00
-p_prior[60]                      0.99    1.00 10182 1.00
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-p_prior[63]                      0.99    1.00 10131 1.00
-p_prior[64]                      0.99    1.00 10131 1.00
-p_prior[65]                      0.99    1.00 10133 1.00
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-p_prior[71]                      0.99    1.00  9874 1.00
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 p_predicted_intervention[10]     1.00    1.00 10008 1.00
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-p_predicted_intervention[67]     0.48    1.00 10000 1.00
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+predicted_difference[50]         0.59    1.00 10438 1.00
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+predicted_difference[57]         0.27    1.00 10546 1.00
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+predicted_difference[71]         0.58    0.98  7625 1.00
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+predicted_difference[73]         0.83    0.96 10991 1.00
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+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
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+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
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+predicted_difference[123]        0.49    0.90  7210 1.00
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+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
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+predicted_difference[141]       -0.62    0.12  7870 1.00
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+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 Sun Apr 21 05:19:49 2024.
+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).
@@ -7615,94 +7752,304 @@ convergence, Rhat=1).
+
+

Parameter Distributions

+
+
#g1 <- group_mcmc_areas("beta",beta_list,fit,1)
+
+
+gx <- c()
+
+#grab parameters for every category with more than 8 observations
+for (i in category_count$category_id[category_count$n >= 8]) {
+    print(i)
+    
+    #Print parameter distributions
+    gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
+    ggsave(
+        paste0("./Images/DirectEffects/Parameters/group_",i,"_",gi$name,".png")
+        ,plot=gi$plot
+        )
+    gx <- c(gx,gi)
+
+    #Get Quantiles and means for parameters
+    table <- xtable(gi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex"))
+}
+
+
[1] 1
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 3 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 2
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 2 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 4
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 2 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 5
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 2 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 6
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 2 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 7
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 3 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 11
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 2 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 12
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 1 row containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
[1] 13
+
+
+
Saving 7 x 5 in image
+
+
+
+
px <- c()
+
+
+for (i in c(1,2,3,9,10,11,12)) {
+    
+    #Print parameter distributions
+    pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
+    ggsave(
+        paste0("./Images/DirectEffects/Parameters/parameters_",i,"_",pi$name,".png")
+        ,plot=pi$plot
+        )
+    px <- c(px,pi)
+
+    #Get Quantiles and means for parameters
+    table <- xtable(pi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex"))
+    
+}
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 6 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
Saving 7 x 5 in image
+Saving 7 x 5 in image
+Saving 7 x 5 in image
+
+
+
Warning: Removed 3 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 6 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 5 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+
Saving 7 x 5 in image
+
+
+
Warning: Removed 5 rows containing missing values or values outside the scale range
+(`geom_vline()`).
+
+
+

Note these have 95% outer CI and 80% inner (shaded)

+
+
print(px[4]$plot + px[7]$plot)
+
+
+
+

+
+
+
+
ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png")
+
+
Saving 7 x 5 in image
+
+
+

Counterfactuals

-
generated_ib <- gqs(
-    fit@stanmodel,
-    data=counterfact_delay,
-    draws=as.matrix(fit),
-    seed=11021585
-    )
+
generated_ib <- gqs(
+    fit@stanmodel,
+    data=counterfact_delay,
+    draws=as.matrix(fit),
+    seed=11021585
+    )
-
df_ib_p <- data.frame(
-    p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior)
-    ,p_predicted = as.vector(extract(generated_ib, pars="p_predicted")$p_predicted)
-)
-
-df_ib_prior <- data.frame(
-    mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior)
-    ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior)
-)
-
-#p_prior
-ggplot(df_ib_p, aes(x=p_prior)) +
-    geom_density() + 
-    labs(
-        title="Implied Prior Distribution P"
-        ,subtitle=""
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
+
df_ib_p <- data.frame(
+    p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior)
+    ,p_predicted = as.vector(extract(generated_ib, pars="p_predicted")$p_predicted)
+)
+
+df_ib_prior <- data.frame(
+    mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior)
+    ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior)
+)
+
+#p_prior
+ggplot(df_ib_p, aes(x=p_prior)) +
+    geom_density() + 
+    labs(
+        title="Implied Prior Distribution P"
+        ,subtitle=""
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
-

+
+
+

+
+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_p.png")
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_p.png")
Saving 7 x 5 in image
-
#p_posterior
-ggplot(df_ib_p, aes(x=p_predicted)) +
-    geom_density() + 
-    labs(
-        title="Implied Posterior Distribution P"
-        ,subtitle=""
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
+
#p_posterior
+ggplot(df_ib_p, aes(x=p_predicted)) +
+    geom_density() + 
+    labs(
+        title="Implied Posterior Distribution P"
+        ,subtitle=""
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
-

+
+
+

+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/posterior_p.png")
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/posterior_p.png")
Saving 7 x 5 in image
-
#mu_prior
-ggplot(df_ib_prior) +
-    geom_density(aes(x=mu_prior)) + 
-    labs(
-        title="Prior - Mu"
-        ,subtitle="same prior for all Mu values"
-        ,x="Mu"
-        ,y="Probability"
-    )
+
#mu_prior
+ggplot(df_ib_prior) +
+    geom_density(aes(x=mu_prior)) + 
+    labs(
+        title="Prior - Mu"
+        ,subtitle="same prior for all Mu values"
+        ,x="Mu"
+        ,y="Probability"
+    )
-

+
+
+

+
+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_mu.png")
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_mu.png")
Saving 7 x 5 in image
-
#sigma_posterior
-ggplot(df_ib_prior) +
-    geom_density(aes(x=sigma_prior)) + 
-    labs(
-        title="Prior - Sigma"
-        ,subtitle="same prior for all Sigma values"
-        ,x="Sigma"
-        ,y="Probability"
-    )
+
#sigma_posterior
+ggplot(df_ib_prior) +
+    geom_density(aes(x=sigma_prior)) + 
+    labs(
+        title="Prior - Sigma"
+        ,subtitle="same prior for all Sigma values"
+        ,x="Sigma"
+        ,y="Probability"
+    )
-

+
+
+

+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_sigma.png")
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_sigma.png")
Saving 7 x 5 in image
-
check_hmc_diagnostics(fit)
+
check_hmc_diagnostics(fit)

 Divergences:
@@ -7723,292 +8070,1424 @@ Energy: 
E-BFMI indicated possible pathological behavior:
-  Chain 2: E-BFMI = 0.184
-  Chain 4: E-BFMI = 0.192
+  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.

Intervention: Delay close of enrollment

-
counterfact_predicted_ib <- data.frame(
-    p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default)
-    ,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention)
-    ,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference)
-)
-
-
-ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: None"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
+
counterfact_predicted_ib <- data.frame(
+    p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default)
+    ,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention)
+    ,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference)
+)
+
+
+ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of 'p'"
+        ,subtitle="Intervention: None"
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
-

+
+
+

+
+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_base.png")
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_base.png")
Saving 7 x 5 in image
-
ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: Delay close of enrollment"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
+
ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of 'p'"
+        ,subtitle="Intervention: Delay close of enrollment"
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
-

+
+
+

+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_interv.png")
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_interv.png")
Saving 7 x 5 in image
-
ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of differences 'p'"
-        ,subtitle="Intervention: Delay close of enrollment"
-        ,x="Difference in 'p' under treatment"
-        ,y="Probability Density"
-    )
+
ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of differences 'p'"
+        ,subtitle="Intervention: Delay close of enrollment"
+        ,x="Difference in 'p' under treatment"
+        ,y="Probability Density"
+    )
-

+
+
+

+
+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_distdiff.png")
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_distdiff.png")
Saving 7 x 5 in image
-
pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
-    pivot_longer(X1:X169)
-
-#TODO: Fix Category names
-pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name))
-pddf_ib["category"] <-  sapply(pddf_ib$entry_idx, function(i) df$category_id[i])
-pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$groups[i])
-
-
-ggplot(pddf_ib, aes(x=value,)) +
-    geom_density(bins=100) +
-    labs(
-        title = "Distribution of predicted differences"
-        ,subtitle = "Intervention: Delay close of enrollment"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
-
-
Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`
+
get_category_count <- function(tbl, id) {
+  result <- tbl$n[tbl$category_id == id]
+  if(length(result) == 0) 0 else result
+}
+
+category_names <- sapply(1:length(beta_list$groups), 
+    function(i) sprintf("ICD-10 #%d: %s (n=%d)", 
+                       i, 
+                       beta_list$groups[i],
+                       get_category_count(category_count, i)))
+
+
pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
+    pivot_longer(X1:X168) #CHANGE_NOTE: moved from X169 to X168
+
+#TODO: Fix Category names
+pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name))
+pddf_ib["category"] <-  sapply(pddf_ib$entry_idx, function(i) df$category_id[i])
+pddf_ib["category_name"] <- sapply(
+    pddf_ib$category, 
+    function(i) category_names[i]
+    )
+  
+
+
+ggplot(pddf_ib, aes(x=value,)) +
+    geom_density(adjust=1/5) +
+    labs(
+        title = "Distribution of predicted differences"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
-

+
+
+

+
+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_styled.png")
+
    #todo: add median, mean, 40/60 quantiles as well as 
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_distdiff_styled.png")
Saving 7 x 5 in image
-
ggplot(pddf_ib, aes(x=value,)) +
-    geom_density(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=4) +
-    labs(
-        title = "Distribution of predicted differences | By Group"
-        ,subtitle = "Intervention: Delay close of enrollment"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-
-
Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`
+
ggplot(pddf_ib, aes(x=value,)) +
+    geom_density(adjust=1/5) +
+    facet_wrap(
+        ~factor(
+            category_name, 
+            levels=category_names
+            )
+        , labeller = label_wrap_gen(multi_line = TRUE)
+        , ncol=4) +
+    labs(
+        title = "Distribution of predicted differences | By Group"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
+    theme(strip.text.x = element_text(size = 8))
+
+
+
+

+
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_distdiff_by_group.png")
+
+
Saving 7 x 5 in image
+
+
ggplot(pddf_ib, aes(x=value,)) +
+    geom_histogram(bins=300) +
+    facet_wrap(
+        ~factor(
+            category_name, 
+            levels=category_names
+            )
+        , labeller = label_wrap_gen(multi_line = TRUE)
+        , ncol=4) +
+    labs(
+        title = "Histogram of predicted differences | By Group"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Predicted counts"
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
+    theme(strip.text.x = element_text(size = 8))
-

+
+
+

+
+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_by_group.png")
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_histdiff_by_group.png")
Saving 7 x 5 in image
-
ggplot(pddf_ib, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Histogram of predicted differences | By Group"
-        ,subtitle = "Intervention: Delay close of enrollment"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    #xlim(-0.25,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
+
+
+
p3 <- ggplot(pddf_ib, aes(x=value,)) +
+    geom_histogram(bins=500) +
+    labs(
+        title = "Distribution of predicted differences"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+        ,caption = "Vertical marks: 5/10/25/50/75/90/95th percentiles. Dot shows mean."
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
+
+stats <- list(
+ p5 = quantile(pddf_ib$value, 0.05),
+ p10 = quantile(pddf_ib$value, 0.10),
+ q1 = quantile(pddf_ib$value, 0.25),
+ med = median(pddf_ib$value),
+ mean = mean(pddf_ib$value),
+ q3 = quantile(pddf_ib$value, 0.75),
+ p90 = quantile(pddf_ib$value, 0.90),
+ p95 = quantile(pddf_ib$value, 0.95),
+ max_height = max(ggplot_build(p3)$data[[1]]$count),
+ y_offset = -max(ggplot_build(p3)$data[[1]]$count) * 0.05
+)
+
+p3 + 
+ # Box
+ geom_segment(data = data.frame(
+   x = c(stats$q1, stats$q3, stats$med),
+   xend = c(stats$q1, stats$q3, stats$med),
+   y = rep(stats$y_offset, 3), 
+   yend = rep(stats$y_offset * 2, 3)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ geom_segment(data = data.frame(
+   x = rep(stats$q1, 2),
+   xend = rep(stats$q3, 2),
+   y = c(stats$y_offset, stats$y_offset * 2),
+   yend = c(stats$y_offset, stats$y_offset * 2)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Inner whiskers (Q1->P10, Q3->P90)
+ geom_segment(data = data.frame(
+   x = c(stats$q1, stats$q3),
+   xend = c(stats$p10, stats$p90),
+   y = rep(stats$y_offset * 1.5, 2),
+   yend = rep(stats$y_offset * 1.5, 2)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Crossbars at P10/P90
+ geom_segment(data = data.frame(
+   x = c(stats$p10, stats$p90),
+   xend = c(stats$p10, stats$p90),
+   y = stats$y_offset * 1.3,
+   yend = stats$y_offset * 1.7
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Outer whiskers (P10->P5, P90->P95)
+ geom_segment(data = data.frame(
+   x = c(stats$p10, stats$p90),
+   xend = c(stats$p5, stats$p95),
+   y = rep(stats$y_offset * 1.5, 2),
+   yend = rep(stats$y_offset * 1.5, 2)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Crossbars at P5/P95
+ geom_segment(data = data.frame(
+   x = c(stats$p5, stats$p95),
+   xend = c(stats$p5, stats$p95),
+   y = stats$y_offset * 1.3,
+   yend = stats$y_offset * 1.7
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Mean dot
+ geom_point(data = data.frame(
+   x = stats$mean,
+   y = stats$y_offset * 1.5
+ ), aes(x = x, y = y))
-

+
+
+

+
-
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_histdiff_by_group.png")
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_histdiff_boxplot.png")
Saving 7 x 5 in image
-

Get the probability of increase over probability of a decrease

-
mean(counterfact_predicted_ib$predicted_difference)
-
-
[1] 0.1672363
+
 ggplot(pddf_ib, aes(x=value)) +
+    stat_ecdf(geom='step') +
+    labs(
+        title = "Cumulative distribution of predicted differences",
+        subtitle = "Intervention: Delay close of enrollment",
+        x = "Difference in probability of termination due to intervention",
+        y = "Cumulative Proportion"
+    )
+
+
+
+

+
+
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_cumulative_distdiff.png")
+
+
Saving 7 x 5 in image
-

Thus adding a Delay close of enrollment increases the probability of termination by 16.72% on average for the snapshots investigated.

+

Get the % of differences in the spike around zero

-
n = length(counterfact_predicted_ib$p_predicted_intervention)
-mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention)))
-
-
[1] 0.2994077
+
# get values around and above/below spike
+width <- 0.02
+spike_band_centered_zero <- mean( pddf_ib$value >= -width/2 & pddf_ib$value <= width/2)
+above_spike_band <- mean( pddf_ib$value >= width/2)
+below_spike_band <- mean( pddf_ib$value <= -width/2)
+
+# get mass above and mass below zero
+mass_below_zero <- mean( pddf_ib$value <= 0)
-
mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default)))
+

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.

+

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.

+
+
# 5%-iles
+
+summary(pddf_ib$value)
-
[1] 0.1320077
+
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
+-0.99850 -0.12919 -0.02259  0.09647  0.14531  1.00000
+
+
# Create your quantiles
+quants <- quantile(pddf_ib$value, probs = seq(0,1,0.05), type=4)
+
+# Convert to a data frame
+quant_df <- data.frame(
+  Percentile = names(quants),
+  Value = quants
+)
+kable(quant_df)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PercentileValue
0%0%-0.9985020
5%5%-0.3763454
10%10%-0.2639654
15%15%-0.2053399
20%20%-0.1628793
25%25%-0.1291890
30%30%-0.0980523
35%35%-0.0734082
40%40%-0.0547123
45%45%-0.0385514
50%50%-0.0225949
55%55%-0.0045955
60%60%-0.0000394
65%65%0.0010549
70%70%0.0509626
75%75%0.1453046
80%80%0.3425234
85%85%0.7084837
90%90%0.9250351
95%95%0.9820456
100%100%1.0000000
+
+

There seems to be some trials that are highly suceptable to this enrollment delay. Specifically, there were some

+
+
n = length(counterfact_predicted_ib$p_predicted_intervention)
+k = 100
+simulated_terminations_intervention <- mean(rbinom(n,k,as.vector(counterfact_predicted_ib$p_predicted_intervention)))
+simulated_terminations_base <-mean(rbinom(n,k,as.vector(counterfact_predicted_ib$p_predicted_default)))
+
+simulated_percentages <- (simulated_terminations_intervention - simulated_terminations_base)/k
+

The simulation above shows that this results in a percentage-point increase of about 9.6462744.

Diagnostics

-
#trace plots
-plot(fit, pars=c("mu"), plotfun="trace")
-
-
-for (i in 1:4) {
-    print(
-        mcmc_rank_overlay(
-        fit, 
-        pars=c(
-            paste0("mu[",4*i-3,"]"),
-            paste0("mu[",4*i-2,"]"),
-            paste0("mu[",4*i-1,"]"),
-            paste0("mu[",4*i,"]")
-            ), 
-        n_bins=100
-        )+  legend_move("top") +
-             scale_colour_ghibli_d("KikiMedium")
-    )
-}
+
#trace plots
+plot(fit, pars=c("mu"), plotfun="trace")
+
+
+
+

+
+
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_plot_mu.png")
+
+
Saving 7 x 5 in image
+
+
for (i in 1:3) {
+    print(
+        mcmc_rank_overlay(
+        fit, 
+        pars=c(
+            paste0("mu[",4*i-3,"]"),
+            paste0("mu[",4*i-2,"]"),
+            paste0("mu[",4*i-1,"]"),
+            paste0("mu[",4*i,"]")
+            ), 
+        n_bins=100
+        )+  legend_move("top") +
+             scale_colour_ghibli_d("KikiMedium")
+    )
+    mu_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_mu_",mu_range,".png")
+    ggsave(filename)
+}
+
+
Scale for colour is already present.
+Adding another scale for colour, which will replace the existing scale.
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+Scale for colour is already present.
+Adding another scale for colour, which will replace the existing scale.
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+Scale for colour is already present.
+Adding another scale for colour, which will replace the existing scale.
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
-
-
plot(fit, pars=c("sigma"), plotfun="trace")
-
-for (i in 1:4) {
-    print(
-        mcmc_rank_overlay(
-        fit, 
-        pars=c(
-            paste0("sigma[",4*i-3,"]"),
-            paste0("sigma[",4*i-2,"]"),
-            paste0("sigma[",4*i-1,"]"),
-            paste0("sigma[",4*i,"]")
-            ), 
-        n_bins=100
-        )+  legend_move("top") +
-             scale_colour_ghibli_d("KikiMedium")
-    )
-}
-
#other diagnostics
-logpost <- log_posterior(fit)
-nuts_prmts <- nuts_params(fit)
-posterior <- as.array(fit)
+
plot(fit, pars=c("sigma"), plotfun="trace")
+
+
+
+

+
+
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/traceplot_sigma.png")
+
+
Saving 7 x 5 in image
+
+
for (i in 1:3) {
+    print(
+        mcmc_rank_overlay(
+        fit, 
+        pars=c(
+            paste0("sigma[",4*i-3,"]"),
+            paste0("sigma[",4*i-2,"]"),
+            paste0("sigma[",4*i-1,"]"),
+            paste0("sigma[",4*i,"]")
+            ), 
+        n_bins=100
+        )+  legend_move("top") +
+             scale_colour_ghibli_d("KikiMedium")
+    )
+    sigma_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_sigma_",sigma_range,".png")
+    ggsave(filename)
+}
+
+
Scale for colour is already present.
+Adding another scale for colour, which will replace the existing scale.
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+Scale for colour is already present.
+Adding another scale for colour, which will replace the existing scale.
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+Scale for colour is already present.
+Adding another scale for colour, which will replace the existing scale.
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+
-
color_scheme_set("darkgray")
-div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
-mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05)
+
#other diagnostics
+logpost <- log_posterior(fit)
+nuts_prmts <- nuts_params(fit)
+posterior <- as.array(fit)
-
for (i in 1:4) {
-    mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                mus,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}
+
color_scheme_set("darkgray")
+div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
+mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05)
+
+
+
+

+
+
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_mu.png")
+
+
Saving 7 x 5 in image
+
-
mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
+
for (i in 1:3) {
+    mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
+    print(
+        mcmc_pairs(
+            posterior,
+            np = nuts_prmts,
+            pars=c(
+                mus,
+                "lp__"
+            ),
+            off_diag_args = list(size = 0.75)
+        )
+    )
+    mu_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_mu_",mu_range,".png")
+    ggsave(filename)
+}
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+
+
+
+
+

+
+
+
+
+
Saving 7 x 5 in image
+
-
for (i in 1:4) {
-    params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                params,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}
+
mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
+
+
+
+

+
+
+
+
ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_sigma.png")
+
+
Saving 7 x 5 in image
+
-
for (k in 1:22) {
-for (i in 1:4) {
-    params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                params,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}}
+
for (i in 1:3) {
+    params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
+    print(
+        mcmc_pairs(
+            posterior,
+            np = nuts_prmts,
+            pars=c(
+                params,
+                "lp__"
+            ),
+            off_diag_args = list(size = 0.75)
+        )
+    )
+    sigma_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_sigma_",sigma_range,".png")
+    ggsave(filename)
+}
+
+
+
+

+
-
-
-

TODO

-
+
+

TODO

+
@@ -8049,18 +9528,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) { } return false; } - const clipboard = new window.ClipboardJS('.code-copy-button', { - text: function(trigger) { - const codeEl = trigger.previousElementSibling.cloneNode(true); - for (const childEl of codeEl.children) { - if (isCodeAnnotation(childEl)) { - childEl.remove(); - } - } - return codeEl.innerText; - } - }); - clipboard.on('success', function(e) { + const onCopySuccess = function(e) { // button target const button = e.trigger; // don't keep focus @@ -8092,11 +9560,50 @@ window.document.addEventListener("DOMContentLoaded", function (event) { }, 1000); // clear code selection e.clearSelection(); + } + const getTextToCopy = function(trigger) { + const codeEl = trigger.previousElementSibling.cloneNode(true); + for (const childEl of codeEl.children) { + if (isCodeAnnotation(childEl)) { + childEl.remove(); + } + } + return codeEl.innerText; + } + const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', { + text: getTextToCopy }); - function tippyHover(el, contentFn) { + clipboard.on('success', onCopySuccess); + if (window.document.getElementById('quarto-embedded-source-code-modal')) { + // For code content inside modals, clipBoardJS needs to be initialized with a container option + // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860) + const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', { + text: getTextToCopy, + container: window.document.getElementById('quarto-embedded-source-code-modal') + }); + clipboardModal.on('success', onCopySuccess); + } + var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//); + var mailtoRegex = new RegExp(/^mailto:/); + var filterRegex = new RegExp('/' + window.location.host + '/'); + var isInternal = (href) => { + return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href); + } + // Inspect non-navigation links and adorn them if external + var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)'); + for (var i=0; i { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { }); } let selectedAnnoteEl; @@ -8163,6 +9802,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) { } 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"); @@ -8188,6 +9828,32 @@ window.document.addEventListener("DOMContentLoaded", function (event) { }); 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) { @@ -8251,4 +9917,5 @@ window.document.addEventListener("DOMContentLoaded", function (event) { + \ No newline at end of file diff --git a/r-analysis/EffectsOfEnrollmentDelay.qmd b/r-analysis/EffectsOfEnrollmentDelay.qmd index 4380921..dd2c502 100644 --- a/r-analysis/EffectsOfEnrollmentDelay.qmd +++ b/r-analysis/EffectsOfEnrollmentDelay.qmd @@ -9,6 +9,7 @@ editor: source # Setup ```{r} +library(knitr) library(bayesplot) available_mcmc(pattern = "_nuts_") library(ggplot2) @@ -32,6 +33,7 @@ options(mc.cores = parallel::detectCores()) ```{r} ################ Pull data from database ###################### library(RPostgreSQL) +host <- 'aact_db-restored-2025-01-07' driver <- dbDriver("PostgreSQL") @@ -42,7 +44,7 @@ con <- dbConnect( user='root', password='root', dbname='aact_db', - host='will-office' + host=host ) on.exit(dbDisconnect(con)) @@ -59,7 +61,7 @@ select ,fdqpe.earliest_date_observed ,fdqpe.elapsed_duration ,fdqpe.n_brands as identical_brands - ,ntbtu.brand_name_count + ,ntbtu.brand_name_counts ,fdqpe.category_id ,fdqpe.final_status ,fdqpe.h_sdi_val @@ -78,7 +80,7 @@ select --,fdqpe.l_sdi_u95 --,fdqpe.l_sdi_l95 from formatted_data_with_planned_enrollment fdqpe - join \"Formularies\".nct_to_brands_through_uspdc ntbtu + join \"Formularies\".nct_to_brand_counts_through_uspdc ntbtu on fdqpe.nct_id = ntbtu.nct_id order by fdqpe.nct_id, fdqpe.earliest_date_observed ; @@ -101,7 +103,7 @@ con <- dbConnect( user='root', password='root', dbname='aact_db', - host='will-office' + host=host ) on.exit(dbDisconnect(con)) @@ -127,7 +129,7 @@ query <- dbSendQuery( ,fdqpe.earliest_date_observed ,fdqpe.elapsed_duration ,fdqpe.n_brands as identical_brands - ,ntbtu.brand_name_count + ,ntbtu.brand_name_counts ,fdqpe.category_id ,fdqpe.final_status ,fdqpe.h_sdi_val @@ -146,7 +148,7 @@ query <- dbSendQuery( --,fdqpe.l_sdi_u95 --,fdqpe.l_sdi_l95 from formatted_data_with_planned_enrollment fdqpe - join \"Formularies\".nct_to_brands_through_uspdc ntbtu + join \"Formularies\".nct_to_brand_counts_through_uspdc ntbtu on fdqpe.nct_id = ntbtu.nct_id join cte on fdqpe.nct_id = cte.nct_id @@ -235,7 +237,7 @@ inherited_cols <- c( ,"m_sdi_val" ,"lm_sdi_val" ,"l_sdi_val" - ,"status_NYR" + ,"status_NYR"# TODO: may need to remove ,"status_EBI" ,"status_Rec" ,"status_ANR" @@ -325,6 +327,7 @@ group_mcmc_areas <- function( rename=TRUE, filter=NULL ) { + #get all parameter names params <- get_parameters(stem,class_list) @@ -339,7 +342,7 @@ group_mcmc_areas <- function( #create area plot with appropriate title p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) + - geom_vline(xintercept=0,color="grey",alpha=0.75) + geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750) d <- pivot_longer(filtdata, everything()) |> group_by(name) |> @@ -372,7 +375,8 @@ parameter_mcmc_areas <- function( parameter_name <- class_list$parameters[parameter_id] #create area plot with appropriate title p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(parameter_name,"Parameter Distribution") + ggtitle(parameter_name,"Parameter Distribution") + + geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750) d <- pivot_longer(filtdata, everything()) |> group_by(name) |> @@ -446,6 +450,109 @@ category_count <- group_trials_by_category |> group_by(category_id) |> count() ``` +```{r} +################################# DATA EXPLORATION ############################ +driver <- dbDriver("PostgreSQL") + +con <- dbConnect( + driver, + user='root', + password='root', + dbname='aact_db', + host=host + ) +#Plot histogram of count of snapshots +df3 <- dbGetQuery( + con, + "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe + group by nct_id,final_status ;" + ) +#df3 <- fetch(query3, n = -1) + +ggplot(data=df3, aes(x=count, fill=final_status)) + + geom_histogram(binwidth=1) + + ggtitle("Histogram of snapshots per trial (matched trials)") + + xlab("Snapshots per trial") +ggsave("./Images/HistSnapshots.png") + +#Plot duration for terminated vs completed +df4 <- dbGetQuery( + con, + " + select + nct_id, + start_date , + primary_completion_date, + overall_status , + primary_completion_date - start_date as duration + from ctgov.studies s + where nct_id in (select distinct nct_id from http.download_status ds) + ;" + ) +#df4 <- fetch(query4, n = -1) + +ggplot(data=df4, aes(x=duration,fill=overall_status)) + + geom_histogram()+ + ggtitle("Histogram of trial durations") + + xlab("duration")+ + facet_wrap(~overall_status) +ggsave("./Images/HistTrialDurations_Faceted.png") + +df5 <- dbGetQuery( + con, + " + with cte1 as ( + select + nct_id, + start_date , + primary_completion_date, + overall_status , + primary_completion_date - start_date as duration + from ctgov.studies s + where nct_id in (select distinct nct_id from http.download_status ds) + ), cte2 as ( + select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe + group by nct_id + ) + select a.nct_id, a.overall_status, a.duration,b.snapshot_count + from cte1 as a + join cte2 as b + on a.nct_id=b.nct_id + ;" + ) +df5$overall_status <- as.factor(df5$overall_status) + +ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) + + geom_jitter() + + ggtitle("Comparison of duration, status, and snapshot_count") + + xlab("duration") + + ylab("snapshot count") +ggsave("./Images/SnapshotsVsDurationVsTermination.png") + +dbDisconnect(con) + +#get number of trials and snapshots in each category +group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max)) +group_trials_by_category <- as.data.frame(group_trials_by_category) + +ggplot(data = group_trials_by_category, aes(x=category_id)) + + geom_bar(binwidth=1,color="black",fill="seagreen") + + scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + + labs( + title="bar chart of trial categories" + ,x="Category ID" + ,y="Count" + ) +ggsave("./Images/CategoryCounts.png") + + + +summary(df5) + +cor(df5$duration,df5$snapshot_count) +sum(df5$snapshot_count) +``` + @@ -457,6 +564,76 @@ category_count <- group_trials_by_category |> group_by(category_id) |> count() print(fit) ``` +# Parameter Distributions + + +```{r} +#g1 <- group_mcmc_areas("beta",beta_list,fit,1) + + +gx <- c() + +#grab parameters for every category with more than 8 observations +for (i in category_count$category_id[category_count$n >= 8]) { + print(i) + + #Print parameter distributions + gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups + ggsave( + paste0("./Images/DirectEffects/Parameters/group_",i,"_",gi$name,".png") + ,plot=gi$plot + ) + gx <- c(gx,gi) + + #Get Quantiles and means for parameters + table <- xtable(gi$quantiles, + floating=FALSE + ,latex.environments = NULL + ,booktabs = TRUE + ,zap=getOption("digits") + ) + write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex")) +} +``` + + + +```{r} +px <- c() + + +for (i in c(1,2,3,9,10,11,12)) { + + #Print parameter distributions + pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups + ggsave( + paste0("./Images/DirectEffects/Parameters/parameters_",i,"_",pi$name,".png") + ,plot=pi$plot + ) + px <- c(px,pi) + + #Get Quantiles and means for parameters + table <- xtable(pi$quantiles, + floating=FALSE + ,latex.environments = NULL + ,booktabs = TRUE + ,zap=getOption("digits") + ) + write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex")) + +} +``` + +Note these have 95% outer CI and 80% inner (shaded) + + + + + +```{r} +print(px[4]$plot + px[7]$plot) +ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png") +``` # Counterfactuals @@ -579,19 +756,35 @@ ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_interv ```{r} +get_category_count <- function(tbl, id) { + result <- tbl$n[tbl$category_id == id] + if(length(result) == 0) 0 else result +} + +category_names <- sapply(1:length(beta_list$groups), + function(i) sprintf("ICD-10 #%d: %s (n=%d)", + i, + beta_list$groups[i], + get_category_count(category_count, i))) +``` +```{r} pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |> - pivot_longer(X1:X169) + pivot_longer(X1:X168) #CHANGE_NOTE: moved from X169 to X168 #TODO: Fix Category names pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name)) pddf_ib["category"] <- sapply(pddf_ib$entry_idx, function(i) df$category_id[i]) -pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$groups[i]) +pddf_ib["category_name"] <- sapply( + pddf_ib$category, + function(i) category_names[i] + ) + ggplot(pddf_ib, aes(x=value,)) + - geom_density(bins=100) + + geom_density(adjust=1/5) + labs( title = "Distribution of predicted differences" ,subtitle = "Intervention: Delay close of enrollment" @@ -599,14 +792,15 @@ ggplot(pddf_ib, aes(x=value,)) + ,y = "Probability Density" ) + geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") -ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_styled.png") + #todo: add median, mean, 40/60 quantiles as well as +ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_distdiff_styled.png") ggplot(pddf_ib, aes(x=value,)) + - geom_density(bins=100) + + geom_density(adjust=1/5) + facet_wrap( ~factor( category_name, - levels=beta_list$groups + levels=category_names ) , labeller = label_wrap_gen(multi_line = TRUE) , ncol=4) + @@ -618,57 +812,185 @@ ggplot(pddf_ib, aes(x=value,)) + ) + geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + theme(strip.text.x = element_text(size = 8)) -ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_by_group.png") +ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_distdiff_by_group.png") ggplot(pddf_ib, aes(x=value,)) + - geom_histogram(bins=100) + + geom_histogram(bins=300) + facet_wrap( ~factor( category_name, - levels=beta_list$groups + levels=category_names ) , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + + , ncol=4) + labs( title = "Histogram of predicted differences | By Group" ,subtitle = "Intervention: Delay close of enrollment" ,x = "Difference in probability due to intervention" ,y = "Predicted counts" ) + - #xlim(-0.25,0.1) + geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + theme(strip.text.x = element_text(size = 8)) -ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_histdiff_by_group.png") +ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_histdiff_by_group.png") ``` -Get the probability of increase over probability of a decrease ```{r} -mean(counterfact_predicted_ib$predicted_difference) +p3 <- ggplot(pddf_ib, aes(x=value,)) + + geom_histogram(bins=500) + + labs( + title = "Distribution of predicted differences" + ,subtitle = "Intervention: Delay close of enrollment" + ,x = "Difference in probability due to intervention" + ,y = "Probability Density" + ,caption = "Vertical marks: 5/10/25/50/75/90/95th percentiles. Dot shows mean." + ) + + geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + +stats <- list( + p5 = quantile(pddf_ib$value, 0.05), + p10 = quantile(pddf_ib$value, 0.10), + q1 = quantile(pddf_ib$value, 0.25), + med = median(pddf_ib$value), + mean = mean(pddf_ib$value), + q3 = quantile(pddf_ib$value, 0.75), + p90 = quantile(pddf_ib$value, 0.90), + p95 = quantile(pddf_ib$value, 0.95), + max_height = max(ggplot_build(p3)$data[[1]]$count), + y_offset = -max(ggplot_build(p3)$data[[1]]$count) * 0.05 +) + +p3 + + # Box + geom_segment(data = data.frame( + x = c(stats$q1, stats$q3, stats$med), + xend = c(stats$q1, stats$q3, stats$med), + y = rep(stats$y_offset, 3), + yend = rep(stats$y_offset * 2, 3) + ), aes(x = x, xend = xend, y = y, yend = yend)) + + geom_segment(data = data.frame( + x = rep(stats$q1, 2), + xend = rep(stats$q3, 2), + y = c(stats$y_offset, stats$y_offset * 2), + yend = c(stats$y_offset, stats$y_offset * 2) + ), aes(x = x, xend = xend, y = y, yend = yend)) + + # Inner whiskers (Q1->P10, Q3->P90) + geom_segment(data = data.frame( + x = c(stats$q1, stats$q3), + xend = c(stats$p10, stats$p90), + y = rep(stats$y_offset * 1.5, 2), + yend = rep(stats$y_offset * 1.5, 2) + ), aes(x = x, xend = xend, y = y, yend = yend)) + + # Crossbars at P10/P90 + geom_segment(data = data.frame( + x = c(stats$p10, stats$p90), + xend = c(stats$p10, stats$p90), + y = stats$y_offset * 1.3, + yend = stats$y_offset * 1.7 + ), aes(x = x, xend = xend, y = y, yend = yend)) + + # Outer whiskers (P10->P5, P90->P95) + geom_segment(data = data.frame( + x = c(stats$p10, stats$p90), + xend = c(stats$p5, stats$p95), + y = rep(stats$y_offset * 1.5, 2), + yend = rep(stats$y_offset * 1.5, 2) + ), aes(x = x, xend = xend, y = y, yend = yend)) + + # Crossbars at P5/P95 + geom_segment(data = data.frame( + x = c(stats$p5, stats$p95), + xend = c(stats$p5, stats$p95), + y = stats$y_offset * 1.3, + yend = stats$y_offset * 1.7 + ), aes(x = x, xend = xend, y = y, yend = yend)) + + # Mean dot + geom_point(data = data.frame( + x = stats$mean, + y = stats$y_offset * 1.5 + ), aes(x = x, y = y)) +ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_histdiff_boxplot.png") ``` -Thus adding a Delay close of enrollment increases the probability of termination by 16.72% on average for -the snapshots investigated. +```{r} + ggplot(pddf_ib, aes(x=value)) + + stat_ecdf(geom='step') + + labs( + title = "Cumulative distribution of predicted differences", + subtitle = "Intervention: Delay close of enrollment", + x = "Difference in probability of termination due to intervention", + y = "Cumulative Proportion" + ) + +ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_cumulative_distdiff.png") +``` +Get the % of differences in the spike around zero +```{r} +# get values around and above/below spike +width <- 0.02 +spike_band_centered_zero <- mean( pddf_ib$value >= -width/2 & pddf_ib$value <= width/2) +above_spike_band <- mean( pddf_ib$value >= width/2) +below_spike_band <- mean( pddf_ib$value <= -width/2) + +# get mass above and mass below zero +mass_below_zero <- mean( pddf_ib$value <= 0) +``` +Looking at the spike around zero, we find that `r spike_band_centered_zero*100`% +of the probability mass is contained within the band from +[`r -width*100/2`,`r width*100/2`]. +Additionally, there was `r above_spike_band*100`% of the probability above that +-- representing those with a general increase in the probability of termination -- +and `r below_spike_band*100`% of the probability mass below the band +-- representing a decrease in the probability of termination. + +On average, if you keep the trial open instead of closing it, +`r mass_below_zero`% of trials will see a decrease in the probability of termination, +but, due to the high increase in probability of termination given termination was increased, +the mean probability of termination increases by `r stats$mean`. + +```{r} +# 5%-iles + +summary(pddf_ib$value) + +# Create your quantiles +quants <- quantile(pddf_ib$value, probs = seq(0,1,0.05), type=4) + +# Convert to a data frame +quant_df <- data.frame( + Percentile = names(quants), + Value = quants +) +kable(quant_df) +``` + +There seems to be some trials that are highly suceptable to this enrollment delay. Specifically, there were some + ```{r} n = length(counterfact_predicted_ib$p_predicted_intervention) -mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention))) -mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default))) +k = 100 +simulated_terminations_intervention <- mean(rbinom(n,k,as.vector(counterfact_predicted_ib$p_predicted_intervention))) +simulated_terminations_base <-mean(rbinom(n,k,as.vector(counterfact_predicted_ib$p_predicted_default))) + +simulated_percentages <- (simulated_terminations_intervention - simulated_terminations_base)/k ``` +The simulation above shows that this results in a percentage-point increase of about +`r simulated_percentages * 100`. + # Diagnostics ```{r} -#| eval: false +#| eval: true #trace plots plot(fit, pars=c("mu"), plotfun="trace") +ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_plot_mu.png") -for (i in 1:4) { +for (i in 1:3) { print( mcmc_rank_overlay( fit, @@ -682,14 +1004,18 @@ for (i in 1:4) { )+ legend_move("top") + scale_colour_ghibli_d("KikiMedium") ) + mu_range <- paste0(4*i-3,"-",4*i) + filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_mu_",mu_range,".png") + ggsave(filename) } ``` ```{r} -#| eval: false +#| eval: true plot(fit, pars=c("sigma"), plotfun="trace") +ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/traceplot_sigma.png") -for (i in 1:4) { +for (i in 1:3) { print( mcmc_rank_overlay( fit, @@ -703,11 +1029,14 @@ for (i in 1:4) { )+ legend_move("top") + scale_colour_ghibli_d("KikiMedium") ) + sigma_range <- paste0(4*i-3,"-",4*i) + filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_sigma_",sigma_range,".png") + ggsave(filename) } ``` ```{r} -#| eval: false +#| eval: true #other diagnostics logpost <- log_posterior(fit) nuts_prmts <- nuts_params(fit) @@ -716,15 +1045,16 @@ posterior <- as.array(fit) ``` ```{r} -#| eval: false +#| eval: true color_scheme_set("darkgray") div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4) mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05) +ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_mu.png") ``` ```{r} -#| eval: false -for (i in 1:4) { +#| eval: true +for (i in 1:3) { mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]")) print( mcmc_pairs( @@ -737,6 +1067,9 @@ for (i in 1:4) { off_diag_args = list(size = 0.75) ) ) + mu_range <- paste0(4*i-3,"-",4*i) + filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_mu_",mu_range,".png") + ggsave(filename) } @@ -744,14 +1077,15 @@ for (i in 1:4) { ``` ```{r} -#| eval: false +#| eval: true mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05) +ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_sigma.png") ``` ```{r} -#| eval: false +#| eval: true -for (i in 1:4) { +for (i in 1:3) { params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]")) print( mcmc_pairs( @@ -764,14 +1098,17 @@ for (i in 1:4) { off_diag_args = list(size = 0.75) ) ) + sigma_range <- paste0(4*i-3,"-",4*i) + filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_sigma_",sigma_range,".png") + ggsave(filename) } ``` ```{r} -#| eval: false +#| eval: true for (k in 1:22) { -for (i in 1:4) { +for (i in 1:3) { params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]")) print( mcmc_pairs( @@ -784,6 +1121,10 @@ for (i in 1:4) { off_diag_args = list(size = 0.75) ) ) + + beta_range <- paste0("k_",k,"_i_",4*i-3,"-",4*i) + filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_beta_",beta_range,".png") + ggsave(filename) }} ``` diff --git a/r-analysis/EffectsOfEnrollmentDelay/Images/DirectEffects/Parameters/group_1_Infections & Parasites.png b/r-analysis/EffectsOfEnrollmentDelay/Images/DirectEffects/Parameters/group_1_Infections & Parasites.png deleted file mode 100644 index e6bf73b..0000000 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The Effects of market conditions on recruitment and completion of clinical trials

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Will King

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Setup

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library(bayesplot)
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-
-query <- dbSendQuery(
-    con,
-#    "select * from formatted_data_with_planned_enrollment;"
-"
-select 
-    fdqpe.nct_id
-    --,fdqpe.start_date
-    --,fdqpe.current_enrollment
-    --,fdqpe.enrollment_category
-    ,fdqpe.current_status 
-    ,fdqpe.earliest_date_observed 
-    ,fdqpe.elapsed_duration
-    ,fdqpe.n_brands as identical_brands
-    ,ntbtu.brand_name_count 
-    ,fdqpe.category_id
-    ,fdqpe.final_status
-    ,fdqpe.h_sdi_val
-    --,fdqpe.h_sdi_u95
-    --,fdqpe.h_sdi_l95
-    ,fdqpe.hm_sdi_val
-    --,fdqpe.hm_sdi_u95
-    --,fdqpe.hm_sdi_l95
-    ,fdqpe.m_sdi_val
-    --,fdqpe.m_sdi_u95
-    --,fdqpe.m_sdi_l95
-    ,fdqpe.lm_sdi_val
-    --,fdqpe.lm_sdi_u95
-    --,fdqpe.lm_sdi_l95
-    ,fdqpe.l_sdi_val
-    --,fdqpe.l_sdi_u95
-    --,fdqpe.l_sdi_l95
-from formatted_data_with_planned_enrollment fdqpe
-    join \"Formularies\".nct_to_brands_through_uspdc ntbtu
-        on fdqpe.nct_id = ntbtu.nct_id 
-order by fdqpe.nct_id, fdqpe.earliest_date_observed 
-;
-"
-    )
-df <- fetch(query, n = -1)
-df <- na.omit(df)
-
-query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
-n_categories <- fetch(query2, n = -1)
-
-return(list(data=df,ncat=n_categories))
-}
-
-d <- get_data(driver)
-df <- d$data
-n_categories <- d$ncat
-
-
-
-
-################ Format Data ###########################
-
-data_formatter <- function(df) {
-categories <- df["category_id"]
-
-x <- df["elapsed_duration"]
-x["identical_brands"] <- asinh(df$identical_brands)
-x["brand_name_counts"] <- asinh(df$brand_name_count)
-x["h_sdi_val"] <- asinh(df$h_sdi_val)
-x["hm_sdi_val"] <- asinh(df$hm_sdi_val)
-x["m_sdi_val"] <- asinh(df$m_sdi_val)
-x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
-x["l_sdi_val"] <- asinh(df$l_sdi_val)
-
-
-#Setup fixed effects
-x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
-x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
-x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) 
-x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
-
-
-y <- ifelse(df["final_status"]=="Terminated",1,0)
-
-#get category list
-
-
-return(list(x=x,y=y))
-}
-
-train <- data_formatter(df)
-
-categories <- df$category_id
-
-x <- train$x
-y <- train$y
-
-
-
-

Fit Model

-
-
################################# FIT MODEL #########################################
-inherited_cols <- c(
-    "elapsed_duration"
-    #,"identical_brands"
-    #,"brand_name_counts"
-    ,"h_sdi_val"
-    ,"hm_sdi_val"
-    ,"m_sdi_val"
-    ,"lm_sdi_val"
-    ,"l_sdi_val"
-    ,"status_NYR"
-    ,"status_EBI"
-    ,"status_Rec"
-    ,"status_ANR"
-)
-
-
-
beta_list <- list(
-        groups = c(
-        `1`="Infections & Parasites",
-        `2`="Neoplasms",
-        `3`="Blood & Immune system",
-        `4`="Endocrine, Nutritional, and Metabolic",
-        `5`="Mental & Behavioral",
-        `6`="Nervous System",
-        `7`="Eye and Adnexa",
-        `8`="Ear and Mastoid",
-        `9`="Circulatory",
-        `10`="Respiratory",
-        `11`="Digestive",
-        `12`="Skin & Subcutaneaous tissue",
-        `13`="Musculoskeletal",
-        `14`="Genitourinary",
-        `15`="Pregancy, Childbirth, & Puerperium",
-        `16`="Perinatal Period",
-        `17`="Congential",
-        `18`="Symptoms, Signs etc.",
-        `19`="Injury etc.",
-        `20`="External Causes",
-        `21`="Contact with Healthcare",
-        `22`="Special Purposes"
-    ),
-    parameters = c(
-        `1`="Elapsed Duration",
-        # brands
-        `2`="asinh(Generic Brands)",
-        `3`="asinh(Competitors USPDC)",
-        # population
-        `4`="asinh(High SDI)",
-        `5`="asinh(High-Medium SDI)",
-        `6`="asinh(Medium SDI)",
-        `7`="asinh(Low-Medium SDI)",
-        `8`="asinh(Low SDI)",
-        #Status
-        `9`="status_NYR",
-        `10`="status_EBI",
-        `11`="status_Rec",
-        `12`="status_ANR"
-    )
-)
-
-get_parameters <- function(stem,class_list) {
-    #get categories and lengths
-    named <- names(class_list)
-    lengths <- sapply(named, (function (x) length(class_list[[x]])))
-    
-    #describe the grid needed
-    iter_list <- sapply(named, (function (x) 1:lengths[x]))
-    
-    #generate the list of parameters
-    pardf <- generate_parameter_df(stem, iter_list)
-    
-    #add columns with appropriate human-readable names
-    for (name in named) {
-        pardf[paste(name,"_hr",sep="")] <- as.factor(
-            sapply(pardf[name], (function (i) class_list[[name]][i]))
-        )
-    }
-     
-    return(pardf)   
-}
-
-generate_parameter_df <- function(stem, iter_list) {
-    grid <- expand.grid(iter_list)
-    grid["param_name"] <- grid %>% unite(x,colnames(grid),sep=",")
-    grid["param_name"] <- paste(stem,"[",grid$param_name,"]",sep="")
-    return(grid)
-}
-
-group_mcmc_areas <- function(
-        stem,# = "beta"
-        class_list,# = beta_list
-        stanfit,# = fit
-        group_id,# = 2
-        rename=TRUE,
-        filter=NULL
-        ) {
-    #get all parameter names
-    params <- get_parameters(stem,class_list)
-    
-    #filter down to parameters of interest
-    params <- filter(params,groups == group_id)
-    #Get dataframe with only the rows of interest
-    filtdata <- as.data.frame(stanfit)[params$param_name]
-    #rename columns
-    if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr
-    #get group name for title
-    group_name <- class_list$groups[group_id]
-    #create area plot with appropriate title
-    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
-        ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) +
-        geom_vline(xintercept=0,color="grey",alpha=0.75)
-    
-    d <- pivot_longer(filtdata, everything()) |> 
-        group_by(name) |> 
-        summarize(
-            mean=mean(value)
-            ,q025 = quantile(value,probs = 0.025)
-            ,q975 = quantile(value,probs = 0.975)
-            ,q05 = quantile(value,probs = 0.05)
-            ,q95 = quantile(value,probs = 0.95)
-            )
-    return(list(plot=p,quantiles=d,name=group_name))
-}
-
-parameter_mcmc_areas <- function(
-        stem,# = "beta"
-        class_list,# = beta_list
-        stanfit,# = fit
-        parameter_id,# = 2
-        rename=TRUE
-        ) {
-    #get all parameter names
-    params <- get_parameters(stem,class_list)
-    #filter down to parameters of interest
-    params <- filter(params,parameters == parameter_id)
-    #Get dataframe with only the rows of interest
-    filtdata <- as.data.frame(stanfit)[params$param_name]
-    #rename columns
-    if (rename) dimnames(filtdata)[[2]] <- params$groups_hr
-    #get group name for title
-    parameter_name <- class_list$parameters[parameter_id]
-    #create area plot with appropriate title
-    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
-        ggtitle(parameter_name,"Parameter Distribution")
-    
-    d <- pivot_longer(filtdata, everything()) |> 
-        group_by(name) |> 
-        summarize(
-            mean=mean(value)
-            ,q025 = quantile(value,probs = 0.025)
-            ,q975 = quantile(value,probs = 0.975)
-            ,q05 = quantile(value,probs = 0.05)
-            ,q95 = quantile(value,probs = 0.95)
-            )
-    return(list(plot=p,quantiles=d,name=parameter_name))
-}
-
-
-
#generics intervention
-brand_intervention_ib <- x[c(inherited_cols,"brand_name_counts")]
-brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand
-
-
-
counterfact_marketing_ib <- list(
-    D = ncol(x),#
-    N = nrow(x),
-    L = n_categories$count,
-    y = as.vector(y),
-    ll = as.vector(categories),
-    x = as.matrix(x),
-    mu_mean = 0,
-    mu_stdev = 0.05,
-    sigma_shape = 4,
-    sigma_rate = 20,
-    Nx = nrow(x),
-    llx = as.vector(categories),
-    counterfact_x_tilde = as.matrix(brand_intervention_ib),
-    counterfact_x = as.matrix(x)
-)
-
-
-
fit <- stan(
-    file='Hierarchal_Logistic.stan', 
-    data = counterfact_marketing_ib,
-    chains = 4,
-    iter = 5000,
-    seed = 11021585
-    )
-
-
recompiling to avoid crashing R session
-
-
-
Trying to compile a simple C file
-
-
-
Running /usr/local/lib/R/bin/R CMD SHLIB foo.c
-using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
-gcc -I"/usr/local/lib/R/include" -DNDEBUG   -I"/usr/local/lib/R/site-library/Rcpp/include/"  -I"/usr/local/lib/R/site-library/RcppEigen/include/"  -I"/usr/local/lib/R/site-library/RcppEigen/include/unsupported"  -I"/usr/local/lib/R/site-library/BH/include" -I"/usr/local/lib/R/site-library/StanHeaders/include/src/"  -I"/usr/local/lib/R/site-library/StanHeaders/include/"  -I"/usr/local/lib/R/site-library/RcppParallel/include/"  -I"/usr/local/lib/R/site-library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DUSE_STANC3 -DSTRICT_R_HEADERS  -DBOOST_PHOENIX_NO_VARIADIC_EXPRESSION  -D_HAS_AUTO_PTR_ETC=0  -include '/usr/local/lib/R/site-library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include    -fpic  -g -O2 -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -g  -c foo.c -o foo.o
-In file included from /usr/local/lib/R/site-library/RcppEigen/include/Eigen/Core:19,
-                 from /usr/local/lib/R/site-library/RcppEigen/include/Eigen/Dense:1,
-                 from /usr/local/lib/R/site-library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp:22,
-                 from <command-line>:
-/usr/local/lib/R/site-library/RcppEigen/include/Eigen/src/Core/util/Macros.h:679:10: fatal error: cmath: No such file or directory
-  679 | #include <cmath>
-      |          ^~~~~~~
-compilation terminated.
-make: *** [/usr/local/lib/R/etc/Makeconf:191: foo.o] Error 1
-
-
-
Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
-https://mc-stan.org/misc/warnings.html#bfmi-low
-
-
-
Warning: Examine the pairs() plot to diagnose sampling problems
-
-
-
-

Explore data

-
-
################################# DATA EXPLORATION ############################
-driver <- dbDriver("PostgreSQL")
-
-con <- dbConnect(
-    driver,
-    user='root',
-    password='root',
-    dbname='aact_db',
-    host='will-office'
-    )
-#Plot histogram of count of snapshots
-df3 <- dbGetQuery(
-    con,
-    "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe 
-    group by nct_id,final_status ;"
-    )
-#df3 <- fetch(query3, n = -1)
-
-ggplot(data=df3, aes(x=count, fill=final_status)) + 
-    geom_histogram(binwidth=1) +
-    ggtitle("Histogram of snapshots per trial (matched trials)") +
-    xlab("Snapshots per trial")
-
-

-
-
ggsave("./Images/HistSnapshots.png")
-
-
Saving 7 x 5 in image
-
-
#Plot duration for terminated vs completed
-df4 <- dbGetQuery(
-    con,
-    "
-    select 
-        nct_id, 
-        start_date , 
-        primary_completion_date, 
-        overall_status ,
-        primary_completion_date - start_date as duration
-    from ctgov.studies s 
-    where nct_id in (select distinct nct_id from http.download_status ds)
-    ;"
-    )
-#df4 <- fetch(query4, n = -1)
-
-ggplot(data=df4, aes(x=duration,fill=overall_status)) +
-    geom_histogram()+
-    ggtitle("Histogram of trial durations") +
-    xlab("duration")+
-    facet_wrap(~overall_status)
-
-
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
-
-
-

-
-
ggsave("./Images/HistTrialDurations_Faceted.png")
-
-
Saving 7 x 5 in image
-`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
-
-
df5 <- dbGetQuery(
-    con,
-    "
-    with cte1 as (
-    select 
-        nct_id, 
-        start_date , 
-        primary_completion_date, 
-        overall_status ,
-        primary_completion_date - start_date as duration
-    from ctgov.studies s 
-    where nct_id in (select distinct nct_id from http.download_status ds)
-    ), cte2 as (
-    select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe
-    group by nct_id
-    )
-    select a.nct_id, a.overall_status, a.duration,b.snapshot_count
-    from cte1 as a
-        join cte2 as b
-            on a.nct_id=b.nct_id
-    ;"
-    )
-df5$overall_status <- as.factor(df5$overall_status)
-
-ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
-    geom_jitter() +
-    ggtitle("Comparison of duration, status, and snapshot_count") +
-    xlab("duration") +
-    ylab("snapshot count")
-
-

-
-
ggsave("./Images/SnapshotsVsDurationVsTermination.png")
-
-
Saving 7 x 5 in image
-
-
dbDisconnect(con)
-
-
[1] TRUE
-
-
#get number of trials and snapshots in each category
-group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
-group_trials_by_category <- as.data.frame(group_trials_by_category)
-
-ggplot(data = group_trials_by_category, aes(x=category_id)) +
-    geom_bar(binwidth=1,color="black",fill="seagreen") +
-    scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + 
-    labs(
-        title="bar chart of trial categories"
-        ,x="Category ID"
-        ,y="Count"
-    )
-
-
Warning in geom_bar(binwidth = 1, color = "black", fill = "seagreen"): Ignoring
-unknown parameters: `binwidth`
-
-
-

-
-
ggsave("./Images/CategoryCounts.png")
-
-
Saving 7 x 5 in image
-
-
summary(df5)
-
-
    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
-
-
-
-
category_count <- group_trials_by_category |> group_by(category_id) |> count()
-
-
-
-

Fit Results

-
-
################################# ANALYZE #####################################
-print(fit)
-
-
Inference for Stan model: anon_model.
-4 chains, each with iter=5000; warmup=2500; thin=1; 
-post-warmup draws per chain=2500, total post-warmup draws=10000.
-
-                                  mean se_mean    sd    2.5%     25%     50%
-mu[1]                            -0.02    0.00  0.05   -0.12   -0.06   -0.03
-mu[2]                             0.00    0.00  0.05   -0.10   -0.04    0.00
-mu[3]                             0.00    0.00  0.05   -0.10   -0.03    0.00
-mu[4]                            -0.04    0.00  0.05   -0.14   -0.08   -0.04
-mu[5]                            -0.04    0.00  0.05   -0.13   -0.07   -0.04
-mu[6]                            -0.03    0.00  0.05   -0.13   -0.06   -0.03
-mu[7]                            -0.01    0.00  0.05   -0.11   -0.04   -0.01
-mu[8]                             0.00    0.00  0.05   -0.09   -0.03    0.00
-mu[9]                             0.00    0.00  0.05   -0.10   -0.04    0.00
-mu[10]                            0.00    0.00  0.05   -0.10   -0.04    0.00
-mu[11]                            0.00    0.00  0.05   -0.09   -0.03    0.00
-mu[12]                           -0.03    0.00  0.05   -0.13   -0.06   -0.03
-sigma[1]                          0.27    0.00  0.12    0.07    0.19    0.26
-sigma[2]                          0.91    0.00  0.19    0.57    0.78    0.90
-sigma[3]                          0.66    0.00  0.18    0.34    0.54    0.65
-sigma[4]                          0.31    0.00  0.09    0.15    0.24    0.30
-sigma[5]                          0.18    0.00  0.09    0.05    0.12    0.17
-sigma[6]                          0.19    0.00  0.09    0.06    0.12    0.18
-sigma[7]                          0.18    0.00  0.09    0.05    0.12    0.17
-sigma[8]                          0.17    0.00  0.08    0.05    0.11    0.16
-sigma[9]                          0.32    0.01  0.15    0.08    0.21    0.30
-sigma[10]                         0.19    0.00  0.10    0.05    0.12    0.18
-sigma[11]                         0.23    0.00  0.12    0.06    0.14    0.21
-sigma[12]                         0.28    0.00  0.13    0.09    0.19    0.27
-beta[1,1]                        -0.10    0.00  0.25   -0.65   -0.24   -0.09
-beta[1,2]                        -0.42    0.00  0.42   -1.23   -0.71   -0.42
-beta[1,3]                         0.68    0.00  0.40   -0.07    0.41    0.67
-beta[1,4]                        -0.46    0.00  0.12   -0.71   -0.54   -0.46
-beta[1,5]                         0.00    0.00  0.18   -0.35   -0.11   -0.01
-beta[1,6]                         0.05    0.00  0.18   -0.29   -0.07    0.03
-beta[1,7]                         0.07    0.00  0.17   -0.24   -0.04    0.06
-beta[1,8]                         0.06    0.00  0.15   -0.23   -0.04    0.05
-beta[1,9]                         0.32    0.01  0.38   -0.24    0.06    0.25
-beta[1,10]                       -0.03    0.00  0.22   -0.53   -0.14   -0.02
-beta[1,11]                        0.02    0.00  0.22   -0.43   -0.10    0.02
-beta[1,12]                       -0.22    0.00  0.27   -0.82   -0.37   -0.19
-beta[2,1]                        -0.41    0.01  0.26   -0.99   -0.58   -0.39
-beta[2,2]                        -1.24    0.00  0.27   -1.78   -1.43   -1.24
-beta[2,3]                         0.47    0.00  0.20    0.08    0.34    0.47
-beta[2,4]                         0.25    0.00  0.22   -0.14    0.10    0.23
-beta[2,5]                        -0.09    0.00  0.18   -0.51   -0.20   -0.08
-beta[2,6]                        -0.12    0.00  0.19   -0.55   -0.23   -0.11
-beta[2,7]                        -0.07    0.00  0.17   -0.46   -0.17   -0.06
-beta[2,8]                         0.05    0.00  0.16   -0.25   -0.05    0.04
-beta[2,9]                        -0.48    0.01  0.40   -1.43   -0.71   -0.41
-beta[2,10]                        0.00    0.00  0.23   -0.48   -0.12    0.00
-beta[2,11]                       -0.14    0.00  0.21   -0.61   -0.26   -0.12
-beta[2,12]                       -0.36    0.01  0.27   -0.96   -0.53   -0.33
-beta[3,1]                        -0.03    0.00  0.30   -0.65   -0.19   -0.03
-beta[3,2]                        -0.12    0.01  0.93   -2.03   -0.71   -0.11
-beta[3,3]                        -0.10    0.01  0.69   -1.52   -0.52   -0.09
-beta[3,4]                        -0.19    0.00  0.29   -0.80   -0.37   -0.18
-beta[3,5]                        -0.10    0.00  0.20   -0.56   -0.20   -0.08
-beta[3,6]                        -0.10    0.00  0.21   -0.57   -0.21   -0.08
-beta[3,7]                        -0.08    0.00  0.20   -0.52   -0.18   -0.06
-beta[3,8]                        -0.06    0.00  0.19   -0.50   -0.16   -0.04
-beta[3,9]                         0.00    0.00  0.34   -0.70   -0.19    0.00
-beta[3,10]                        0.00    0.00  0.22   -0.48   -0.11    0.00
-beta[3,11]                        0.00    0.00  0.26   -0.57   -0.13    0.00
-beta[3,12]                       -0.03    0.00  0.31   -0.67   -0.20   -0.04
-beta[4,1]                        -0.04    0.00  0.29   -0.62   -0.20   -0.04
-beta[4,2]                        -0.43    0.00  0.57   -1.59   -0.80   -0.42
-beta[4,3]                        -0.67    0.01  0.55   -1.85   -1.02   -0.63
-beta[4,4]                         0.06    0.00  0.25   -0.42   -0.10    0.06
-beta[4,5]                        -0.03    0.00  0.17   -0.39   -0.13   -0.03
-beta[4,6]                        -0.08    0.00  0.19   -0.49   -0.18   -0.07
-beta[4,7]                         0.00    0.00  0.18   -0.37   -0.10    0.00
-beta[4,8]                         0.07    0.00  0.18   -0.26   -0.04    0.06
-beta[4,9]                        -0.13    0.00  0.34   -0.91   -0.30   -0.10
-beta[4,10]                       -0.01    0.00  0.22   -0.49   -0.12   -0.01
-beta[4,11]                        0.21    0.01  0.30   -0.23    0.01    0.15
-beta[4,12]                       -0.21    0.01  0.31   -0.95   -0.37   -0.17
-beta[5,1]                        -0.10    0.00  0.29   -0.75   -0.26   -0.08
-beta[5,2]                        -1.41    0.01  0.92   -3.40   -1.98   -1.35
-beta[5,3]                         0.26    0.01  0.67   -1.01   -0.18    0.23
-beta[5,4]                         0.02    0.00  0.24   -0.45   -0.13    0.02
-beta[5,5]                        -0.02    0.00  0.18   -0.38   -0.12   -0.02
-beta[5,6]                        -0.05    0.00  0.19   -0.45   -0.15   -0.04
-beta[5,7]                         0.05    0.00  0.19   -0.29   -0.06    0.04
-beta[5,8]                         0.09    0.00  0.19   -0.24   -0.03    0.07
-beta[5,9]                         0.01    0.00  0.33   -0.67   -0.17    0.00
-beta[5,10]                       -0.01    0.00  0.22   -0.49   -0.12   -0.01
-beta[5,11]                        0.08    0.00  0.26   -0.38   -0.07    0.05
-beta[5,12]                       -0.18    0.00  0.31   -0.89   -0.33   -0.14
-beta[6,1]                        -0.06    0.00  0.29   -0.67   -0.22   -0.06
-beta[6,2]                         2.93    0.01  0.88    1.33    2.32    2.89
-beta[6,3]                         0.21    0.00  0.34   -0.48   -0.02    0.21
-beta[6,4]                        -0.48    0.00  0.28   -1.09   -0.65   -0.46
-beta[6,5]                        -0.14    0.00  0.20   -0.59   -0.25   -0.12
-beta[6,6]                        -0.07    0.00  0.19   -0.48   -0.18   -0.06
-beta[6,7]                        -0.01    0.00  0.18   -0.39   -0.12   -0.01
-beta[6,8]                         0.05    0.00  0.18   -0.29   -0.06    0.04
-beta[6,9]                         0.02    0.00  0.33   -0.65   -0.17    0.01
-beta[6,10]                        0.00    0.00  0.22   -0.47   -0.12    0.00
-beta[6,11]                       -0.10    0.00  0.26   -0.70   -0.22   -0.07
-beta[6,12]                        0.04    0.00  0.30   -0.52   -0.14    0.02
-beta[7,1]                        -0.14    0.00  0.31   -0.84   -0.30   -0.10
-beta[7,2]                        -0.18    0.01  0.53   -1.24   -0.52   -0.17
-beta[7,3]                         1.42    0.01  0.73    0.17    0.91    1.36
-beta[7,4]                        -0.08    0.00  0.25   -0.58   -0.24   -0.08
-beta[7,5]                        -0.11    0.00  0.19   -0.54   -0.22   -0.09
-beta[7,6]                        -0.12    0.00  0.20   -0.57   -0.23   -0.10
-beta[7,7]                        -0.08    0.00  0.19   -0.50   -0.18   -0.06
-beta[7,8]                        -0.06    0.00  0.19   -0.50   -0.16   -0.05
-beta[7,9]                         0.05    0.00  0.34   -0.62   -0.14    0.04
-beta[7,10]                        0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[7,11]                       -0.03    0.00  0.24   -0.58   -0.15   -0.02
-beta[7,12]                       -0.06    0.00  0.29   -0.68   -0.23   -0.06
-beta[8,1]                        -0.03    0.00  0.31   -0.67   -0.20   -0.03
-beta[8,2]                         0.01    0.01  0.94   -1.87   -0.59    0.01
-beta[8,3]                         0.00    0.01  0.69   -1.42   -0.42    0.00
-beta[8,4]                        -0.04    0.00  0.32   -0.70   -0.24   -0.04
-beta[8,5]                        -0.04    0.00  0.20   -0.45   -0.15   -0.04
-beta[8,6]                        -0.03    0.00  0.22   -0.49   -0.15   -0.03
-beta[8,7]                        -0.01    0.00  0.21   -0.44   -0.12   -0.01
-beta[8,8]                         0.00    0.00  0.20   -0.41   -0.10    0.00
-beta[8,9]                         0.00    0.00  0.35   -0.74   -0.19    0.00
-beta[8,10]                        0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[8,11]                        0.00    0.00  0.25   -0.53   -0.13    0.00
-beta[8,12]                       -0.04    0.00  0.31   -0.69   -0.21   -0.04
-beta[9,1]                        -0.04    0.00  0.30   -0.68   -0.20   -0.04
-beta[9,2]                        -0.64    0.01  0.83   -2.39   -1.16   -0.60
-beta[9,3]                        -0.56    0.01  0.60   -1.84   -0.93   -0.52
-beta[9,4]                         0.02    0.00  0.26   -0.52   -0.15    0.02
-beta[9,5]                         0.03    0.00  0.19   -0.32   -0.09    0.02
-beta[9,6]                         0.10    0.00  0.21   -0.24   -0.04    0.07
-beta[9,7]                         0.11    0.00  0.20   -0.23   -0.02    0.08
-beta[9,8]                         0.10    0.00  0.19   -0.23   -0.02    0.07
-beta[9,9]                         0.05    0.00  0.34   -0.63   -0.14    0.03
-beta[9,10]                        0.00    0.00  0.23   -0.47   -0.12    0.00
-beta[9,11]                       -0.05    0.00  0.26   -0.63   -0.17   -0.03
-beta[9,12]                       -0.01    0.00  0.31   -0.64   -0.18   -0.02
-beta[10,1]                       -0.03    0.00  0.30   -0.64   -0.19   -0.03
-beta[10,2]                       -0.24    0.01  0.89   -2.03   -0.80   -0.22
-beta[10,3]                       -0.11    0.01  0.68   -1.53   -0.52   -0.09
-beta[10,4]                       -0.22    0.00  0.29   -0.85   -0.39   -0.20
-beta[10,5]                       -0.11    0.00  0.20   -0.56   -0.21   -0.09
-beta[10,6]                       -0.11    0.00  0.21   -0.59   -0.22   -0.09
-beta[10,7]                       -0.09    0.00  0.20   -0.55   -0.19   -0.07
-beta[10,8]                       -0.07    0.00  0.19   -0.51   -0.16   -0.05
-beta[10,9]                       -0.01    0.00  0.35   -0.73   -0.19   -0.01
-beta[10,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[10,11]                       0.00    0.00  0.26   -0.55   -0.13    0.00
-beta[10,12]                      -0.04    0.00  0.32   -0.68   -0.21   -0.04
-beta[11,1]                       -0.03    0.00  0.30   -0.65   -0.20   -0.03
-beta[11,2]                       -0.15    0.01  0.90   -2.00   -0.71   -0.14
-beta[11,3]                       -0.08    0.01  0.68   -1.47   -0.50   -0.07
-beta[11,4]                       -0.27    0.00  0.28   -0.88   -0.44   -0.25
-beta[11,5]                       -0.12    0.00  0.20   -0.59   -0.22   -0.10
-beta[11,6]                       -0.12    0.00  0.21   -0.62   -0.23   -0.10
-beta[11,7]                       -0.09    0.00  0.21   -0.58   -0.20   -0.07
-beta[11,8]                       -0.07    0.00  0.19   -0.52   -0.18   -0.05
-beta[11,9]                        0.00    0.00  0.35   -0.75   -0.19    0.00
-beta[11,10]                      -0.01    0.00  0.22   -0.48   -0.12    0.00
-beta[11,11]                      -0.01    0.00  0.26   -0.55   -0.14    0.00
-beta[11,12]                      -0.04    0.00  0.32   -0.70   -0.21   -0.04
-beta[12,1]                       -0.17    0.00  0.29   -0.85   -0.32   -0.14
-beta[12,2]                       -0.71    0.01  0.84   -2.48   -1.25   -0.68
-beta[12,3]                        0.34    0.01  0.62   -0.81   -0.06    0.32
-beta[12,4]                       -0.19    0.00  0.24   -0.70   -0.33   -0.17
-beta[12,5]                       -0.06    0.00  0.18   -0.46   -0.16   -0.06
-beta[12,6]                        0.01    0.00  0.19   -0.36   -0.11    0.00
-beta[12,7]                        0.02    0.00  0.18   -0.33   -0.09    0.01
-beta[12,8]                        0.05    0.00  0.18   -0.30   -0.06    0.04
-beta[12,9]                        0.04    0.00  0.33   -0.63   -0.14    0.02
-beta[12,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[12,11]                       0.05    0.00  0.26   -0.43   -0.09    0.04
-beta[12,12]                      -0.14    0.00  0.30   -0.82   -0.30   -0.12
-beta[13,1]                        0.10    0.00  0.30   -0.41   -0.08    0.06
-beta[13,2]                        1.52    0.01  0.55    0.53    1.15    1.49
-beta[13,3]                       -1.42    0.01  0.55   -2.60   -1.76   -1.37
-beta[13,4]                       -0.09    0.00  0.24   -0.58   -0.24   -0.08
-beta[13,5]                       -0.07    0.00  0.18   -0.46   -0.17   -0.06
-beta[13,6]                       -0.03    0.00  0.18   -0.41   -0.14   -0.03
-beta[13,7]                        0.01    0.00  0.18   -0.36   -0.10    0.00
-beta[13,8]                        0.01    0.00  0.18   -0.35   -0.09    0.01
-beta[13,9]                       -0.07    0.00  0.32   -0.80   -0.24   -0.05
-beta[13,10]                       0.00    0.00  0.22   -0.46   -0.11    0.00
-beta[13,11]                       0.12    0.00  0.26   -0.33   -0.04    0.08
-beta[13,12]                      -0.21    0.01  0.31   -0.94   -0.38   -0.17
-beta[14,1]                       -0.03    0.00  0.31   -0.67   -0.19   -0.03
-beta[14,2]                       -0.29    0.01  0.91   -2.12   -0.87   -0.27
-beta[14,3]                       -0.17    0.01  0.66   -1.56   -0.58   -0.15
-beta[14,4]                       -0.19    0.00  0.30   -0.83   -0.36   -0.17
-beta[14,5]                       -0.10    0.00  0.20   -0.55   -0.20   -0.08
-beta[14,6]                       -0.09    0.00  0.21   -0.57   -0.20   -0.07
-beta[14,7]                       -0.07    0.00  0.20   -0.53   -0.18   -0.05
-beta[14,8]                       -0.05    0.00  0.19   -0.49   -0.14   -0.03
-beta[14,9]                       -0.01    0.00  0.35   -0.74   -0.20   -0.01
-beta[14,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[14,11]                       0.01    0.00  0.26   -0.53   -0.13    0.01
-beta[14,12]                      -0.03    0.00  0.31   -0.69   -0.21   -0.03
-beta[15,1]                       -0.03    0.00  0.29   -0.62   -0.19   -0.03
-beta[15,2]                       -0.01    0.01  0.95   -1.91   -0.60   -0.01
-beta[15,3]                       -0.01    0.01  0.68   -1.38   -0.43    0.00
-beta[15,4]                       -0.04    0.00  0.33   -0.70   -0.24   -0.04
-beta[15,5]                       -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[15,6]                       -0.03    0.00  0.22   -0.49   -0.15   -0.03
-beta[15,7]                       -0.01    0.00  0.21   -0.44   -0.12   -0.01
-beta[15,8]                        0.00    0.00  0.20   -0.42   -0.10    0.00
-beta[15,9]                       -0.01    0.00  0.35   -0.74   -0.20   -0.01
-beta[15,10]                       0.00    0.00  0.22   -0.48   -0.11    0.00
-beta[15,11]                       0.00    0.00  0.26   -0.55   -0.13    0.00
-beta[15,12]                      -0.03    0.00  0.32   -0.68   -0.20   -0.04
-beta[16,1]                       -0.02    0.00  0.31   -0.65   -0.19   -0.02
-beta[16,2]                        0.00    0.01  0.95   -1.90   -0.59    0.00
-beta[16,3]                        0.00    0.01  0.69   -1.40   -0.42    0.00
-beta[16,4]                       -0.04    0.00  0.33   -0.69   -0.24   -0.05
-beta[16,5]                       -0.04    0.00  0.21   -0.48   -0.15   -0.04
-beta[16,6]                       -0.03    0.00  0.21   -0.47   -0.15   -0.03
-beta[16,7]                       -0.01    0.00  0.21   -0.44   -0.12   -0.01
-beta[16,8]                        0.00    0.00  0.20   -0.42   -0.10    0.00
-beta[16,9]                        0.00    0.00  0.36   -0.75   -0.19   -0.01
-beta[16,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[16,11]                       0.00    0.00  0.26   -0.55   -0.13    0.00
-beta[16,12]                      -0.03    0.00  0.31   -0.68   -0.20   -0.03
-beta[17,1]                       -0.03    0.00  0.31   -0.68   -0.20   -0.03
-beta[17,2]                       -0.16    0.01  0.91   -1.99   -0.73   -0.14
-beta[17,3]                       -0.10    0.01  0.70   -1.53   -0.52   -0.08
-beta[17,4]                       -0.22    0.00  0.29   -0.86   -0.38   -0.20
-beta[17,5]                       -0.11    0.00  0.20   -0.56   -0.21   -0.09
-beta[17,6]                       -0.11    0.00  0.20   -0.57   -0.22   -0.09
-beta[17,7]                       -0.09    0.00  0.20   -0.53   -0.19   -0.07
-beta[17,8]                       -0.07    0.00  0.19   -0.51   -0.17   -0.05
-beta[17,9]                       -0.01    0.00  0.35   -0.72   -0.19   -0.01
-beta[17,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[17,11]                       0.00    0.00  0.26   -0.55   -0.13    0.00
-beta[17,12]                      -0.03    0.00  0.32   -0.68   -0.21   -0.04
-beta[18,1]                       -0.03    0.00  0.31   -0.67   -0.19   -0.03
-beta[18,2]                       -0.13    0.01  0.93   -2.01   -0.72   -0.12
-beta[18,3]                       -0.06    0.01  0.69   -1.47   -0.47   -0.04
-beta[18,4]                       -0.19    0.00  0.29   -0.82   -0.36   -0.17
-beta[18,5]                       -0.09    0.00  0.20   -0.54   -0.20   -0.07
-beta[18,6]                       -0.09    0.00  0.20   -0.54   -0.20   -0.07
-beta[18,7]                       -0.06    0.00  0.20   -0.51   -0.17   -0.05
-beta[18,8]                       -0.05    0.00  0.19   -0.48   -0.15   -0.03
-beta[18,9]                       -0.01    0.00  0.35   -0.74   -0.19   -0.01
-beta[18,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[18,11]                       0.00    0.00  0.27   -0.56   -0.13    0.00
-beta[18,12]                      -0.04    0.00  0.32   -0.70   -0.21   -0.04
-beta[19,1]                       -0.02    0.00  0.29   -0.64   -0.19   -0.03
-beta[19,2]                        0.00    0.01  0.95   -1.88   -0.59    0.00
-beta[19,3]                        0.00    0.01  0.70   -1.39   -0.43    0.00
-beta[19,4]                       -0.05    0.00  0.33   -0.70   -0.25   -0.05
-beta[19,5]                       -0.03    0.00  0.21   -0.45   -0.14   -0.04
-beta[19,6]                       -0.03    0.00  0.21   -0.47   -0.15   -0.03
-beta[19,7]                       -0.01    0.00  0.21   -0.45   -0.13   -0.01
-beta[19,8]                        0.00    0.00  0.20   -0.41   -0.10    0.00
-beta[19,9]                        0.00    0.00  0.35   -0.74   -0.19   -0.01
-beta[19,10]                       0.00    0.00  0.22   -0.46   -0.12    0.00
-beta[19,11]                       0.00    0.00  0.26   -0.54   -0.13    0.00
-beta[19,12]                      -0.03    0.00  0.31   -0.68   -0.20   -0.03
-beta[20,1]                       -0.02    0.00  0.31   -0.64   -0.19   -0.02
-beta[20,2]                       -0.02    0.01  0.91   -1.82   -0.61   -0.02
-beta[20,3]                        0.00    0.01  0.68   -1.36   -0.43    0.00
-beta[20,4]                       -0.04    0.00  0.33   -0.70   -0.25   -0.05
-beta[20,5]                       -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[20,6]                       -0.03    0.00  0.22   -0.49   -0.15   -0.03
-beta[20,7]                       -0.01    0.00  0.21   -0.45   -0.13   -0.01
-beta[20,8]                        0.00    0.00  0.19   -0.40   -0.10    0.00
-beta[20,9]                        0.00    0.00  0.35   -0.73   -0.19    0.00
-beta[20,10]                      -0.01    0.00  0.23   -0.49   -0.11   -0.01
-beta[20,11]                       0.00    0.00  0.26   -0.53   -0.13    0.00
-beta[20,12]                      -0.03    0.00  0.31   -0.65   -0.20   -0.03
-beta[21,1]                       -0.03    0.00  0.31   -0.65   -0.19   -0.03
-beta[21,2]                        0.00    0.01  0.92   -1.84   -0.58   -0.01
-beta[21,3]                        0.00    0.01  0.68   -1.39   -0.44   -0.01
-beta[21,4]                       -0.04    0.00  0.32   -0.68   -0.24   -0.05
-beta[21,5]                       -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[21,6]                       -0.03    0.00  0.22   -0.49   -0.14   -0.03
-beta[21,7]                       -0.01    0.00  0.21   -0.43   -0.13   -0.01
-beta[21,8]                        0.00    0.00  0.20   -0.41   -0.11    0.00
-beta[21,9]                        0.00    0.00  0.35   -0.73   -0.19   -0.01
-beta[21,10]                       0.00    0.00  0.22   -0.47   -0.11    0.00
-beta[21,11]                       0.01    0.00  0.26   -0.53   -0.13    0.00
-beta[21,12]                      -0.03    0.00  0.31   -0.67   -0.20   -0.03
-beta[22,1]                       -0.02    0.00  0.31   -0.65   -0.19   -0.03
-beta[22,2]                       -0.01    0.01  0.92   -1.86   -0.60   -0.01
-beta[22,3]                        0.00    0.01  0.69   -1.35   -0.43    0.00
-beta[22,4]                       -0.04    0.00  0.33   -0.71   -0.24   -0.04
-beta[22,5]                       -0.04    0.00  0.21   -0.47   -0.15   -0.04
-beta[22,6]                       -0.03    0.00  0.22   -0.47   -0.15   -0.03
-beta[22,7]                       -0.01    0.00  0.21   -0.43   -0.13   -0.02
-beta[22,8]                        0.01    0.00  0.20   -0.41   -0.10    0.01
-beta[22,9]                       -0.01    0.00  0.35   -0.75   -0.20   -0.01
-beta[22,10]                       0.00    0.00  0.22   -0.46   -0.11    0.00
-beta[22,11]                       0.00    0.00  0.26   -0.53   -0.13    0.00
-beta[22,12]                      -0.03    0.00  0.30   -0.64   -0.20   -0.04
-mu_prior[1]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[2]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[3]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[4]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[5]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[6]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[7]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[8]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[9]                       0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[10]                      0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[11]                      0.00    0.00  0.05   -0.10   -0.03    0.00
-mu_prior[12]                      0.00    0.00  0.05   -0.10   -0.03    0.00
-sigma_prior[1]                    0.20    0.00  0.10    0.06    0.13    0.18
-sigma_prior[2]                    0.20    0.00  0.10    0.05    0.13    0.18
-sigma_prior[3]                    0.20    0.00  0.10    0.05    0.12    0.18
-sigma_prior[4]                    0.20    0.00  0.10    0.06    0.13    0.18
-sigma_prior[5]                    0.20    0.00  0.10    0.05    0.13    0.18
-sigma_prior[6]                    0.20    0.00  0.10    0.05    0.13    0.18
-sigma_prior[7]                    0.20    0.00  0.10    0.05    0.13    0.18
-sigma_prior[8]                    0.20    0.00  0.10    0.06    0.13    0.18
-sigma_prior[9]                    0.20    0.00  0.10    0.05    0.13    0.18
-sigma_prior[10]                   0.20    0.00  0.10    0.06    0.13    0.18
-sigma_prior[11]                   0.20    0.00  0.10    0.05    0.13    0.18
-sigma_prior[12]                   0.20    0.00  0.10    0.06    0.13    0.18
-p_prior[1]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[2]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[3]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[4]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[5]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[6]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[7]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[8]                        0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[9]                        0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[10]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[11]                       0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[12]                       0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[13]                       0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[14]                       0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[15]                       0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[16]                       0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[17]                       0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[18]                       0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[19]                       0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[20]                       0.50    0.00  0.43    0.00    0.02    0.48
-p_prior[21]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[22]                       0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[23]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[24]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[25]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[26]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[27]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[28]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[29]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[30]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[31]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[32]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[33]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[34]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[35]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[36]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[37]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[38]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[39]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[40]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[41]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[42]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[43]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[44]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[45]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[46]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[47]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[48]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[49]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[50]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[51]                       0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[52]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[53]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[54]                       0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[55]                       0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[56]                       0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[57]                       0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[58]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[59]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[60]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[61]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[62]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[63]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[64]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[65]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[66]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[67]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[68]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[69]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[70]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[71]                       0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[72]                       0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[73]                       0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[74]                       0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[75]                       0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[76]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[77]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[78]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[79]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[80]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[81]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[82]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[83]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[84]                       0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[85]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[86]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[87]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[88]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[89]                       0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[90]                       0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[91]                       0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[92]                       0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[93]                       0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[94]                       0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[95]                       0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[96]                       0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[97]                       0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[98]                       0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[99]                       0.50    0.00  0.43    0.00    0.01    0.51
-p_prior[100]                      0.50    0.00  0.43    0.00    0.01    0.51
-p_prior[101]                      0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[102]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[103]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[104]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[105]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[106]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[107]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[108]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[109]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[110]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[111]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[112]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[113]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[114]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[115]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[116]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[117]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[118]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[119]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[120]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[121]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[122]                      0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[123]                      0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[124]                      0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[125]                      0.50    0.00  0.43    0.00    0.01    0.48
-p_prior[126]                      0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[127]                      0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[128]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[129]                      0.50    0.00  0.45    0.00    0.01    0.48
-p_prior[130]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[131]                      0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[132]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[133]                      0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[134]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[135]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[136]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[137]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[138]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[139]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[140]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[141]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[142]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[143]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[144]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[145]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[146]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[147]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[148]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[149]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[150]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[151]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[152]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[153]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[154]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[155]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[156]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[157]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[158]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[159]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[160]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[161]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[162]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[163]                      0.50    0.00  0.17    0.17    0.38    0.50
-p_prior[164]                      0.50    0.00  0.17    0.17    0.38    0.50
-p_prior[165]                      0.50    0.00  0.17    0.17    0.38    0.50
-p_prior[166]                      0.50    0.00  0.17    0.17    0.38    0.50
-p_prior[167]                      0.50    0.00  0.18    0.16    0.38    0.50
-p_prior[168]                      0.50    0.00  0.18    0.16    0.38    0.50
-p_prior[169]                      0.50    0.00  0.18    0.16    0.37    0.50
-p_prior[170]                      0.50    0.00  0.18    0.16    0.37    0.50
-p_prior[171]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[172]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[173]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[174]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[175]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[176]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[177]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[178]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[179]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[180]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[181]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[182]                      0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[183]                      0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[184]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[185]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[186]                      0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[187]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[188]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[189]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[190]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[191]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[192]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[193]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[194]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[195]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[196]                      0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[197]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[198]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[199]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[200]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[201]                      0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[202]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[203]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[204]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[205]                      0.50    0.00  0.45    0.00    0.01    0.47
-p_prior[206]                      0.50    0.00  0.45    0.00    0.01    0.47
-p_prior[207]                      0.50    0.00  0.45    0.00    0.01    0.47
-p_prior[208]                      0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[209]                      0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[210]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[211]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[212]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[213]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[214]                      0.50    0.00  0.44    0.00    0.01    0.50
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-p_prior[950]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[951]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[952]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[953]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[954]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[955]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[956]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[957]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[958]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[959]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[960]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[961]                      0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[962]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[963]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[964]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[965]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[966]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[967]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[968]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[969]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[970]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[971]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[972]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[973]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[974]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[975]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[976]                      0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[977]                      0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[978]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[979]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[980]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[981]                      0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[982]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[983]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[984]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[985]                      0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[986]                      0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[987]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[988]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[989]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[990]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[991]                      0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[992]                      0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[993]                      0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[994]                      0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[995]                      0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[996]                      0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[997]                      0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[998]                      0.50    0.00  0.45    0.00    0.01    0.51
-p_prior[999]                      0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[1000]                     0.49    0.00  0.44    0.00    0.01    0.47
-p_prior[1001]                     0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[1002]                     0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[1003]                     0.49    0.00  0.44    0.00    0.01    0.48
-p_prior[1004]                     0.50    0.00  0.45    0.00    0.01    0.48
-p_prior[1005]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1006]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1007]                     0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1008]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1009]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1010]                     0.50    0.00  0.44    0.00    0.01    0.47
-p_prior[1011]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1012]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1013]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1014]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1015]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1016]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1017]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1018]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1019]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1020]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1021]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1022]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1023]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1024]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1025]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1026]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1027]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1028]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1029]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1030]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1031]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1032]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1033]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1034]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1035]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1036]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1037]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1038]                     0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1039]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1040]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1041]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1042]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1043]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1044]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1045]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1046]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1047]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1048]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1049]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1050]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1051]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1052]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1053]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1054]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1055]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1056]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1057]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1058]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1059]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1060]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1061]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1062]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1063]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1064]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1065]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1066]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1067]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1068]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1069]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1070]                     0.50    0.00  0.43    0.00    0.01    0.50
-p_prior[1071]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1072]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1073]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1074]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1075]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1076]                     0.50    0.00  0.42    0.00    0.03    0.49
-p_prior[1077]                     0.50    0.00  0.42    0.00    0.03    0.49
-p_prior[1078]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1079]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1080]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1081]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1082]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1083]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1084]                     0.50    0.00  0.14    0.22    0.41    0.50
-p_prior[1085]                     0.50    0.00  0.14    0.22    0.41    0.50
-p_prior[1086]                     0.50    0.00  0.14    0.22    0.40    0.50
-p_prior[1087]                     0.50    0.00  0.15    0.22    0.40    0.50
-p_prior[1088]                     0.50    0.00  0.15    0.21    0.40    0.50
-p_prior[1089]                     0.50    0.00  0.16    0.20    0.39    0.50
-p_prior[1090]                     0.50    0.00  0.16    0.20    0.39    0.50
-p_prior[1091]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1092]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1093]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1094]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1095]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1096]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1097]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1098]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1099]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1100]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1101]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1102]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1103]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1104]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1105]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1106]                     0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1107]                     0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1108]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1109]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1110]                     0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1111]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1112]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1113]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1114]                     0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1115]                     0.50    0.00  0.43    0.00    0.02    0.51
-p_prior[1116]                     0.50    0.00  0.12    0.26    0.42    0.50
-p_prior[1117]                     0.50    0.00  0.12    0.26    0.42    0.50
-p_prior[1118]                     0.50    0.00  0.12    0.26    0.42    0.50
-p_prior[1119]                     0.50    0.00  0.13    0.26    0.42    0.50
-p_prior[1120]                     0.50    0.00  0.13    0.26    0.42    0.50
-p_prior[1121]                     0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1122]                     0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1123]                     0.50    0.00  0.13    0.25    0.42    0.50
-p_prior[1124]                     0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1125]                     0.50    0.00  0.13    0.25    0.42    0.50
-p_prior[1126]                     0.50    0.00  0.13    0.25    0.41    0.50
-p_prior[1127]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1128]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1129]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1130]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1131]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1132]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1133]                     0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[1134]                     0.50    0.00  0.44    0.00    0.01    0.51
-p_prior[1135]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1136]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1137]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1138]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1139]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1140]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1141]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1142]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1143]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1144]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1145]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1146]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1147]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1148]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1149]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1150]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1151]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1152]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1153]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1154]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1155]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1156]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1157]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1158]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1159]                     0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1160]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1161]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1162]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1163]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1164]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1165]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1166]                     0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1167]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1168]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1169]                     0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1170]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1171]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1172]                     0.50    0.00  0.45    0.00    0.00    0.47
-p_prior[1173]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1174]                     0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1175]                     0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1176]                     0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1177]                     0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1178]                     0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1179]                     0.50    0.00  0.19    0.15    0.37    0.50
-p_prior[1180]                     0.50    0.00  0.19    0.14    0.37    0.50
-p_prior[1181]                     0.50    0.00  0.19    0.14    0.36    0.50
-p_prior[1182]                     0.50    0.00  0.19    0.13    0.36    0.50
-p_prior[1183]                     0.50    0.00  0.19    0.13    0.36    0.50
-p_prior[1184]                     0.50    0.00  0.20    0.12    0.35    0.50
-p_prior[1185]                     0.50    0.00  0.21    0.12    0.35    0.50
-p_prior[1186]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1187]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1188]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1189]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1190]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1191]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1192]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1193]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1194]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1195]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1196]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1197]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1198]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1199]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1200]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1201]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1202]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1203]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1204]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1205]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1206]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1207]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1208]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1209]                     0.50    0.00  0.45    0.00    0.00    0.51
-p_prior[1210]                     0.50    0.00  0.45    0.00    0.00    0.51
-p_prior[1211]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1212]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1213]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1214]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1215]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1216]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1217]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1218]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1219]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1220]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1221]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1222]                     0.50    0.00  0.45    0.00    0.00    0.51
-p_prior[1223]                     0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1224]                     0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1225]                     0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1226]                     0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1227]                     0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1228]                     0.50    0.00  0.45    0.00    0.01    0.50
-p_prior[1229]                     0.50    0.00  0.45    0.00    0.01    0.49
-p_prior[1230]                     0.50    0.00  0.45    0.00    0.00    0.50
-p_prior[1231]                     0.50    0.00  0.45    0.00    0.01    0.48
-p_prior[1232]                     0.50    0.00  0.45    0.00    0.01    0.48
-p_prior[1233]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1234]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1235]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1236]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1237]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1238]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1239]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1240]                     0.50    0.00  0.09    0.32    0.44    0.50
-p_prior[1241]                     0.50    0.00  0.10    0.31    0.44    0.50
-p_prior[1242]                     0.50    0.00  0.10    0.31    0.44    0.50
-p_prior[1243]                     0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1244]                     0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1245]                     0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1246]                     0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1247]                     0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1248]                     0.50    0.00  0.10    0.30    0.43    0.50
-p_prior[1249]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1250]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1251]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1252]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1253]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1254]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1255]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1256]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1257]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1258]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1259]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1260]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1261]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1262]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1263]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1264]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1265]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1266]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1267]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1268]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1269]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1270]                     0.50    0.00  0.44    0.00    0.01    0.50
-p_prior[1271]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1272]                     0.50    0.00  0.44    0.00    0.01    0.48
-p_prior[1273]                     0.50    0.00  0.44    0.00    0.01    0.49
-p_prior[1274]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1275]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1276]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1277]                     0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1278]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1279]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1280]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1281]                     0.50    0.00  0.45    0.00    0.00    0.49
-p_prior[1282]                     0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1283]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1284]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1285]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1286]                     0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1287]                     0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1288]                     0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1289]                     0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1290]                     0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1291]                     0.50    0.00  0.43    0.00    0.01    0.48
-p_prior[1292]                     0.50    0.00  0.43    0.00    0.01    0.49
-p_prior[1293]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1294]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1295]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1296]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1297]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1298]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1299]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1300]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1301]                     0.50    0.00  0.45    0.00    0.00    0.48
-p_prior[1302]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1303]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1304]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1305]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1306]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1307]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1308]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1309]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1310]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1311]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1312]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1313]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1314]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1315]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1316]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1317]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1318]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1319]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1320]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1321]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1322]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1323]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1324]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1325]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1326]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1327]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1328]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1329]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1330]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1331]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1332]                     0.50    0.00  0.42    0.00    0.03    0.50
-p_prior[1333]                     0.50    0.00  0.42    0.00    0.02    0.50
-p_prior[1334]                     0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1335]                     0.50    0.00  0.43    0.00    0.02    0.49
-p_prior[1336]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1337]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1338]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1339]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1340]                     0.50    0.00  0.43    0.00    0.02    0.50
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-p_prior[1342]                     0.50    0.00  0.43    0.00    0.02    0.50
-p_prior[1343]                     0.50    0.00  0.43    0.00    0.02    0.50
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-p_predicted[2]                    0.22    0.00  0.07    0.10    0.17    0.21
-p_predicted[3]                    0.22    0.00  0.07    0.11    0.17    0.21
-p_predicted[4]                    0.19    0.00  0.06    0.09    0.15    0.19
-p_predicted[5]                    0.19    0.00  0.06    0.09    0.15    0.19
-p_predicted[6]                    0.19    0.00  0.06    0.09    0.15    0.18
-p_predicted[7]                    0.18    0.00  0.07    0.07    0.13    0.17
-p_predicted[8]                    0.18    0.00  0.07    0.07    0.13    0.17
-p_predicted[9]                    0.38    0.00  0.08    0.23    0.32    0.37
-p_predicted[10]                   0.39    0.00  0.08    0.25    0.34    0.39
-p_predicted[11]                   0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[12]                   0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[13]                   0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[14]                   0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[15]                   0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[16]                   0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[17]                   0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[18]                   0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[19]                   0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[20]                   0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted[21]                   0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[22]                   0.09    0.00  0.06    0.01    0.04    0.07
-p_predicted[23]                   0.38    0.00  0.04    0.30    0.35    0.38
-p_predicted[24]                   0.36    0.00  0.04    0.28    0.33    0.36
-p_predicted[25]                   0.33    0.00  0.05    0.24    0.29    0.33
-p_predicted[26]                   0.27    0.00  0.04    0.20    0.25    0.27
-p_predicted[27]                   0.26    0.00  0.04    0.19    0.23    0.26
-p_predicted[28]                   0.24    0.00  0.04    0.16    0.21    0.24
-p_predicted[29]                   0.23    0.00  0.04    0.15    0.20    0.23
-p_predicted[30]                   0.33    0.00  0.04    0.25    0.30    0.33
-p_predicted[31]                   0.33    0.00  0.04    0.25    0.30    0.33
-p_predicted[32]                   0.31    0.00  0.04    0.24    0.28    0.31
-p_predicted[33]                   0.31    0.00  0.04    0.24    0.28    0.31
-p_predicted[34]                   0.29    0.00  0.04    0.22    0.26    0.29
-p_predicted[35]                   0.29    0.00  0.04    0.22    0.26    0.29
-p_predicted[36]                   0.28    0.00  0.04    0.21    0.25    0.28
-p_predicted[37]                   0.28    0.00  0.04    0.21    0.25    0.28
-p_predicted[38]                   0.21    0.00  0.03    0.15    0.18    0.20
-p_predicted[39]                   0.21    0.00  0.03    0.15    0.18    0.20
-p_predicted[40]                   0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[41]                   0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[42]                   0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[43]                   0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[44]                   0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[45]                   0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted[46]                   0.14    0.00  0.02    0.10    0.12    0.14
-p_predicted[47]                   0.14    0.00  0.02    0.10    0.12    0.14
-p_predicted[48]                   0.14    0.00  0.02    0.09    0.12    0.14
-p_predicted[49]                   0.14    0.00  0.02    0.09    0.12    0.14
-p_predicted[50]                   0.11    0.00  0.06    0.04    0.07    0.10
-p_predicted[51]                   0.08    0.00  0.04    0.03    0.06    0.08
-p_predicted[52]                   0.08    0.00  0.04    0.03    0.06    0.08
-p_predicted[53]                   0.08    0.00  0.03    0.03    0.06    0.08
-p_predicted[54]                   0.06    0.00  0.03    0.02    0.04    0.06
-p_predicted[55]                   0.06    0.00  0.03    0.02    0.04    0.06
-p_predicted[56]                   0.06    0.00  0.03    0.02    0.04    0.05
-p_predicted[57]                   0.06    0.00  0.03    0.01    0.03    0.05
-p_predicted[58]                   0.07    0.00  0.04    0.02    0.04    0.06
-p_predicted[59]                   0.07    0.00  0.04    0.02    0.04    0.06
-p_predicted[60]                   0.07    0.00  0.04    0.02    0.04    0.06
-p_predicted[61]                   0.05    0.00  0.02    0.02    0.03    0.04
-p_predicted[62]                   0.05    0.00  0.02    0.02    0.03    0.04
-p_predicted[63]                   0.04    0.00  0.02    0.01    0.03    0.03
-p_predicted[64]                   0.04    0.00  0.02    0.01    0.02    0.03
-p_predicted[65]                   0.04    0.00  0.02    0.01    0.03    0.04
-p_predicted[66]                   0.04    0.00  0.02    0.01    0.02    0.03
-p_predicted[67]                   0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted[68]                   0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted[69]                   0.03    0.00  0.03    0.00    0.01    0.02
-p_predicted[70]                   0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[71]                   0.20    0.00  0.05    0.11    0.16    0.20
-p_predicted[72]                   0.19    0.00  0.05    0.11    0.16    0.19
-p_predicted[73]                   0.18    0.00  0.05    0.10    0.15    0.18
-p_predicted[74]                   0.18    0.00  0.05    0.10    0.14    0.17
-p_predicted[75]                   0.13    0.00  0.05    0.05    0.09    0.12
-p_predicted[76]                   0.64    0.00  0.11    0.42    0.57    0.65
-p_predicted[77]                   0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted[78]                   0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted[79]                   0.64    0.00  0.11    0.42    0.57    0.65
-p_predicted[80]                   0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted[81]                   0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted[82]                   0.64    0.00  0.11    0.42    0.57    0.65
-p_predicted[83]                   0.63    0.00  0.11    0.42    0.56    0.64
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-p_predicted[1033]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1034]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1035]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1036]                 0.04    0.00  0.03    0.00    0.02    0.03
-p_predicted[1037]                 0.04    0.00  0.03    0.00    0.02    0.03
-p_predicted[1038]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1039]                 0.04    0.00  0.03    0.01    0.02    0.04
-p_predicted[1040]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1041]                 0.04    0.00  0.03    0.01    0.02    0.04
-p_predicted[1042]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1043]                 0.04    0.00  0.03    0.01    0.02    0.04
-p_predicted[1044]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1045]                 0.04    0.00  0.03    0.01    0.02    0.04
-p_predicted[1046]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1047]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1048]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1049]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1050]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1051]                 0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted[1052]                 0.10    0.00  0.06    0.02    0.05    0.09
-p_predicted[1053]                 0.10    0.00  0.06    0.02    0.05    0.09
-p_predicted[1054]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1055]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1056]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1057]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1058]                 0.09    0.00  0.05    0.02    0.05    0.08
-p_predicted[1059]                 0.09    0.00  0.05    0.02    0.05    0.08
-p_predicted[1060]                 0.08    0.00  0.06    0.01    0.04    0.07
-p_predicted[1061]                 0.08    0.00  0.06    0.01    0.04    0.07
-p_predicted[1062]                 0.10    0.00  0.06    0.02    0.05    0.09
-p_predicted[1063]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1064]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1065]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1066]                 0.09    0.00  0.05    0.02    0.05    0.08
-p_predicted[1067]                 0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted[1068]                 0.09    0.00  0.05    0.02    0.05    0.08
-p_predicted[1069]                 0.09    0.00  0.06    0.01    0.04    0.07
-p_predicted[1070]                 0.08    0.00  0.06    0.01    0.04    0.07
-p_predicted[1071]                 0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted[1072]                 0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted[1073]                 0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted[1074]                 0.14    0.00  0.04    0.06    0.11    0.13
-p_predicted[1075]                 0.14    0.00  0.04    0.06    0.11    0.13
-p_predicted[1076]                 0.17    0.00  0.05    0.08    0.13    0.16
-p_predicted[1077]                 0.17    0.00  0.05    0.08    0.13    0.16
-p_predicted[1078]                 0.13    0.00  0.04    0.06    0.10    0.13
-p_predicted[1079]                 0.13    0.00  0.04    0.06    0.10    0.13
-p_predicted[1080]                 0.13    0.00  0.04    0.06    0.10    0.13
-p_predicted[1081]                 0.13    0.00  0.04    0.06    0.10    0.13
-p_predicted[1082]                 0.13    0.00  0.04    0.06    0.10    0.13
-p_predicted[1083]                 0.13    0.00  0.04    0.06    0.10    0.13
-p_predicted[1084]                 0.14    0.00  0.05    0.05    0.10    0.14
-p_predicted[1085]                 0.18    0.00  0.04    0.10    0.15    0.17
-p_predicted[1086]                 0.13    0.00  0.03    0.07    0.10    0.12
-p_predicted[1087]                 0.12    0.00  0.03    0.07    0.10    0.12
-p_predicted[1088]                 0.11    0.00  0.03    0.07    0.09    0.11
-p_predicted[1089]                 0.10    0.00  0.03    0.05    0.08    0.09
-p_predicted[1090]                 0.09    0.00  0.03    0.05    0.08    0.09
-p_predicted[1091]                 0.18    0.00  0.06    0.08    0.14    0.18
-p_predicted[1092]                 0.18    0.00  0.06    0.08    0.14    0.18
-p_predicted[1093]                 0.18    0.00  0.06    0.08    0.14    0.18
-p_predicted[1094]                 0.18    0.00  0.06    0.08    0.14    0.18
-p_predicted[1095]                 0.18    0.00  0.06    0.08    0.13    0.17
-p_predicted[1096]                 0.18    0.00  0.06    0.08    0.14    0.17
-p_predicted[1097]                 0.18    0.00  0.06    0.08    0.13    0.17
-p_predicted[1098]                 0.18    0.00  0.06    0.08    0.14    0.17
-p_predicted[1099]                 0.17    0.00  0.06    0.08    0.13    0.17
-p_predicted[1100]                 0.17    0.00  0.06    0.08    0.13    0.17
-p_predicted[1101]                 0.17    0.00  0.06    0.08    0.13    0.17
-p_predicted[1102]                 0.16    0.00  0.06    0.07    0.12    0.15
-p_predicted[1103]                 0.13    0.00  0.04    0.06    0.10    0.12
-p_predicted[1104]                 0.12    0.00  0.04    0.05    0.09    0.12
-p_predicted[1105]                 0.12    0.00  0.04    0.05    0.09    0.11
-p_predicted[1106]                 0.43    0.00  0.07    0.30    0.38    0.43
-p_predicted[1107]                 0.43    0.00  0.07    0.30    0.38    0.43
-p_predicted[1108]                 0.42    0.00  0.07    0.29    0.37    0.42
-p_predicted[1109]                 0.36    0.00  0.08    0.23    0.31    0.36
-p_predicted[1110]                 0.22    0.00  0.06    0.12    0.18    0.21
-p_predicted[1111]                 0.25    0.00  0.06    0.16    0.21    0.24
-p_predicted[1112]                 0.24    0.00  0.05    0.15    0.20    0.23
-p_predicted[1113]                 0.23    0.00  0.05    0.15    0.20    0.23
-p_predicted[1114]                 0.18    0.00  0.04    0.11    0.15    0.18
-p_predicted[1115]                 0.20    0.00  0.05    0.12    0.17    0.20
-p_predicted[1116]                 0.22    0.00  0.04    0.15    0.19    0.22
-p_predicted[1117]                 0.22    0.00  0.04    0.14    0.19    0.21
-p_predicted[1118]                 0.20    0.00  0.04    0.13    0.17    0.20
-p_predicted[1119]                 0.15    0.00  0.03    0.10    0.13    0.15
-p_predicted[1120]                 0.15    0.00  0.03    0.10    0.13    0.15
-p_predicted[1121]                 0.14    0.00  0.03    0.09    0.12    0.14
-p_predicted[1122]                 0.14    0.00  0.03    0.09    0.12    0.14
-p_predicted[1123]                 0.15    0.00  0.03    0.09    0.12    0.14
-p_predicted[1124]                 0.14    0.00  0.03    0.09    0.12    0.14
-p_predicted[1125]                 0.15    0.00  0.03    0.10    0.13    0.15
-p_predicted[1126]                 0.14    0.00  0.03    0.09    0.11    0.13
-p_predicted[1127]                 0.29    0.00  0.10    0.13    0.22    0.28
-p_predicted[1128]                 0.23    0.00  0.07    0.12    0.18    0.23
-p_predicted[1129]                 0.23    0.00  0.07    0.12    0.18    0.22
-p_predicted[1130]                 0.18    0.00  0.05    0.09    0.14    0.18
-p_predicted[1131]                 0.17    0.00  0.05    0.09    0.14    0.17
-p_predicted[1132]                 0.07    0.00  0.07    0.00    0.02    0.04
-p_predicted[1133]                 0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted[1134]                 0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted[1135]                 0.02    0.00  0.02    0.00    0.00    0.01
-p_predicted[1136]                 0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[1137]                 0.02    0.00  0.02    0.00    0.00    0.01
-p_predicted[1138]                 0.01    0.00  0.02    0.00    0.00    0.01
-p_predicted[1139]                 0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[1140]                 0.01    0.00  0.02    0.00    0.00    0.01
-p_predicted[1141]                 0.02    0.00  0.02    0.00    0.00    0.01
-p_predicted[1142]                 0.01    0.00  0.01    0.00    0.00    0.01
-p_predicted[1143]                 0.02    0.00  0.02    0.00    0.00    0.01
-p_predicted[1144]                 0.01    0.00  0.02    0.00    0.00    0.01
-p_predicted[1145]                 0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[1146]                 0.01    0.00  0.02    0.00    0.00    0.01
-p_predicted[1147]                 0.07    0.00  0.03    0.03    0.05    0.07
-p_predicted[1148]                 0.05    0.00  0.02    0.01    0.03    0.05
-p_predicted[1149]                 0.07    0.00  0.03    0.03    0.05    0.07
-p_predicted[1150]                 0.06    0.00  0.03    0.02    0.04    0.05
-p_predicted[1151]                 0.04    0.00  0.02    0.01    0.02    0.04
-p_predicted[1152]                 0.06    0.00  0.03    0.02    0.04    0.05
-p_predicted[1153]                 0.06    0.00  0.02    0.02    0.04    0.05
-p_predicted[1154]                 0.04    0.00  0.02    0.01    0.02    0.04
-p_predicted[1155]                 0.06    0.00  0.02    0.02    0.04    0.05
-p_predicted[1156]                 0.10    0.00  0.04    0.04    0.07    0.09
-p_predicted[1157]                 0.07    0.00  0.03    0.02    0.05    0.06
-p_predicted[1158]                 0.10    0.00  0.04    0.04    0.07    0.09
-p_predicted[1159]                 0.03    0.00  0.03    0.00    0.01    0.02
-p_predicted[1160]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1161]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1162]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1163]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1164]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1165]                 0.06    0.00  0.03    0.01    0.03    0.05
-p_predicted[1166]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1167]                 0.06    0.00  0.03    0.01    0.03    0.05
-p_predicted[1168]                 0.04    0.00  0.02    0.01    0.03    0.04
-p_predicted[1169]                 0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted[1170]                 0.04    0.00  0.02    0.01    0.03    0.04
-p_predicted[1171]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1172]                 0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted[1173]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1174]                 0.79    0.00  0.14    0.45    0.71    0.82
-p_predicted[1175]                 0.78    0.00  0.14    0.45    0.70    0.81
-p_predicted[1176]                 0.77    0.00  0.14    0.43    0.69    0.80
-p_predicted[1177]                 0.76    0.00  0.15    0.41    0.67    0.79
-p_predicted[1178]                 0.76    0.00  0.16    0.39    0.67    0.79
-p_predicted[1179]                 0.08    0.00  0.04    0.03    0.05    0.08
-p_predicted[1180]                 0.11    0.00  0.04    0.05    0.08    0.10
-p_predicted[1181]                 0.06    0.00  0.02    0.02    0.04    0.05
-p_predicted[1182]                 0.07    0.00  0.03    0.03    0.05    0.06
-p_predicted[1183]                 0.05    0.00  0.02    0.02    0.04    0.05
-p_predicted[1184]                 0.04    0.00  0.02    0.02    0.03    0.04
-p_predicted[1185]                 0.04    0.00  0.02    0.01    0.02    0.03
-p_predicted[1186]                 0.52    0.00  0.19    0.17    0.38    0.52
-p_predicted[1187]                 0.49    0.00  0.20    0.14    0.34    0.48
-p_predicted[1188]                 0.49    0.00  0.20    0.14    0.34    0.49
-p_predicted[1189]                 0.07    0.00  0.04    0.01    0.04    0.06
-p_predicted[1190]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1191]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1192]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1193]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1194]                 0.07    0.00  0.04    0.01    0.04    0.06
-p_predicted[1195]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1196]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1197]                 0.05    0.00  0.03    0.01    0.03    0.04
-p_predicted[1198]                 0.04    0.00  0.03    0.01    0.02    0.03
-p_predicted[1199]                 0.58    0.00  0.12    0.33    0.49    0.58
-p_predicted[1200]                 0.58    0.00  0.12    0.33    0.49    0.58
-p_predicted[1201]                 0.64    0.00  0.14    0.35    0.54    0.65
-p_predicted[1202]                 0.55    0.00  0.12    0.32    0.47    0.56
-p_predicted[1203]                 0.55    0.00  0.12    0.31    0.47    0.55
-p_predicted[1204]                 0.55    0.00  0.12    0.31    0.47    0.55
-p_predicted[1205]                 0.55    0.00  0.12    0.31    0.47    0.55
-p_predicted[1206]                 0.55    0.00  0.12    0.31    0.47    0.56
-p_predicted[1207]                 0.50    0.00  0.12    0.25    0.41    0.50
-p_predicted[1208]                 0.48    0.00  0.12    0.25    0.40    0.48
-p_predicted[1209]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1210]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1211]                 0.06    0.00  0.05    0.01    0.03    0.05
-p_predicted[1212]                 0.06    0.00  0.05    0.01    0.03    0.05
-p_predicted[1213]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1214]                 0.05    0.00  0.03    0.01    0.02    0.04
-p_predicted[1215]                 0.04    0.00  0.03    0.01    0.02    0.04
-p_predicted[1216]                 0.04    0.00  0.03    0.00    0.02    0.03
-p_predicted[1217]                 0.27    0.00  0.08    0.13    0.21    0.26
-p_predicted[1218]                 0.26    0.00  0.08    0.13    0.21    0.26
-p_predicted[1219]                 0.22    0.00  0.06    0.11    0.17    0.21
-p_predicted[1220]                 0.21    0.00  0.06    0.11    0.17    0.21
-p_predicted[1221]                 0.21    0.00  0.06    0.11    0.17    0.21
-p_predicted[1222]                 0.08    0.00  0.06    0.01    0.04    0.07
-p_predicted[1223]                 0.08    0.00  0.06    0.01    0.03    0.06
-p_predicted[1224]                 0.08    0.00  0.06    0.01    0.03    0.06
-p_predicted[1225]                 0.05    0.00  0.04    0.01    0.02    0.04
-p_predicted[1226]                 0.05    0.00  0.04    0.01    0.02    0.04
-p_predicted[1227]                 0.05    0.00  0.04    0.01    0.02    0.04
-p_predicted[1228]                 0.05    0.00  0.04    0.01    0.02    0.04
-p_predicted[1229]                 0.63    0.00  0.14    0.34    0.54    0.64
-p_predicted[1230]                 0.64    0.00  0.12    0.38    0.56    0.65
-p_predicted[1231]                 0.57    0.00  0.14    0.29    0.48    0.58
-p_predicted[1232]                 0.57    0.00  0.14    0.28    0.47    0.57
-p_predicted[1233]                 0.31    0.00  0.04    0.22    0.28    0.31
-p_predicted[1234]                 0.31    0.00  0.04    0.22    0.28    0.31
-p_predicted[1235]                 0.29    0.00  0.04    0.21    0.26    0.29
-p_predicted[1236]                 0.29    0.00  0.04    0.21    0.26    0.29
-p_predicted[1237]                 0.27    0.00  0.04    0.19    0.25    0.27
-p_predicted[1238]                 0.27    0.00  0.04    0.19    0.25    0.27
-p_predicted[1239]                 0.23    0.00  0.04    0.16    0.20    0.23
-p_predicted[1240]                 0.23    0.00  0.04    0.16    0.20    0.23
-p_predicted[1241]                 0.21    0.00  0.03    0.14    0.18    0.21
-p_predicted[1242]                 0.21    0.00  0.03    0.14    0.18    0.21
-p_predicted[1243]                 0.19    0.00  0.04    0.13    0.17    0.19
-p_predicted[1244]                 0.19    0.00  0.04    0.13    0.17    0.19
-p_predicted[1245]                 0.20    0.00  0.04    0.13    0.17    0.20
-p_predicted[1246]                 0.20    0.00  0.04    0.13    0.17    0.20
-p_predicted[1247]                 0.19    0.00  0.04    0.13    0.17    0.19
-p_predicted[1248]                 0.19    0.00  0.04    0.13    0.17    0.19
-p_predicted[1249]                 0.14    0.00  0.08    0.03    0.08    0.12
-p_predicted[1250]                 0.10    0.00  0.05    0.02    0.06    0.09
-p_predicted[1251]                 0.14    0.00  0.08    0.03    0.08    0.12
-p_predicted[1252]                 0.16    0.00  0.07    0.04    0.10    0.15
-p_predicted[1253]                 0.11    0.00  0.05    0.03    0.07    0.10
-p_predicted[1254]                 0.16    0.00  0.07    0.04    0.10    0.15
-p_predicted[1255]                 0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted[1256]                 0.11    0.00  0.05    0.04    0.07    0.10
-p_predicted[1257]                 0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted[1258]                 0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted[1259]                 0.11    0.00  0.05    0.04    0.07    0.10
-p_predicted[1260]                 0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted[1261]                 0.18    0.00  0.09    0.05    0.12    0.17
-p_predicted[1262]                 0.13    0.00  0.06    0.04    0.09    0.12
-p_predicted[1263]                 0.18    0.00  0.09    0.05    0.12    0.17
-p_predicted[1264]                 0.15    0.00  0.08    0.03    0.09    0.13
-p_predicted[1265]                 0.10    0.00  0.05    0.03    0.06    0.09
-p_predicted[1266]                 0.15    0.00  0.08    0.03    0.09    0.13
-p_predicted[1267]                 0.04    0.00  0.03    0.00    0.01    0.03
-p_predicted[1268]                 0.04    0.00  0.03    0.00    0.01    0.03
-p_predicted[1269]                 0.03    0.00  0.03    0.00    0.01    0.03
-p_predicted[1270]                 0.03    0.00  0.03    0.00    0.01    0.03
-p_predicted[1271]                 0.03    0.00  0.03    0.00    0.01    0.02
-p_predicted[1272]                 0.07    0.00  0.05    0.01    0.03    0.06
-p_predicted[1273]                 0.06    0.00  0.05    0.01    0.02    0.04
-p_predicted[1274]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1275]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1276]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1277]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1278]                 0.07    0.00  0.03    0.03    0.05    0.07
-p_predicted[1279]                 0.07    0.00  0.03    0.03    0.05    0.07
-p_predicted[1280]                 0.05    0.00  0.02    0.02    0.04    0.05
-p_predicted[1281]                 0.05    0.00  0.02    0.02    0.04    0.05
-p_predicted[1282]                 0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[1283]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1284]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1285]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1286]                 0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[1287]                 0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[1288]                 0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted[1289]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1290]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1291]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1292]                 0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted[1293]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1294]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1295]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1296]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1297]                 0.11    0.00  0.05    0.03    0.07    0.11
-p_predicted[1298]                 0.13    0.00  0.05    0.05    0.09    0.12
-p_predicted[1299]                 0.20    0.00  0.08    0.08    0.14    0.19
-p_predicted[1300]                 0.20    0.00  0.08    0.08    0.14    0.19
-p_predicted[1301]                 0.16    0.00  0.07    0.05    0.11    0.15
-p_predicted[1302]                 0.36    0.00  0.11    0.18    0.28    0.35
-p_predicted[1303]                 0.36    0.00  0.11    0.18    0.28    0.35
-p_predicted[1304]                 0.28    0.00  0.08    0.14    0.22    0.27
-p_predicted[1305]                 0.28    0.00  0.08    0.14    0.22    0.27
-p_predicted[1306]                 0.29    0.00  0.08    0.16    0.24    0.28
-p_predicted[1307]                 0.29    0.00  0.08    0.16    0.24    0.28
-p_predicted[1308]                 0.23    0.00  0.06    0.12    0.19    0.23
-p_predicted[1309]                 0.23    0.00  0.06    0.12    0.19    0.23
-p_predicted[1310]                 0.23    0.00  0.06    0.12    0.18    0.22
-p_predicted[1311]                 0.23    0.00  0.06    0.12    0.18    0.22
-p_predicted[1312]                 0.21    0.00  0.06    0.11    0.17    0.20
-p_predicted[1313]                 0.21    0.00  0.06    0.11    0.17    0.20
-p_predicted[1314]                 0.21    0.00  0.06    0.11    0.17    0.20
-p_predicted[1315]                 0.21    0.00  0.06    0.11    0.17    0.20
-p_predicted[1316]                 0.08    0.00  0.05    0.01    0.04    0.06
-p_predicted[1317]                 0.07    0.00  0.04    0.01    0.03    0.06
-p_predicted[1318]                 0.07    0.00  0.04    0.01    0.03    0.06
-p_predicted[1319]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1320]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1321]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1322]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1323]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1324]                 0.08    0.00  0.05    0.01    0.04    0.06
-p_predicted[1325]                 0.07    0.00  0.04    0.01    0.03    0.06
-p_predicted[1326]                 0.07    0.00  0.04    0.01    0.03    0.06
-p_predicted[1327]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1328]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1329]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1330]                 0.06    0.00  0.04    0.01    0.03    0.05
-p_predicted[1331]                 0.27    0.00  0.07    0.15    0.22    0.27
-p_predicted[1332]                 0.22    0.00  0.06    0.12    0.18    0.22
-p_predicted[1333]                 0.21    0.00  0.06    0.10    0.16    0.20
-p_predicted[1334]                 0.03    0.00  0.03    0.00    0.01    0.02
-p_predicted[1335]                 0.03    0.00  0.03    0.00    0.01    0.02
-p_predicted[1336]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1337]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1338]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1339]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1340]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1341]                 0.03    0.00  0.02    0.00    0.01    0.02
-p_predicted[1342]                 0.02    0.00  0.02    0.00    0.01    0.02
-p_predicted[1343]                 0.02    0.00  0.02    0.00    0.01    0.02
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-p_predicted_default[2]            0.22    0.00  0.07    0.10    0.17    0.21
-p_predicted_default[3]            0.22    0.00  0.07    0.11    0.17    0.21
-p_predicted_default[4]            0.19    0.00  0.06    0.09    0.15    0.19
-p_predicted_default[5]            0.19    0.00  0.06    0.09    0.15    0.19
-p_predicted_default[6]            0.19    0.00  0.06    0.09    0.15    0.18
-p_predicted_default[7]            0.18    0.00  0.07    0.07    0.13    0.17
-p_predicted_default[8]            0.18    0.00  0.07    0.07    0.13    0.17
-p_predicted_default[9]            0.38    0.00  0.08    0.23    0.32    0.37
-p_predicted_default[10]           0.39    0.00  0.08    0.25    0.34    0.39
-p_predicted_default[11]           0.00    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[12]           0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[13]           0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[14]           0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[15]           0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[16]           0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted_default[17]           0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted_default[18]           0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted_default[19]           0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted_default[20]           0.02    0.00  0.02    0.00    0.01    0.01
-p_predicted_default[21]           0.09    0.00  0.06    0.02    0.05    0.08
-p_predicted_default[22]           0.09    0.00  0.06    0.01    0.04    0.07
-p_predicted_default[23]           0.38    0.00  0.04    0.30    0.35    0.38
-p_predicted_default[24]           0.36    0.00  0.04    0.28    0.33    0.36
-p_predicted_default[25]           0.33    0.00  0.05    0.24    0.29    0.33
-p_predicted_default[26]           0.27    0.00  0.04    0.20    0.25    0.27
-p_predicted_default[27]           0.26    0.00  0.04    0.19    0.23    0.26
-p_predicted_default[28]           0.24    0.00  0.04    0.16    0.21    0.24
-p_predicted_default[29]           0.23    0.00  0.04    0.15    0.20    0.23
-p_predicted_default[30]           0.33    0.00  0.04    0.25    0.30    0.33
-p_predicted_default[31]           0.33    0.00  0.04    0.25    0.30    0.33
-p_predicted_default[32]           0.31    0.00  0.04    0.24    0.28    0.31
-p_predicted_default[33]           0.31    0.00  0.04    0.24    0.28    0.31
-p_predicted_default[34]           0.29    0.00  0.04    0.22    0.26    0.29
-p_predicted_default[35]           0.29    0.00  0.04    0.22    0.26    0.29
-p_predicted_default[36]           0.28    0.00  0.04    0.21    0.25    0.28
-p_predicted_default[37]           0.28    0.00  0.04    0.21    0.25    0.28
-p_predicted_default[38]           0.21    0.00  0.03    0.15    0.18    0.20
-p_predicted_default[39]           0.21    0.00  0.03    0.15    0.18    0.20
-p_predicted_default[40]           0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted_default[41]           0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted_default[42]           0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted_default[43]           0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted_default[44]           0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted_default[45]           0.14    0.00  0.02    0.10    0.13    0.14
-p_predicted_default[46]           0.14    0.00  0.02    0.10    0.12    0.14
-p_predicted_default[47]           0.14    0.00  0.02    0.10    0.12    0.14
-p_predicted_default[48]           0.14    0.00  0.02    0.09    0.12    0.14
-p_predicted_default[49]           0.14    0.00  0.02    0.09    0.12    0.14
-p_predicted_default[50]           0.11    0.00  0.06    0.04    0.07    0.10
-p_predicted_default[51]           0.08    0.00  0.04    0.03    0.06    0.08
-p_predicted_default[52]           0.08    0.00  0.04    0.03    0.06    0.08
-p_predicted_default[53]           0.08    0.00  0.03    0.03    0.06    0.08
-p_predicted_default[54]           0.06    0.00  0.03    0.02    0.04    0.06
-p_predicted_default[55]           0.06    0.00  0.03    0.02    0.04    0.06
-p_predicted_default[56]           0.06    0.00  0.03    0.02    0.04    0.05
-p_predicted_default[57]           0.06    0.00  0.03    0.01    0.03    0.05
-p_predicted_default[58]           0.07    0.00  0.04    0.02    0.04    0.06
-p_predicted_default[59]           0.07    0.00  0.04    0.02    0.04    0.06
-p_predicted_default[60]           0.07    0.00  0.04    0.02    0.04    0.06
-p_predicted_default[61]           0.05    0.00  0.02    0.02    0.03    0.04
-p_predicted_default[62]           0.05    0.00  0.02    0.02    0.03    0.04
-p_predicted_default[63]           0.04    0.00  0.02    0.01    0.03    0.03
-p_predicted_default[64]           0.04    0.00  0.02    0.01    0.02    0.03
-p_predicted_default[65]           0.04    0.00  0.02    0.01    0.03    0.04
-p_predicted_default[66]           0.04    0.00  0.02    0.01    0.02    0.03
-p_predicted_default[67]           0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted_default[68]           0.03    0.00  0.02    0.01    0.02    0.03
-p_predicted_default[69]           0.03    0.00  0.03    0.00    0.01    0.02
-p_predicted_default[70]           0.03    0.00  0.02    0.00    0.01    0.02
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-p_predicted_default[72]           0.19    0.00  0.05    0.11    0.16    0.19
-p_predicted_default[73]           0.18    0.00  0.05    0.10    0.15    0.18
-p_predicted_default[74]           0.18    0.00  0.05    0.10    0.14    0.17
-p_predicted_default[75]           0.13    0.00  0.05    0.05    0.09    0.12
-p_predicted_default[76]           0.64    0.00  0.11    0.42    0.57    0.65
-p_predicted_default[77]           0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted_default[78]           0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted_default[79]           0.64    0.00  0.11    0.42    0.57    0.65
-p_predicted_default[80]           0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted_default[81]           0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted_default[82]           0.64    0.00  0.11    0.42    0.57    0.65
-p_predicted_default[83]           0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted_default[84]           0.63    0.00  0.11    0.42    0.56    0.64
-p_predicted_default[85]           0.56    0.00  0.08    0.39    0.51    0.57
-p_predicted_default[86]           0.38    0.00  0.06    0.26    0.34    0.38
-p_predicted_default[87]           0.32    0.00  0.06    0.20    0.27    0.31
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-p_predicted_default[89]           0.15    0.00  0.03    0.10    0.13    0.14
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-p_predicted_default[91]           0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[92]           0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[93]           0.00    0.00  0.01    0.00    0.00    0.00
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-p_predicted_default[95]           0.62    0.00  0.12    0.37    0.54    0.63
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-p_predicted_default[100]          0.20    0.00  0.07    0.09    0.16    0.20
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-p_predicted_default[102]          0.22    0.00  0.04    0.14    0.19    0.21
-p_predicted_default[103]          0.18    0.00  0.03    0.11    0.15    0.17
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-p_predicted_default[108]          0.01    0.00  0.01    0.00    0.00    0.01
-p_predicted_default[109]          0.01    0.00  0.01    0.00    0.00    0.00
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-p_predicted_default[131]          0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted_default[132]          0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted_default[133]          0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted_default[134]          0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted_default[135]          0.01    0.00  0.02    0.00    0.00    0.00
-p_predicted_default[136]          0.25    0.00  0.08    0.11    0.20    0.25
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-p_predicted_default[138]          0.31    0.00  0.06    0.21    0.27    0.31
-p_predicted_default[139]          0.31    0.00  0.06    0.21    0.27    0.31
-p_predicted_default[140]          0.27    0.00  0.05    0.18    0.24    0.27
-p_predicted_default[141]          0.25    0.00  0.05    0.16    0.21    0.25
-p_predicted_default[142]          0.21    0.00  0.04    0.13    0.18    0.21
-p_predicted_default[143]          0.20    0.00  0.04    0.13    0.17    0.20
-p_predicted_default[144]          0.19    0.00  0.04    0.12    0.17    0.19
-p_predicted_default[145]          0.20    0.00  0.04    0.13    0.17    0.19
-p_predicted_default[146]          0.19    0.00  0.04    0.12    0.16    0.19
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-p_predicted_default[1088]         0.11    0.00  0.03    0.07    0.09    0.11
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-p_predicted_default[1090]         0.09    0.00  0.03    0.05    0.08    0.09
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-p_predicted_default[1092]         0.18    0.00  0.06    0.08    0.14    0.18
-p_predicted_default[1093]         0.18    0.00  0.06    0.08    0.14    0.18
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-p_predicted_default[1096]         0.18    0.00  0.06    0.08    0.14    0.17
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-p_predicted_default[1099]         0.17    0.00  0.06    0.08    0.13    0.17
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-p_predicted_default[1101]         0.17    0.00  0.06    0.08    0.13    0.17
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-p_predicted_default[1103]         0.13    0.00  0.04    0.06    0.10    0.12
-p_predicted_default[1104]         0.12    0.00  0.04    0.05    0.09    0.12
-p_predicted_default[1105]         0.12    0.00  0.04    0.05    0.09    0.11
-p_predicted_default[1106]         0.43    0.00  0.07    0.30    0.38    0.43
-p_predicted_default[1107]         0.43    0.00  0.07    0.30    0.38    0.43
-p_predicted_default[1108]         0.42    0.00  0.07    0.29    0.37    0.42
-p_predicted_default[1109]         0.36    0.00  0.08    0.23    0.31    0.36
-p_predicted_default[1110]         0.22    0.00  0.06    0.12    0.18    0.21
-p_predicted_default[1111]         0.25    0.00  0.06    0.16    0.21    0.24
-p_predicted_default[1112]         0.24    0.00  0.05    0.15    0.20    0.23
-p_predicted_default[1113]         0.23    0.00  0.05    0.15    0.20    0.23
-p_predicted_default[1114]         0.18    0.00  0.04    0.11    0.15    0.18
-p_predicted_default[1115]         0.20    0.00  0.05    0.12    0.17    0.20
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-p_predicted_default[1117]         0.22    0.00  0.04    0.14    0.19    0.21
-p_predicted_default[1118]         0.20    0.00  0.04    0.13    0.17    0.20
-p_predicted_default[1119]         0.15    0.00  0.03    0.10    0.13    0.15
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-p_predicted_default[1122]         0.14    0.00  0.03    0.09    0.12    0.14
-p_predicted_default[1123]         0.15    0.00  0.03    0.09    0.12    0.14
-p_predicted_default[1124]         0.14    0.00  0.03    0.09    0.12    0.14
-p_predicted_default[1125]         0.15    0.00  0.03    0.10    0.13    0.15
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-p_predicted_default[1128]         0.23    0.00  0.07    0.12    0.18    0.23
-p_predicted_default[1129]         0.23    0.00  0.07    0.12    0.18    0.22
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-p_predicted_default[1132]         0.07    0.00  0.07    0.00    0.02    0.04
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-p_predicted_default[1134]         0.01    0.00  0.02    0.00    0.00    0.00
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-p_predicted_default[1136]         0.01    0.00  0.01    0.00    0.00    0.00
-p_predicted_default[1137]         0.02    0.00  0.02    0.00    0.00    0.01
-p_predicted_default[1138]         0.01    0.00  0.02    0.00    0.00    0.01
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-p_predicted_default[1173]         0.05    0.00  0.03    0.01    0.03    0.04
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-p_predicted_default[1175]         0.78    0.00  0.14    0.45    0.70    0.81
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-p_predicted_default[1198]         0.04    0.00  0.03    0.01    0.02    0.03
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-p_predicted_default[1228]         0.05    0.00  0.04    0.01    0.02    0.04
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-p_predicted_default[1234]         0.31    0.00  0.04    0.22    0.28    0.31
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-p_predicted_default[1236]         0.29    0.00  0.04    0.21    0.26    0.29
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-p_predicted_default[1238]         0.27    0.00  0.04    0.19    0.25    0.27
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-p_predicted_default[1246]         0.20    0.00  0.04    0.13    0.17    0.20
-p_predicted_default[1247]         0.19    0.00  0.04    0.13    0.17    0.19
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-p_predicted_default[1250]         0.10    0.00  0.05    0.02    0.06    0.09
-p_predicted_default[1251]         0.14    0.00  0.08    0.03    0.08    0.12
-p_predicted_default[1252]         0.16    0.00  0.07    0.04    0.10    0.15
-p_predicted_default[1253]         0.11    0.00  0.05    0.03    0.07    0.10
-p_predicted_default[1254]         0.16    0.00  0.07    0.04    0.10    0.15
-p_predicted_default[1255]         0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted_default[1256]         0.11    0.00  0.05    0.04    0.07    0.10
-p_predicted_default[1257]         0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted_default[1258]         0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted_default[1259]         0.11    0.00  0.05    0.04    0.07    0.10
-p_predicted_default[1260]         0.16    0.00  0.07    0.05    0.10    0.15
-p_predicted_default[1261]         0.18    0.00  0.09    0.05    0.12    0.17
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-predicted_difference[1236]       -0.04    0.00  0.11   -0.22   -0.12   -0.04
-predicted_difference[1237]       -0.04    0.00  0.10   -0.21   -0.11   -0.04
-predicted_difference[1238]       -0.04    0.00  0.10   -0.21   -0.11   -0.04
-predicted_difference[1239]        0.09    0.00  0.09   -0.07    0.02    0.08
-predicted_difference[1240]        0.09    0.00  0.09   -0.07    0.02    0.08
-predicted_difference[1241]        0.08    0.00  0.08   -0.06    0.02    0.08
-predicted_difference[1242]        0.08    0.00  0.08   -0.06    0.02    0.08
-predicted_difference[1243]        0.08    0.00  0.08   -0.06    0.02    0.07
-predicted_difference[1244]        0.08    0.00  0.08   -0.06    0.02    0.07
-predicted_difference[1245]        0.08    0.00  0.08   -0.06    0.02    0.07
-predicted_difference[1246]        0.08    0.00  0.08   -0.06    0.02    0.07
-predicted_difference[1247]        0.08    0.00  0.08   -0.06    0.02    0.07
-predicted_difference[1248]        0.08    0.00  0.08   -0.06    0.02    0.07
-predicted_difference[1249]        0.30    0.00  0.40   -0.16   -0.07    0.11
-predicted_difference[1250]        0.34    0.00  0.43   -0.14   -0.05    0.13
-predicted_difference[1251]        0.30    0.00  0.40   -0.16   -0.07    0.11
-predicted_difference[1252]        0.27    0.00  0.39   -0.18   -0.09    0.08
-predicted_difference[1253]        0.33    0.00  0.43   -0.14   -0.07    0.11
-predicted_difference[1254]        0.27    0.00  0.39   -0.18   -0.09    0.08
-predicted_difference[1255]        0.27    0.00  0.39   -0.18   -0.09    0.08
-predicted_difference[1256]        0.33    0.00  0.43   -0.14   -0.07    0.11
-predicted_difference[1257]        0.27    0.00  0.39   -0.18   -0.09    0.08
-predicted_difference[1258]        0.27    0.00  0.39   -0.18   -0.09    0.08
-predicted_difference[1259]        0.33    0.00  0.43   -0.15   -0.07    0.11
-predicted_difference[1260]        0.27    0.00  0.39   -0.18   -0.09    0.08
-predicted_difference[1261]        0.24    0.00  0.38   -0.21   -0.10    0.05
-predicted_difference[1262]        0.31    0.00  0.42   -0.17   -0.08    0.08
-predicted_difference[1263]        0.24    0.00  0.38   -0.21   -0.10    0.05
-predicted_difference[1264]        0.29    0.00  0.39   -0.15   -0.07    0.11
-predicted_difference[1265]        0.34    0.00  0.42   -0.13   -0.06    0.13
-predicted_difference[1266]        0.29    0.00  0.39   -0.15   -0.07    0.11
-predicted_difference[1267]        0.90    0.00  0.23   -0.02    0.94    0.97
-predicted_difference[1268]        0.90    0.00  0.23   -0.02    0.94    0.97
-predicted_difference[1269]        0.90    0.00  0.23   -0.02    0.94    0.97
-predicted_difference[1270]        0.90    0.00  0.23   -0.02    0.94    0.97
-predicted_difference[1271]        0.90    0.00  0.23   -0.02    0.95    0.97
-predicted_difference[1272]        0.35    0.00  0.41   -0.07   -0.02    0.13
-predicted_difference[1273]        0.37    0.00  0.41   -0.05   -0.01    0.16
-predicted_difference[1274]        0.90    0.00  0.23    0.00    0.96    0.98
-predicted_difference[1275]        0.90    0.00  0.23    0.00    0.96    0.98
-predicted_difference[1276]        0.90    0.00  0.23    0.00    0.96    0.98
-predicted_difference[1277]        0.90    0.00  0.23    0.00    0.96    0.98
-predicted_difference[1278]        0.24    0.00  0.38   -0.12   -0.06    0.02
-predicted_difference[1279]        0.24    0.00  0.38   -0.12   -0.06    0.02
-predicted_difference[1280]        0.23    0.00  0.37   -0.09   -0.05    0.01
-predicted_difference[1281]        0.23    0.00  0.37   -0.09   -0.05    0.01
-predicted_difference[1282]        0.99    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1283]        1.00    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1284]        1.00    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1285]        1.00    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1286]        0.99    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1287]        0.99    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1288]        0.99    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1289]        0.99    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1290]        1.00    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1291]        1.00    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1292]        1.00    0.00  0.01    0.98    0.99    1.00
-predicted_difference[1293]        0.90    0.00  0.22    0.01    0.95    0.98
-predicted_difference[1294]        0.90    0.00  0.22    0.01    0.96    0.98
-predicted_difference[1295]        0.90    0.00  0.22    0.01    0.96    0.98
-predicted_difference[1296]        0.91    0.00  0.23    0.00    0.96    0.98
-predicted_difference[1297]        0.33    0.00  0.46   -0.22   -0.10    0.11
-predicted_difference[1298]        0.30    0.00  0.47   -0.24   -0.13    0.08
-predicted_difference[1299]        0.24    0.00  0.47   -0.34   -0.19    0.03
-predicted_difference[1300]        0.24    0.00  0.47   -0.36   -0.19    0.03
-predicted_difference[1301]        0.29    0.00  0.46   -0.29   -0.13    0.09
-predicted_difference[1302]       -0.08    0.00  0.32   -0.52   -0.31   -0.19
-predicted_difference[1303]       -0.08    0.00  0.32   -0.52   -0.31   -0.19
-predicted_difference[1304]        0.02    0.00  0.32   -0.36   -0.22   -0.12
-predicted_difference[1305]        0.02    0.00  0.32   -0.36   -0.22   -0.12
-predicted_difference[1306]        0.02    0.00  0.32   -0.36   -0.23   -0.13
-predicted_difference[1307]        0.02    0.00  0.32   -0.36   -0.23   -0.13
-predicted_difference[1308]        0.03    0.00  0.31   -0.29   -0.19   -0.11
-predicted_difference[1309]        0.03    0.00  0.31   -0.29   -0.19   -0.11
-predicted_difference[1310]        0.04    0.00  0.31   -0.28   -0.18   -0.11
-predicted_difference[1311]        0.04    0.00  0.31   -0.28   -0.18   -0.11
-predicted_difference[1312]        0.05    0.00  0.30   -0.26   -0.16   -0.10
-predicted_difference[1313]        0.05    0.00  0.30   -0.26   -0.16   -0.10
-predicted_difference[1314]        0.05    0.00  0.30   -0.25   -0.16   -0.10
-predicted_difference[1315]        0.05    0.00  0.30   -0.25   -0.16   -0.10
-predicted_difference[1316]        0.86    0.00  0.23   -0.03    0.87    0.93
-predicted_difference[1317]        0.86    0.00  0.23   -0.02    0.89    0.94
-predicted_difference[1318]        0.87    0.00  0.23   -0.02    0.89    0.94
-predicted_difference[1319]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[1320]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[1321]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[1322]        0.87    0.00  0.23   -0.02    0.90    0.95
-predicted_difference[1323]        0.87    0.00  0.23   -0.02    0.90    0.95
-predicted_difference[1324]        0.86    0.00  0.23   -0.03    0.87    0.93
-predicted_difference[1325]        0.86    0.00  0.23   -0.02    0.89    0.94
-predicted_difference[1326]        0.87    0.00  0.23   -0.02    0.89    0.94
-predicted_difference[1327]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[1328]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[1329]        0.87    0.00  0.23   -0.02    0.90    0.94
-predicted_difference[1330]        0.87    0.00  0.23   -0.02    0.90    0.95
-predicted_difference[1331]        0.05    0.00  0.35   -0.37   -0.23   -0.10
-predicted_difference[1332]        0.06    0.00  0.33   -0.31   -0.19   -0.09
-predicted_difference[1333]        0.07    0.00  0.33   -0.30   -0.18   -0.08
-predicted_difference[1334]        0.90    0.00  0.22    0.00    0.94    0.97
-predicted_difference[1335]        0.90    0.00  0.22    0.00    0.95    0.97
-predicted_difference[1336]        0.90    0.00  0.22    0.01    0.95    0.98
-predicted_difference[1337]        0.90    0.00  0.22    0.01    0.95    0.98
-predicted_difference[1338]        0.90    0.00  0.22    0.01    0.95    0.98
-predicted_difference[1339]        0.90    0.00  0.22    0.01    0.95    0.98
-predicted_difference[1340]        0.91    0.00  0.22    0.01    0.96    0.98
-predicted_difference[1341]        0.91    0.00  0.22    0.01    0.96    0.98
-predicted_difference[1342]        0.91    0.00  0.22    0.01    0.96    0.98
-predicted_difference[1343]        0.90    0.00  0.23    0.00    0.96    0.98
-lp__                           -332.26    1.49 35.27 -400.64 -356.27 -332.64
-                                   75%   97.5% n_eff Rhat
-mu[1]                             0.01    0.07 10715 1.00
-mu[2]                             0.03    0.09 18058 1.00
-mu[3]                             0.03    0.10 16182 1.00
-mu[4]                            -0.01    0.05 10873 1.00
-mu[5]                             0.00    0.06  7404 1.00
-mu[6]                             0.00    0.06  7602 1.00
-mu[7]                             0.02    0.08  8196 1.00
-mu[8]                             0.03    0.09  8765 1.00
-mu[9]                             0.03    0.09 13199 1.00
-mu[10]                            0.03    0.09  9486 1.00
-mu[11]                            0.04    0.10 10276 1.00
-mu[12]                            0.00    0.06  9725 1.00
-sigma[1]                          0.35    0.55   638 1.00
-sigma[2]                          1.04    1.31  2882 1.00
-sigma[3]                          0.77    1.04  1982 1.00
-sigma[4]                          0.36    0.51  1845 1.00
-sigma[5]                          0.23    0.39   867 1.01
-sigma[6]                          0.24    0.40   666 1.00
-sigma[7]                          0.23    0.38   779 1.00
-sigma[8]                          0.22    0.37   766 1.01
-sigma[9]                          0.41    0.65   582 1.01
-sigma[10]                         0.25    0.43   544 1.01
-sigma[11]                         0.29    0.49   551 1.01
-sigma[12]                         0.36    0.57   677 1.01
-beta[1,1]                         0.05    0.37  9066 1.00
-beta[1,2]                        -0.14    0.39  8483 1.00
-beta[1,3]                         0.96    1.48  7083 1.00
-beta[1,4]                        -0.38   -0.24  7191 1.00
-beta[1,5]                         0.10    0.39  6927 1.00
-beta[1,6]                         0.15    0.44  6029 1.00
-beta[1,7]                         0.17    0.43  6011 1.00
-beta[1,8]                         0.15    0.39  6231 1.00
-beta[1,9]                         0.53    1.24  1679 1.00
-beta[1,10]                        0.09    0.40 10766 1.00
-beta[1,11]                        0.14    0.49 10642 1.00
-beta[1,12]                       -0.04    0.24  3475 1.00
-beta[2,1]                        -0.21    0.02  1215 1.00
-beta[2,2]                        -1.06   -0.72  4682 1.00
-beta[2,3]                         0.61    0.87  7252 1.00
-beta[2,4]                         0.38    0.71  4991 1.00
-beta[2,5]                         0.02    0.24  5798 1.00
-beta[2,6]                        -0.01    0.21  4610 1.00
-beta[2,7]                         0.04    0.25  6419 1.00
-beta[2,8]                         0.14    0.40  6623 1.00
-beta[2,9]                        -0.17    0.09  1075 1.01
-beta[2,10]                        0.11    0.49 12027 1.00
-beta[2,11]                        0.00    0.21  1907 1.00
-beta[2,12]                       -0.16    0.08  1447 1.00
-beta[3,1]                         0.13    0.62 12260 1.00
-beta[3,2]                         0.48    1.68 17366 1.00
-beta[3,3]                         0.33    1.23 15895 1.00
-beta[3,4]                         0.00    0.35  9815 1.00
-beta[3,5]                         0.02    0.26  6149 1.00
-beta[3,6]                         0.03    0.28  7530 1.00
-beta[3,7]                         0.04    0.28  6650 1.00
-beta[3,8]                         0.05    0.28  7512 1.00
-beta[3,9]                         0.19    0.73 13777 1.00
-beta[3,10]                        0.11    0.47 13043 1.00
-beta[3,11]                        0.13    0.54 10910 1.00
-beta[3,12]                        0.13    0.63 13387 1.00
-beta[4,1]                         0.12    0.56 12720 1.00
-beta[4,2]                        -0.04    0.68 13634 1.00
-beta[4,3]                        -0.28    0.33  7149 1.00
-beta[4,4]                         0.22    0.58  9664 1.00
-beta[4,5]                         0.07    0.33 11536 1.00
-beta[4,6]                         0.04    0.28  7322 1.00
-beta[4,7]                         0.11    0.39 11201 1.00
-beta[4,8]                         0.17    0.47  6889 1.00
-beta[4,9]                         0.07    0.47  6613 1.00
-beta[4,10]                        0.11    0.45 12593 1.00
-beta[4,11]                        0.35    0.95  1730 1.00
-beta[4,12]                       -0.01    0.32  3528 1.00
-beta[5,1]                         0.07    0.46  9932 1.00
-beta[5,2]                        -0.77    0.20  6836 1.00
-beta[5,3]                         0.67    1.69 13393 1.00
-beta[5,4]                         0.18    0.51  9419 1.00
-beta[5,5]                         0.08    0.37  9987 1.00
-beta[5,6]                         0.06    0.31  7781 1.00
-beta[5,7]                         0.16    0.48  7046 1.00
-beta[5,8]                         0.18    0.52  4719 1.00
-beta[5,9]                         0.19    0.72 13846 1.00
-beta[5,10]                        0.10    0.44 10366 1.00
-beta[5,11]                        0.21    0.70  4859 1.00
-beta[5,12]                        0.01    0.37  4764 1.00
-beta[6,1]                         0.11    0.52 12623 1.00
-beta[6,2]                         3.50    4.82  4633 1.00
-beta[6,3]                         0.44    0.86  7594 1.00
-beta[6,4]                        -0.28    0.01  4684 1.00
-beta[6,5]                        -0.01    0.21  4154 1.00
-beta[6,6]                         0.05    0.31  8138 1.00
-beta[6,7]                         0.09    0.38  8766 1.00
-beta[6,8]                         0.14    0.45  6377 1.00
-beta[6,9]                         0.21    0.72 13286 1.00
-beta[6,10]                        0.11    0.46 11752 1.00
-beta[6,11]                        0.06    0.35  4346 1.00
-beta[6,12]                        0.21    0.71  7940 1.00
-beta[7,1]                         0.05    0.42  6783 1.00
-beta[7,2]                         0.17    0.85 10672 1.00
-beta[7,3]                         1.87    3.03  3550 1.00
-beta[7,4]                         0.08    0.42  8663 1.00
-beta[7,5]                         0.00    0.22  6178 1.00
-beta[7,6]                         0.01    0.22  4993 1.00
-beta[7,7]                         0.04    0.26  6144 1.00
-beta[7,8]                         0.05    0.27  5876 1.00
-beta[7,9]                         0.23    0.80 13090 1.00
-beta[7,10]                        0.11    0.45 12114 1.00
-beta[7,11]                        0.11    0.44 11395 1.00
-beta[7,12]                        0.10    0.52 13796 1.00
-beta[8,1]                         0.14    0.62 12389 1.00
-beta[8,2]                         0.60    1.88 15778 1.00
-beta[8,3]                         0.42    1.41 14503 1.00
-beta[8,4]                         0.15    0.63 16403 1.00
-beta[8,5]                         0.07    0.39 10728 1.00
-beta[8,6]                         0.09    0.44 11903 1.00
-beta[8,7]                         0.10    0.42 11984 1.00
-beta[8,8]                         0.11    0.43 12574 1.00
-beta[8,9]                         0.18    0.75 13906 1.00
-beta[8,10]                        0.11    0.45 12990 1.00
-beta[8,11]                        0.13    0.52 12317 1.00
-beta[8,12]                        0.13    0.61 12390 1.00
-beta[9,1]                         0.12    0.57 12798 1.00
-beta[9,2]                        -0.09    0.94 11941 1.00
-beta[9,3]                        -0.15    0.52  7824 1.00
-beta[9,4]                         0.18    0.55  8193 1.00
-beta[9,5]                         0.14    0.48  6798 1.00
-beta[9,6]                         0.21    0.57  2987 1.00
-beta[9,7]                         0.22    0.58  2913 1.00
-beta[9,8]                         0.19    0.55  4076 1.00
-beta[9,9]                         0.22    0.79 12585 1.00
-beta[9,10]                        0.11    0.47 12564 1.00
-beta[9,11]                        0.09    0.44  5830 1.00
-beta[9,12]                        0.16    0.65 11484 1.00
-beta[10,1]                        0.14    0.60  9477 1.00
-beta[10,2]                        0.35    1.48 14736 1.00
-beta[10,3]                        0.32    1.22 15226 1.00
-beta[10,4]                       -0.03    0.31  8348 1.00
-beta[10,5]                        0.01    0.24  5835 1.00
-beta[10,6]                        0.02    0.25  5457 1.00
-beta[10,7]                        0.04    0.27  5433 1.00
-beta[10,8]                        0.05    0.27  6003 1.00
-beta[10,9]                        0.17    0.72 12171 1.00
-beta[10,10]                       0.11    0.46 10585 1.00
-beta[10,11]                       0.13    0.55 11180 1.00
-beta[10,12]                       0.14    0.61 11090 1.00
-beta[11,1]                        0.13    0.59 12005 1.00
-beta[11,2]                        0.43    1.63 14648 1.00
-beta[11,3]                        0.34    1.26 14895 1.00
-beta[11,4]                       -0.08    0.24  8131 1.00
-beta[11,5]                        0.00    0.23  4133 1.00
-beta[11,6]                        0.01    0.24  3814 1.00
-beta[11,7]                        0.03    0.27  5187 1.00
-beta[11,8]                        0.05    0.27  5711 1.00
-beta[11,9]                        0.18    0.72 13678 1.00
-beta[11,10]                       0.11    0.45 12344 1.00
-beta[11,11]                       0.13    0.52 11520 1.00
-beta[11,12]                       0.14    0.63 13890 1.00
-beta[12,1]                        0.01    0.31  5371 1.00
-beta[12,2]                       -0.13    0.80 11749 1.00
-beta[12,3]                        0.72    1.65 11757 1.00
-beta[12,4]                       -0.03    0.26  8842 1.00
-beta[12,5]                        0.05    0.30 10169 1.00
-beta[12,6]                        0.12    0.42  8064 1.00
-beta[12,7]                        0.13    0.42  9868 1.00
-beta[12,8]                        0.15    0.46  8561 1.00
-beta[12,9]                        0.21    0.77 11619 1.00
-beta[12,10]                       0.11    0.47 11603 1.00
-beta[12,11]                       0.18    0.62  9231 1.00
-beta[12,12]                       0.04    0.42  6645 1.00
-beta[13,1]                        0.25    0.80  5796 1.00
-beta[13,2]                        1.87    2.69  5568 1.00
-beta[13,3]                       -1.02   -0.46  3462 1.00
-beta[13,4]                        0.07    0.37 11350 1.00
-beta[13,5]                        0.04    0.30  8940 1.00
-beta[13,6]                        0.07    0.34 11574 1.00
-beta[13,7]                        0.11    0.40 11356 1.00
-beta[13,8]                        0.11    0.38 13561 1.00
-beta[13,9]                        0.11    0.55 11082 1.00
-beta[13,10]                       0.11    0.45 10950 1.00
-beta[13,11]                       0.24    0.74  3425 1.00
-beta[13,12]                      -0.01    0.31  3809 1.00
-beta[14,1]                        0.14    0.62 13510 1.00
-beta[14,2]                        0.30    1.47 16134 1.00
-beta[14,3]                        0.25    1.10 14412 1.00
-beta[14,4]                        0.01    0.36  9865 1.00
-beta[14,5]                        0.02    0.27  6691 1.00
-beta[14,6]                        0.03    0.28  6210 1.00
-beta[14,7]                        0.05    0.29  5885 1.00
-beta[14,8]                        0.07    0.30  6243 1.00
-beta[14,9]                        0.18    0.73 14036 1.00
-beta[14,10]                       0.11    0.46 11993 1.00
-beta[14,11]                       0.14    0.54 12182 1.00
-beta[14,12]                       0.14    0.61 11473 1.00
-beta[15,1]                        0.14    0.57 12226 1.00
-beta[15,2]                        0.58    1.89 15238 1.00
-beta[15,3]                        0.42    1.33 15261 1.00
-beta[15,4]                        0.16    0.64 17472 1.00
-beta[15,5]                        0.08    0.38 12029 1.00
-beta[15,6]                        0.09    0.44 14032 1.00
-beta[15,7]                        0.10    0.42 13617 1.00
-beta[15,8]                        0.11    0.40 12142 1.00
-beta[15,9]                        0.18    0.71 13177 1.00
-beta[15,10]                       0.11    0.47 10330 1.00
-beta[15,11]                       0.14    0.56 12983 1.00
-beta[15,12]                       0.14    0.66 11426 1.00
-beta[16,1]                        0.14    0.62 13668 1.00
-beta[16,2]                        0.60    1.90 17002 1.00
-beta[16,3]                        0.43    1.40 17319 1.00
-beta[16,4]                        0.15    0.61 16222 1.00
-beta[16,5]                        0.07    0.39 10587 1.00
-beta[16,6]                        0.09    0.42 12017 1.00
-beta[16,7]                        0.10    0.44 12360 1.00
-beta[16,8]                        0.11    0.41 11787 1.00
-beta[16,9]                        0.18    0.75 14589 1.00
-beta[16,10]                       0.11    0.47 12036 1.00
-beta[16,11]                       0.14    0.55 13347 1.00
-beta[16,12]                       0.14    0.63 12584 1.00
-beta[17,1]                        0.13    0.59 11039 1.00
-beta[17,2]                        0.43    1.63 15229 1.00
-beta[17,3]                        0.34    1.28 15210 1.00
-beta[17,4]                       -0.03    0.31  9408 1.00
-beta[17,5]                        0.02    0.25  6863 1.00
-beta[17,6]                        0.02    0.25  5013 1.00
-beta[17,7]                        0.04    0.26  5727 1.00
-beta[17,8]                        0.05    0.27  5150 1.00
-beta[17,9]                        0.18    0.74 12974 1.00
-beta[17,10]                       0.11    0.44 12960 1.00
-beta[17,11]                       0.14    0.53 11285 1.00
-beta[17,12]                       0.13    0.64 10869 1.00
-beta[18,1]                        0.13    0.62 11414 1.00
-beta[18,2]                        0.49    1.72 14231 1.00
-beta[18,3]                        0.36    1.33 17391 1.00
-beta[18,4]                        0.00    0.35  9838 1.00
-beta[18,5]                        0.03    0.26  8023 1.00
-beta[18,6]                        0.03    0.28  7663 1.00
-beta[18,7]                        0.06    0.31  7575 1.00
-beta[18,8]                        0.07    0.30  8218 1.00
-beta[18,9]                        0.18    0.72 12556 1.00
-beta[18,10]                       0.11    0.46 11149 1.00
-beta[18,11]                       0.14    0.56 10909 1.00
-beta[18,12]                       0.14    0.64 12515 1.00
-beta[19,1]                        0.14    0.60 11312 1.00
-beta[19,2]                        0.60    1.88 14824 1.00
-beta[19,3]                        0.43    1.42 15500 1.00
-beta[19,4]                        0.15    0.62 16133 1.00
-beta[19,5]                        0.07    0.38 11028 1.00
-beta[19,6]                        0.09    0.40 13577 1.00
-beta[19,7]                        0.10    0.41 13373 1.00
-beta[19,8]                        0.11    0.40 12737 1.00
-beta[19,9]                        0.19    0.74 13159 1.00
-beta[19,10]                       0.11    0.46 12985 1.00
-beta[19,11]                       0.14    0.54 11927 1.00
-beta[19,12]                       0.13    0.61 12493 1.00
-beta[20,1]                        0.14    0.64 12527 1.00
-beta[20,2]                        0.58    1.79 14600 1.00
-beta[20,3]                        0.42    1.34 14157 1.00
-beta[20,4]                        0.15    0.60 14624 1.00
-beta[20,5]                        0.07    0.39 12107 1.00
-beta[20,6]                        0.08    0.43 11456 1.00
-beta[20,7]                        0.10    0.42 14420 1.00
-beta[20,8]                        0.11    0.40 12973 1.00
-beta[20,9]                        0.18    0.74 13938 1.00
-beta[20,10]                       0.11    0.47 11705 1.00
-beta[20,11]                       0.14    0.55 12037 1.00
-beta[20,12]                       0.14    0.63 10747 1.00
-beta[21,1]                        0.14    0.60 12020 1.00
-beta[21,2]                        0.59    1.82 16537 1.00
-beta[21,3]                        0.43    1.35 16669 1.00
-beta[21,4]                        0.15    0.61 14566 1.00
-beta[21,5]                        0.08    0.39  8745 1.00
-beta[21,6]                        0.09    0.43 12785 1.00
-beta[21,7]                        0.10    0.42 11216 1.00
-beta[21,8]                        0.11    0.41 13303 1.00
-beta[21,9]                        0.19    0.70 13673 1.00
-beta[21,10]                       0.11    0.45 11517 1.00
-beta[21,11]                       0.14    0.56 12222 1.00
-beta[21,12]                       0.14    0.63 13741 1.00
-beta[22,1]                        0.14    0.63 12411 1.00
-beta[22,2]                        0.58    1.86 16164 1.00
-beta[22,3]                        0.43    1.40 16086 1.00
-beta[22,4]                        0.16    0.62 16735 1.00
-beta[22,5]                        0.07    0.39 11099 1.00
-beta[22,6]                        0.09    0.44 14591 1.00
-beta[22,7]                        0.10    0.42  9810 1.00
-beta[22,8]                        0.11    0.41 12341 1.00
-beta[22,9]                        0.18    0.74 14158 1.00
-beta[22,10]                       0.11    0.47 11335 1.00
-beta[22,11]                       0.14    0.54 12673 1.00
-beta[22,12]                       0.13    0.59 13421 1.00
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-mu_prior[2]                       0.03    0.10  9705 1.00
-mu_prior[3]                       0.03    0.10  9157 1.00
-mu_prior[4]                       0.03    0.10  9624 1.00
-mu_prior[5]                       0.03    0.10  9532 1.00
-mu_prior[6]                       0.03    0.10  9869 1.00
-mu_prior[7]                       0.03    0.10  9627 1.00
-mu_prior[8]                       0.03    0.10 10070 1.00
-mu_prior[9]                       0.03    0.10  9363 1.00
-mu_prior[10]                      0.03    0.10  9753 1.00
-mu_prior[11]                      0.03    0.10 10068 1.00
-mu_prior[12]                      0.03    0.10  9961 1.00
-sigma_prior[1]                    0.26    0.44  9535 1.00
-sigma_prior[2]                    0.26    0.44  9783 1.00
-sigma_prior[3]                    0.25    0.44 10247 1.00
-sigma_prior[4]                    0.26    0.44  9757 1.00
-sigma_prior[5]                    0.26    0.43  9352 1.00
-sigma_prior[6]                    0.25    0.44  9706 1.00
-sigma_prior[7]                    0.26    0.44  9938 1.00
-sigma_prior[8]                    0.25    0.43  9889 1.00
-sigma_prior[9]                    0.25    0.45 10163 1.00
-sigma_prior[10]                   0.25    0.44 10090 1.00
-sigma_prior[11]                   0.25    0.44  9750 1.00
-sigma_prior[12]                   0.25    0.44  9493 1.00
-p_prior[1]                        0.99    1.00 10222 1.00
-p_prior[2]                        0.99    1.00 10216 1.00
-p_prior[3]                        0.99    1.00 10210 1.00
-p_prior[4]                        0.99    1.00 10214 1.00
-p_prior[5]                        0.99    1.00 10213 1.00
-p_prior[6]                        0.99    1.00 10212 1.00
-p_prior[7]                        0.99    1.00 10213 1.00
-p_prior[8]                        0.99    1.00 10208 1.00
-p_prior[9]                        0.99    1.00  9824 1.00
-p_prior[10]                       0.99    1.00  9829 1.00
-p_prior[11]                       0.98    1.00  9946 1.00
-p_prior[12]                       0.98    1.00  9910 1.00
-p_prior[13]                       0.98    1.00  9907 1.00
-p_prior[14]                       0.98    1.00  9906 1.00
-p_prior[15]                       0.98    1.00  9910 1.00
-p_prior[16]                       0.98    1.00  9904 1.00
-p_prior[17]                       0.98    1.00  9904 1.00
-p_prior[18]                       0.98    1.00  9904 1.00
-p_prior[19]                       0.98    1.00  9903 1.00
-p_prior[20]                       0.98    1.00  9905 1.00
-p_prior[21]                       0.99    1.00 10227 1.00
-p_prior[22]                       0.99    1.00 10208 1.00
-p_prior[23]                       0.99    1.00  9892 1.00
-p_prior[24]                       0.99    1.00  9884 1.00
-p_prior[25]                       0.99    1.00  9869 1.00
-p_prior[26]                       0.99    1.00  9827 1.00
-p_prior[27]                       0.99    1.00  9821 1.00
-p_prior[28]                       0.99    1.00  9813 1.00
-p_prior[29]                       0.99    1.00  9809 1.00
-p_prior[30]                       0.99    1.00  9900 1.00
-p_prior[31]                       0.99    1.00  9900 1.00
-p_prior[32]                       0.99    1.00  9893 1.00
-p_prior[33]                       0.99    1.00  9893 1.00
-p_prior[34]                       0.99    1.00  9886 1.00
-p_prior[35]                       0.99    1.00  9886 1.00
-p_prior[36]                       0.99    1.00  9882 1.00
-p_prior[37]                       0.99    1.00  9882 1.00
-p_prior[38]                       0.99    1.00  9898 1.00
-p_prior[39]                       0.99    1.00  9898 1.00
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-p_prior[43]                       0.99    1.00  9848 1.00
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-p_prior[50]                       0.98    1.00 10201 1.00
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-p_prior[53]                       0.98    1.00 10183 1.00
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-p_prior[59]                       0.99    1.00 10182 1.00
-p_prior[60]                       0.99    1.00 10182 1.00
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-p_prior[944]                      0.99    1.00  9876 1.00
-p_prior[945]                      0.99    1.00  9870 1.00
-p_prior[946]                      0.99    1.00  9825 1.00
-p_prior[947]                      0.99    1.00  9825 1.00
-p_prior[948]                      0.99    1.00  9826 1.00
-p_prior[949]                      0.99    1.00  9822 1.00
-p_prior[950]                      0.97    1.00 10207 1.00
-p_prior[951]                      0.97    1.00 10207 1.00
-p_prior[952]                      0.97    1.00 10207 1.00
-p_prior[953]                      0.97    1.00 10166 1.00
-p_prior[954]                      0.97    1.00 10166 1.00
-p_prior[955]                      0.97    1.00 10166 1.00
-p_prior[956]                      0.97    1.00 10166 1.00
-p_prior[957]                      0.97    1.00 10166 1.00
-p_prior[958]                      0.97    1.00 10166 1.00
-p_prior[959]                      0.97    1.00 10171 1.00
-p_prior[960]                      0.97    1.00 10171 1.00
-p_prior[961]                      0.97    1.00 10171 1.00
-p_prior[962]                      0.97    1.00 10165 1.00
-p_prior[963]                      0.97    1.00 10165 1.00
-p_prior[964]                      0.97    1.00 10165 1.00
-p_prior[965]                      0.97    1.00 10165 1.00
-p_prior[966]                      0.97    1.00 10165 1.00
-p_prior[967]                      0.97    1.00 10165 1.00
-p_prior[968]                      0.97    1.00 10165 1.00
-p_prior[969]                      0.97    1.00 10165 1.00
-p_prior[970]                      0.97    1.00 10165 1.00
-p_prior[971]                      0.97    1.00 10166 1.00
-p_prior[972]                      0.97    1.00 10166 1.00
-p_prior[973]                      0.97    1.00 10166 1.00
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-p_prior[975]                      0.97    1.00 10167 1.00
-p_prior[976]                      0.97    1.00 10167 1.00
-p_prior[977]                      0.98    1.00  9843 1.00
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-p_prior[979]                      0.99    1.00  9769 1.00
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-p_prior[985]                      0.99    1.00  9759 1.00
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-p_prior[989]                      0.99    1.00  9766 1.00
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-p_prior[996]                      0.99    1.00 10204 1.00
-p_prior[997]                      0.99    1.00 10235 1.00
-p_prior[998]                      0.99    1.00 10223 1.00
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-p_prior[1000]                     0.99    1.00  9824 1.00
-p_prior[1001]                     0.99    1.00  9837 1.00
-p_prior[1002]                     0.99    1.00  9838 1.00
-p_prior[1003]                     0.99    1.00  9841 1.00
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-p_prior[1007]                     1.00    1.00 10008 1.00
-p_prior[1008]                     1.00    1.00  9995 1.00
-p_prior[1009]                     0.99    1.00 10035 1.00
-p_prior[1010]                     0.99    1.00 10000 1.00
-p_prior[1011]                     0.99    1.00 10017 1.00
-p_prior[1012]                     0.99    1.00  9861 1.00
-p_prior[1013]                     0.99    1.00  9929 1.00
-p_prior[1014]                     0.99    1.00  9953 1.00
-p_prior[1015]                     0.99    1.00  9832 1.00
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-p_prior[1017]                     0.99    1.00  9939 1.00
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-p_prior[1020]                     0.99    1.00  9969 1.00
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-p_prior[1022]                     0.99    1.00  9970 1.00
-p_prior[1023]                     0.99    1.00  9970 1.00
-p_prior[1024]                     0.99    1.00  9969 1.00
-p_prior[1025]                     0.99    1.00  9969 1.00
-p_prior[1026]                     0.99    1.00  9969 1.00
-p_prior[1027]                     0.99    1.00  9834 1.00
-p_prior[1028]                     0.99    1.00  9864 1.00
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-p_prior[1032]                     0.99    1.00  9970 1.00
-p_prior[1033]                     0.99    1.00  9970 1.00
-p_prior[1034]                     0.99    1.00  9970 1.00
-p_prior[1035]                     0.99    1.00  9969 1.00
-p_prior[1036]                     0.99    1.00  9969 1.00
-p_prior[1037]                     0.99    1.00  9969 1.00
-p_prior[1038]                     0.99    1.00 10164 1.00
-p_prior[1039]                     0.99    1.00 10176 1.00
-p_prior[1040]                     0.99    1.00 10162 1.00
-p_prior[1041]                     0.99    1.00 10174 1.00
-p_prior[1042]                     0.99    1.00 10161 1.00
-p_prior[1043]                     0.99    1.00 10171 1.00
-p_prior[1044]                     0.99    1.00 10160 1.00
-p_prior[1045]                     0.99    1.00 10170 1.00
-p_prior[1046]                     0.99    1.00 10160 1.00
-p_prior[1047]                     0.99    1.00 10170 1.00
-p_prior[1048]                     0.99    1.00 10159 1.00
-p_prior[1049]                     0.99    1.00 10170 1.00
-p_prior[1050]                     0.99    1.00 10141 1.00
-p_prior[1051]                     0.99    1.00 10146 1.00
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-p_prior[1053]                     0.98    1.00  9595 1.00
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-p_prior[1055]                     0.98    1.00  9593 1.00
-p_prior[1056]                     0.98    1.00  9593 1.00
-p_prior[1057]                     0.98    1.00  9593 1.00
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-p_prior[1059]                     0.98    1.00  9592 1.00
-p_prior[1060]                     0.98    1.00  9647 1.00
-p_prior[1061]                     0.98    1.00  9647 1.00
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-p_prior[1063]                     0.98    1.00  9593 1.00
-p_prior[1064]                     0.98    1.00  9593 1.00
-p_prior[1065]                     0.98    1.00  9593 1.00
-p_prior[1066]                     0.98    1.00  9592 1.00
-p_prior[1067]                     0.98    1.00  9593 1.00
-p_prior[1068]                     0.98    1.00  9593 1.00
-p_prior[1069]                     0.98    1.00  9648 1.00
-p_prior[1070]                     0.98    1.00  9647 1.00
-p_prior[1071]                     0.99    1.00  9581 1.00
-p_prior[1072]                     0.99    1.00  9544 1.00
-p_prior[1073]                     0.99    1.00  9460 1.00
-p_prior[1074]                     0.97    1.00 10172 1.00
-p_prior[1075]                     0.97    1.00 10172 1.00
-p_prior[1076]                     0.97    1.00 10212 1.00
-p_prior[1077]                     0.97    1.00 10212 1.00
-p_prior[1078]                     0.97    1.00 10174 1.00
-p_prior[1079]                     0.97    1.00 10174 1.00
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-p_prior[1081]                     0.97    1.00 10175 1.00
-p_prior[1082]                     0.97    1.00 10177 1.00
-p_prior[1083]                     0.97    1.00 10177 1.00
-p_prior[1084]                     0.60    0.78  9798 1.00
-p_prior[1085]                     0.59    0.79  9519 1.00
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-p_prior[1088]                     0.60    0.79  9536 1.00
-p_prior[1089]                     0.61    0.80  9542 1.00
-p_prior[1090]                     0.61    0.81  9544 1.00
-p_prior[1091]                     0.98    1.00 10189 1.00
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-p_prior[1096]                     0.98    1.00 10189 1.00
-p_prior[1097]                     0.98    1.00 10189 1.00
-p_prior[1098]                     0.98    1.00 10189 1.00
-p_prior[1099]                     0.98    1.00 10189 1.00
-p_prior[1100]                     0.98    1.00 10189 1.00
-p_prior[1101]                     0.98    1.00 10189 1.00
-p_prior[1102]                     0.98    1.00 10191 1.00
-p_prior[1103]                     0.99    1.00 10158 1.00
-p_prior[1104]                     0.99    1.00 10158 1.00
-p_prior[1105]                     0.99    1.00 10159 1.00
-p_prior[1106]                     0.99    1.00  9854 1.00
-p_prior[1107]                     0.99    1.00  9854 1.00
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-p_prior[1109]                     0.99    1.00  9807 1.00
-p_prior[1110]                     0.99    1.00  9824 1.00
-p_prior[1111]                     0.99    1.00  9859 1.00
-p_prior[1112]                     0.99    1.00  9852 1.00
-p_prior[1113]                     0.99    1.00  9851 1.00
-p_prior[1114]                     0.99    1.00  9807 1.00
-p_prior[1115]                     0.99    1.00  9818 1.00
-p_prior[1116]                     0.58    0.75  9543 1.00
-p_prior[1117]                     0.58    0.75  9545 1.00
-p_prior[1118]                     0.58    0.75  9560 1.00
-p_prior[1119]                     0.58    0.75  9581 1.00
-p_prior[1120]                     0.58    0.75  9579 1.00
-p_prior[1121]                     0.59    0.76  9576 1.00
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-p_prior[1124]                     0.59    0.76  9576 1.00
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-p_prior[1126]                     0.59    0.76  9577 1.00
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-p_prior[1129]                     0.97    1.00 10207 1.00
-p_prior[1130]                     0.97    1.00 10167 1.00
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-p_prior[1132]                     0.99    1.00 10241 1.00
-p_prior[1133]                     0.99    1.00  9681 1.00
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-p_predicted_intervention[798]     0.39    0.54  1961 1.00
-p_predicted_intervention[799]     0.15    1.00  9582 1.00
-p_predicted_intervention[800]     0.15    1.00  9599 1.00
-p_predicted_intervention[801]     0.15    1.00  9602 1.00
-p_predicted_intervention[802]     0.16    1.00  9614 1.00
-p_predicted_intervention[803]     0.16    1.00  9630 1.00
-p_predicted_intervention[804]     0.15    1.00  9636 1.00
-p_predicted_intervention[805]     0.16    1.00  9561 1.00
-p_predicted_intervention[806]     0.16    1.00  9562 1.00
-p_predicted_intervention[807]     1.00    1.00 10008 1.00
-p_predicted_intervention[808]     1.00    1.00 10007 1.00
-p_predicted_intervention[809]     1.00    1.00 10008 1.00
-p_predicted_intervention[810]     1.00    1.00 10007 1.00
-p_predicted_intervention[811]     1.00    1.00 10007 1.00
-p_predicted_intervention[812]     1.00    1.00 10007 1.00
-p_predicted_intervention[813]     1.00    1.00 10007 1.00
-p_predicted_intervention[814]     1.00    1.00 10007 1.00
-p_predicted_intervention[815]     1.00    1.00 10007 1.00
-p_predicted_intervention[816]     1.00    1.00 10007 1.00
-p_predicted_intervention[817]     1.00    1.00 10007 1.00
-p_predicted_intervention[818]     0.48    1.00 10031 1.00
-p_predicted_intervention[819]     0.49    1.00 10062 1.00
-p_predicted_intervention[820]     0.49    1.00 10060 1.00
-p_predicted_intervention[821]     0.49    1.00 10025 1.00
-p_predicted_intervention[822]     0.47    1.00  9988 1.00
-p_predicted_intervention[823]     0.48    1.00 10005 1.00
-p_predicted_intervention[824]     0.47    1.00 10004 1.00
-p_predicted_intervention[825]     0.46    1.00  9976 1.00
-p_predicted_intervention[826]     0.46    1.00  9986 1.00
-p_predicted_intervention[827]     0.44    1.00  9942 1.00
-p_predicted_intervention[828]     0.57    1.00 10669 1.00
-p_predicted_intervention[829]     0.56    1.00 10672 1.00
-p_predicted_intervention[830]     0.00    0.49  8361 1.00
-p_predicted_intervention[831]     0.00    0.48  8332 1.00
-p_predicted_intervention[832]     0.00    0.48  8356 1.00
-p_predicted_intervention[833]     0.00    0.47  8338 1.00
-p_predicted_intervention[834]     0.00    0.46  8334 1.00
-p_predicted_intervention[835]     0.00    0.47  8355 1.00
-p_predicted_intervention[836]     0.00    0.00  8831 1.00
-p_predicted_intervention[837]     0.37    0.59  1761 1.00
-p_predicted_intervention[838]     0.00    0.00  8904 1.00
-p_predicted_intervention[839]     0.30    0.55  1646 1.00
-p_predicted_intervention[840]     0.00    0.00  8893 1.00
-p_predicted_intervention[841]     0.30    0.55  1643 1.00
-p_predicted_intervention[842]     0.00    0.00  8901 1.00
-p_predicted_intervention[843]     0.30    0.55  1645 1.00
-p_predicted_intervention[844]     0.00    0.00  9064 1.00
-p_predicted_intervention[845]     0.25    0.50  1731 1.00
-p_predicted_intervention[846]     0.00    0.00  9058 1.00
-p_predicted_intervention[847]     0.25    0.51  1729 1.00
-p_predicted_intervention[848]     0.00    0.00  9120 1.00
-p_predicted_intervention[849]     0.24    0.48  1745 1.00
-p_predicted_intervention[850]     0.00    0.00  9043 1.00
-p_predicted_intervention[851]     0.30    0.54  2055 1.00
-p_predicted_intervention[852]     0.00    0.00  9291 1.00
-p_predicted_intervention[853]     0.24    0.47  2232 1.00
-p_predicted_intervention[854]     0.00    0.00  9361 1.00
-p_predicted_intervention[855]     0.22    0.46  2271 1.00
-p_predicted_intervention[856]     0.00    0.00  9509 1.00
-p_predicted_intervention[857]     0.18    0.42  2348 1.00
-p_predicted_intervention[858]     0.33    0.49  1334 1.00
-p_predicted_intervention[859]     0.32    0.48  1330 1.00
-p_predicted_intervention[860]     0.31    0.46  1324 1.00
-p_predicted_intervention[861]     0.30    0.46  1323 1.00
-p_predicted_intervention[862]     0.29    0.44  1318 1.00
-p_predicted_intervention[863]     0.28    0.43  1316 1.00
-p_predicted_intervention[864]     0.35    0.50  1990 1.00
-p_predicted_intervention[865]     0.32    0.48  2067 1.00
-p_predicted_intervention[866]     0.31    0.46  2104 1.00
-p_predicted_intervention[867]     0.32    0.47  2080 1.00
-p_predicted_intervention[868]     0.32    0.48  2069 1.00
-p_predicted_intervention[869]     0.33    0.48  2051 1.00
-p_predicted_intervention[870]     0.21    1.00  9378 1.00
-p_predicted_intervention[871]     0.22    1.00  9497 1.00
-p_predicted_intervention[872]     0.22    1.00  9505 1.00
-p_predicted_intervention[873]     0.21    1.00  9507 1.00
-p_predicted_intervention[874]     0.22    1.00  9481 1.00
-p_predicted_intervention[875]     0.21    1.00  9484 1.00
-p_predicted_intervention[876]     0.00    0.01  8401 1.00
-p_predicted_intervention[877]     0.00    0.02  8126 1.00
-p_predicted_intervention[878]     0.00    0.01  8528 1.00
-p_predicted_intervention[879]     0.00    0.01  8570 1.00
-p_predicted_intervention[880]     0.56    1.00  7521 1.00
-p_predicted_intervention[881]     0.64    1.00  7509 1.00
-p_predicted_intervention[882]     0.54    1.00  7529 1.00
-p_predicted_intervention[883]     0.52    1.00  7543 1.00
-p_predicted_intervention[884]     0.51    1.00  7542 1.00
-p_predicted_intervention[885]     0.52    1.00 10594 1.00
-p_predicted_intervention[886]     0.57    1.00 10669 1.00
-p_predicted_intervention[887]     0.57    1.00 10668 1.00
-p_predicted_intervention[888]     0.57    1.00 10667 1.00
-p_predicted_intervention[889]     0.57    1.00 10663 1.00
-p_predicted_intervention[890]     0.56    1.00 10654 1.00
-p_predicted_intervention[891]     0.55    1.00 10651 1.00
-p_predicted_intervention[892]     0.55    1.00 10652 1.00
-p_predicted_intervention[893]     0.55    1.00 10650 1.00
-p_predicted_intervention[894]     0.55    1.00 10649 1.00
-p_predicted_intervention[895]     0.55    1.00 10646 1.00
-p_predicted_intervention[896]     0.54    1.00 10670 1.00
-p_predicted_intervention[897]     0.47    1.00 10587 1.00
-p_predicted_intervention[898]     0.47    1.00 10582 1.00
-p_predicted_intervention[899]     0.47    1.00 10597 1.00
-p_predicted_intervention[900]     0.47    1.00 10590 1.00
-p_predicted_intervention[901]     0.39    0.56  1631 1.00
-p_predicted_intervention[902]     0.32    0.52  1432 1.00
-p_predicted_intervention[903]     0.33    0.52  1942 1.00
-p_predicted_intervention[904]     0.31    0.50  1990 1.00
-p_predicted_intervention[905]     0.29    0.48  2046 1.00
-p_predicted_intervention[906]     0.26    0.45  2108 1.00
-p_predicted_intervention[907]     0.00    0.01  8384 1.00
-p_predicted_intervention[908]     0.00    0.01  8534 1.00
-p_predicted_intervention[909]     0.00    0.01  7989 1.00
-p_predicted_intervention[910]     0.00    0.01  8170 1.00
-p_predicted_intervention[911]     0.00    0.01  8179 1.00
-p_predicted_intervention[912]     0.97    1.00  9635 1.00
-p_predicted_intervention[913]     0.99    1.00  9693 1.00
-p_predicted_intervention[914]     0.97    1.00  9635 1.00
-p_predicted_intervention[915]     0.97    1.00  9638 1.00
-p_predicted_intervention[916]     0.99    1.00  9706 1.00
-p_predicted_intervention[917]     0.97    1.00  9638 1.00
-p_predicted_intervention[918]     0.98    1.00  9591 1.00
-p_predicted_intervention[919]     0.99    1.00  9647 1.00
-p_predicted_intervention[920]     0.98    1.00  9591 1.00
-p_predicted_intervention[921]     0.98    1.00  9573 1.00
-p_predicted_intervention[922]     0.99    1.00  9640 1.00
-p_predicted_intervention[923]     0.98    1.00  9573 1.00
-p_predicted_intervention[924]     0.00    0.00  8318 1.00
-p_predicted_intervention[925]     0.00    0.00  9192 1.00
-p_predicted_intervention[926]     0.00    0.00  9197 1.00
-p_predicted_intervention[927]     0.00    0.00  9180 1.00
-p_predicted_intervention[928]     0.00    0.00  9324 1.00
-p_predicted_intervention[929]     0.00    0.00  9274 1.00
-p_predicted_intervention[930]     0.00    0.00  9366 1.00
-p_predicted_intervention[931]     0.00    0.00  9611 1.00
-p_predicted_intervention[932]     0.58    1.00 10687 1.00
-p_predicted_intervention[933]     0.58    1.00 10687 1.00
-p_predicted_intervention[934]     0.53    1.00 10613 1.00
-p_predicted_intervention[935]     0.58    1.00 10686 1.00
-p_predicted_intervention[936]     0.58    1.00 10686 1.00
-p_predicted_intervention[937]     0.57    1.00 10685 1.00
-p_predicted_intervention[938]     0.57    1.00 10684 1.00
-p_predicted_intervention[939]     0.57    1.00 10679 1.00
-p_predicted_intervention[940]     0.57    1.00 10681 1.00
-p_predicted_intervention[941]     0.57    1.00 10679 1.00
-p_predicted_intervention[942]     0.57    1.00 10677 1.00
-p_predicted_intervention[943]     0.57    1.00 10675 1.00
-p_predicted_intervention[944]     0.00    0.00  9290 1.00
-p_predicted_intervention[945]     0.00    0.00  9319 1.00
-p_predicted_intervention[946]     0.00    0.00  9080 1.00
-p_predicted_intervention[947]     0.00    0.00  9313 1.00
-p_predicted_intervention[948]     0.00    0.00  9311 1.00
-p_predicted_intervention[949]     0.00    0.00  9341 1.00
-p_predicted_intervention[950]     0.57    0.99  6691 1.00
-p_predicted_intervention[951]     0.57    0.99  6691 1.00
-p_predicted_intervention[952]     0.57    0.99  6691 1.00
-p_predicted_intervention[953]     0.47    0.98  6929 1.00
-p_predicted_intervention[954]     0.47    0.98  6929 1.00
-p_predicted_intervention[955]     0.47    0.98  6929 1.00
-p_predicted_intervention[956]     0.46    0.98  6933 1.00
-p_predicted_intervention[957]     0.46    0.98  6933 1.00
-p_predicted_intervention[958]     0.46    0.98  6933 1.00
-p_predicted_intervention[959]     0.44    0.98  6940 1.00
-p_predicted_intervention[960]     0.44    0.98  6940 1.00
-p_predicted_intervention[961]     0.44    0.98  6940 1.00
-p_predicted_intervention[962]     0.46    0.98  6938 1.00
-p_predicted_intervention[963]     0.46    0.98  6938 1.00
-p_predicted_intervention[964]     0.46    0.98  6938 1.00
-p_predicted_intervention[965]     0.45    0.98  6947 1.00
-p_predicted_intervention[966]     0.45    0.98  6947 1.00
-p_predicted_intervention[967]     0.45    0.98  6947 1.00
-p_predicted_intervention[968]     0.45    0.98  6947 1.00
-p_predicted_intervention[969]     0.45    0.98  6947 1.00
-p_predicted_intervention[970]     0.45    0.98  6947 1.00
-p_predicted_intervention[971]     0.44    0.98  6929 1.00
-p_predicted_intervention[972]     0.44    0.98  6929 1.00
-p_predicted_intervention[973]     0.44    0.98  6929 1.00
-p_predicted_intervention[974]     0.43    0.98  6936 1.00
-p_predicted_intervention[975]     0.43    0.98  6936 1.00
-p_predicted_intervention[976]     0.43    0.98  6936 1.00
-p_predicted_intervention[977]     0.53    1.00 10614 1.00
-p_predicted_intervention[978]     0.58    1.00 10687 1.00
-p_predicted_intervention[979]     0.58    1.00 10687 1.00
-p_predicted_intervention[980]     0.58    1.00 10687 1.00
-p_predicted_intervention[981]     0.53    1.00 10613 1.00
-p_predicted_intervention[982]     0.58    1.00 10686 1.00
-p_predicted_intervention[983]     0.58    1.00 10685 1.00
-p_predicted_intervention[984]     0.57    1.00 10685 1.00
-p_predicted_intervention[985]     0.57    1.00 10679 1.00
-p_predicted_intervention[986]     0.57    1.00 10678 1.00
-p_predicted_intervention[987]     0.57    1.00 10679 1.00
-p_predicted_intervention[988]     0.57    1.00 10681 1.00
-p_predicted_intervention[989]     0.57    1.00 10679 1.00
-p_predicted_intervention[990]     0.57    1.00 10677 1.00
-p_predicted_intervention[991]     0.57    1.00 10675 1.00
-p_predicted_intervention[992]     0.56    1.00 10693 1.00
-p_predicted_intervention[993]     0.22    1.00  9658 1.00
-p_predicted_intervention[994]     0.22    1.00  9614 1.00
-p_predicted_intervention[995]     0.21    1.00  9625 1.00
-p_predicted_intervention[996]     0.00    0.98  8598 1.00
-p_predicted_intervention[997]     0.00    0.97  8683 1.00
-p_predicted_intervention[998]     0.00    0.98  8621 1.00
-p_predicted_intervention[999]     0.54    1.00 10641 1.00
-p_predicted_intervention[1000]    0.54    1.00 10639 1.00
-p_predicted_intervention[1001]    0.56    1.00 10696 1.00
-p_predicted_intervention[1002]    0.56    1.00 10695 1.00
-p_predicted_intervention[1003]    0.56    1.00 10694 1.00
-p_predicted_intervention[1004]    0.98    1.00  9623 1.00
-p_predicted_intervention[1005]    0.98    1.00  9655 1.00
-p_predicted_intervention[1006]    0.98    1.00  9656 1.00
-p_predicted_intervention[1007]    0.99    1.00  9583 1.00
-p_predicted_intervention[1008]    0.99    1.00  9570 1.00
-p_predicted_intervention[1009]    0.97    1.00  9530 1.00
-p_predicted_intervention[1010]    0.96    1.00  9561 1.00
-p_predicted_intervention[1011]    0.97    1.00  9483 1.00
-p_predicted_intervention[1012]    0.49    1.00 10066 1.00
-p_predicted_intervention[1013]    0.46    1.00  9985 1.00
-p_predicted_intervention[1014]    0.44    1.00  9943 1.00
-p_predicted_intervention[1015]    0.48    1.00 10034 1.00
-p_predicted_intervention[1016]    0.49    1.00 10055 1.00
-p_predicted_intervention[1017]    0.46    1.00  9967 1.00
-p_predicted_intervention[1018]    0.45    1.00  9957 1.00
-p_predicted_intervention[1019]    0.45    1.00  9949 1.00
-p_predicted_intervention[1020]    0.37    1.00  9801 1.00
-p_predicted_intervention[1021]    0.41    1.00  9867 1.00
-p_predicted_intervention[1022]    0.40    1.00  9844 1.00
-p_predicted_intervention[1023]    0.40    1.00  9834 1.00
-p_predicted_intervention[1024]    0.39    1.00  9820 1.00
-p_predicted_intervention[1025]    0.37    1.00  9808 1.00
-p_predicted_intervention[1026]    0.37    1.00  9804 1.00
-p_predicted_intervention[1027]    0.48    1.00 10031 1.00
-p_predicted_intervention[1028]    0.49    1.00 10062 1.00
-p_predicted_intervention[1029]    0.48    1.00 10011 1.00
-p_predicted_intervention[1030]    0.45    1.00  9954 1.00
-p_predicted_intervention[1031]    0.45    1.00  9951 1.00
-p_predicted_intervention[1032]    0.42    1.00  9876 1.00
-p_predicted_intervention[1033]    0.41    1.00  9856 1.00
-p_predicted_intervention[1034]    0.41    1.00  9851 1.00
-p_predicted_intervention[1035]    0.40    1.00  9836 1.00
-p_predicted_intervention[1036]    0.39    1.00  9824 1.00
-p_predicted_intervention[1037]    0.37    1.00  9811 1.00
-p_predicted_intervention[1038]    0.63    1.00  7624 1.00
-p_predicted_intervention[1039]    0.60    1.00  7787 1.00
-p_predicted_intervention[1040]    0.62    1.00  7630 1.00
-p_predicted_intervention[1041]    0.59    1.00  7790 1.00
-p_predicted_intervention[1042]    0.62    1.00  7634 1.00
-p_predicted_intervention[1043]    0.59    1.00  7795 1.00
-p_predicted_intervention[1044]    0.62    1.00  7634 1.00
-p_predicted_intervention[1045]    0.58    1.00  7796 1.00
-p_predicted_intervention[1046]    0.62    1.00  7635 1.00
-p_predicted_intervention[1047]    0.58    1.00  7797 1.00
-p_predicted_intervention[1048]    0.62    1.00  7643 1.00
-p_predicted_intervention[1049]    0.58    1.00  7804 1.00
-p_predicted_intervention[1050]    0.53    1.00  7537 1.00
-p_predicted_intervention[1051]    0.49    0.99  7788 1.00
-p_predicted_intervention[1052]    1.00    1.00  8233 1.00
-p_predicted_intervention[1053]    1.00    1.00  8233 1.00
-p_predicted_intervention[1054]    1.00    1.00  8187 1.00
-p_predicted_intervention[1055]    1.00    1.00  8187 1.00
-p_predicted_intervention[1056]    1.00    1.00  8156 1.00
-p_predicted_intervention[1057]    1.00    1.00  8156 1.00
-p_predicted_intervention[1058]    1.00    1.00  8144 1.00
-p_predicted_intervention[1059]    1.00    1.00  8144 1.00
-p_predicted_intervention[1060]    1.00    1.00  8195 1.00
-p_predicted_intervention[1061]    1.00    1.00  8195 1.00
-p_predicted_intervention[1062]    1.00    1.00  8227 1.00
-p_predicted_intervention[1063]    1.00    1.00  8199 1.00
-p_predicted_intervention[1064]    1.00    1.00  8192 1.00
-p_predicted_intervention[1065]    1.00    1.00  8157 1.00
-p_predicted_intervention[1066]    1.00    1.00  8150 1.00
-p_predicted_intervention[1067]    1.00    1.00  8173 1.00
-p_predicted_intervention[1068]    1.00    1.00  8160 1.00
-p_predicted_intervention[1069]    1.00    1.00  8212 1.00
-p_predicted_intervention[1070]    1.00    1.00  8191 1.00
-p_predicted_intervention[1071]    0.65    1.00 10329 1.00
-p_predicted_intervention[1072]    0.65    1.00 10290 1.00
-p_predicted_intervention[1073]    0.66    1.00 10286 1.00
-p_predicted_intervention[1074]    0.52    0.98  7242 1.00
-p_predicted_intervention[1075]    0.52    0.98  7242 1.00
-p_predicted_intervention[1076]    0.60    0.99  7086 1.00
-p_predicted_intervention[1077]    0.60    0.99  7086 1.00
-p_predicted_intervention[1078]    0.51    0.98  7241 1.00
-p_predicted_intervention[1079]    0.51    0.98  7241 1.00
-p_predicted_intervention[1080]    0.51    0.98  7240 1.00
-p_predicted_intervention[1081]    0.51    0.98  7240 1.00
-p_predicted_intervention[1082]    0.50    0.98  7239 1.00
-p_predicted_intervention[1083]    0.50    0.98  7239 1.00
-p_predicted_intervention[1084]    0.38    0.56  1635 1.00
-p_predicted_intervention[1085]    0.31    0.52  1477 1.00
-p_predicted_intervention[1086]    0.35    0.54  1891 1.00
-p_predicted_intervention[1087]    0.34    0.53  1921 1.00
-p_predicted_intervention[1088]    0.32    0.50  1987 1.00
-p_predicted_intervention[1089]    0.28    0.47  2069 1.00
-p_predicted_intervention[1090]    0.27    0.47  2080 1.00
-p_predicted_intervention[1091]    0.61    0.99  7027 1.00
-p_predicted_intervention[1092]    0.60    0.99  7029 1.00
-p_predicted_intervention[1093]    0.61    0.99  7026 1.00
-p_predicted_intervention[1094]    0.60    0.99  7030 1.00
-p_predicted_intervention[1095]    0.59    0.99  7047 1.00
-p_predicted_intervention[1096]    0.60    0.99  7034 1.00
-p_predicted_intervention[1097]    0.59    0.99  7047 1.00
-p_predicted_intervention[1098]    0.59    0.99  7036 1.00
-p_predicted_intervention[1099]    0.58    0.99  7049 1.00
-p_predicted_intervention[1100]    0.58    0.99  7049 1.00
-p_predicted_intervention[1101]    0.58    0.99  7052 1.00
-p_predicted_intervention[1102]    0.57    0.99  7019 1.00
-p_predicted_intervention[1103]    0.48    0.99  7151 1.00
-p_predicted_intervention[1104]    0.47    0.99  7136 1.00
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-predicted_difference[1061]        0.94    0.98  7802 1.00
-predicted_difference[1062]        0.93    0.97  7757 1.00
-predicted_difference[1063]        0.93    0.98  7814 1.00
-predicted_difference[1064]        0.93    0.98  7828 1.00
-predicted_difference[1065]        0.94    0.98  7883 1.00
-predicted_difference[1066]        0.94    0.98  7893 1.00
-predicted_difference[1067]        0.94    0.98  7859 1.00
-predicted_difference[1068]        0.94    0.98  7889 1.00
-predicted_difference[1069]        0.94    0.98  7786 1.00
-predicted_difference[1070]        0.94    0.98  7805 1.00
-predicted_difference[1071]        0.65    1.00 10334 1.00
-predicted_difference[1072]        0.65    1.00 10279 1.00
-predicted_difference[1073]        0.66    1.00 10268 1.00
-predicted_difference[1074]        0.39    0.89  7359 1.00
-predicted_difference[1075]        0.39    0.89  7359 1.00
-predicted_difference[1076]        0.46    0.87  6987 1.00
-predicted_difference[1077]        0.46    0.87  6987 1.00
-predicted_difference[1078]        0.39    0.89  7343 1.00
-predicted_difference[1079]        0.39    0.89  7343 1.00
-predicted_difference[1080]        0.39    0.89  7338 1.00
-predicted_difference[1081]        0.39    0.89  7338 1.00
-predicted_difference[1082]        0.38    0.89  7327 1.00
-predicted_difference[1083]        0.38    0.89  7327 1.00
-predicted_difference[1084]        0.25    0.45  1801 1.00
-predicted_difference[1085]        0.14    0.35  1593 1.00
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-predicted_difference[1087]        0.22    0.40  2081 1.00
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-predicted_difference[1090]        0.18    0.36  2156 1.00
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-predicted_difference[1108]       -0.37   -0.29  6014 1.00
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-predicted_difference[1110]       -0.18   -0.12  3105 1.00
-predicted_difference[1111]       -0.21   -0.16  6985 1.00
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-predicted_difference[1113]       -0.20   -0.15  6349 1.00
-predicted_difference[1114]       -0.15   -0.11  6918 1.00
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-predicted_difference[1122]        0.18    0.34  2188 1.00
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-predicted_difference[1124]        0.18    0.34  2185 1.00
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-predicted_difference[1189]        0.43    0.98 10180 1.00
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-predicted_difference[1195]        0.41    0.98  9993 1.00
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-predicted_difference[1197]        0.41    0.98  9977 1.00
-predicted_difference[1198]        0.37    0.99  9897 1.00
-predicted_difference[1199]        0.36    0.61  7543 1.00
-predicted_difference[1200]        0.36    0.61  7543 1.00
-predicted_difference[1201]        0.28    0.57  7581 1.00
-predicted_difference[1202]        0.37    0.62  7566 1.00
-predicted_difference[1203]        0.37    0.62  7614 1.00
-predicted_difference[1204]        0.37    0.62  7617 1.00
-predicted_difference[1205]        0.38    0.62  7619 1.00
-predicted_difference[1206]        0.37    0.62  7607 1.00
-predicted_difference[1207]        0.41    0.67  8086 1.00
-predicted_difference[1208]        0.41    0.68  8176 1.00
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-predicted_difference[1212]        0.40    0.96  9731 1.00
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-predicted_difference[1216]        0.35    0.97  9616 1.00
-predicted_difference[1217]        0.27    0.76  7243 1.00
-predicted_difference[1218]        0.27    0.76  7231 1.00
-predicted_difference[1219]        0.29    0.78  7523 1.00
-predicted_difference[1220]        0.28    0.78  7527 1.00
-predicted_difference[1221]        0.28    0.79  7517 1.00
-predicted_difference[1222]       -0.04    0.29  7817 1.00
-predicted_difference[1223]       -0.03    0.26  7864 1.00
-predicted_difference[1224]       -0.03    0.26  7866 1.00
-predicted_difference[1225]       -0.02    0.33  7862 1.00
-predicted_difference[1226]       -0.02    0.33  7862 1.00
-predicted_difference[1227]       -0.02    0.33  7862 1.00
-predicted_difference[1228]       -0.02    0.33  7862 1.00
-predicted_difference[1229]       -0.45    0.40  7659 1.00
-predicted_difference[1230]       -0.48    0.36  7457 1.00
-predicted_difference[1231]       -0.39    0.47  7071 1.00
-predicted_difference[1232]       -0.38    0.49  7104 1.00
-predicted_difference[1233]        0.04    0.18  1393 1.00
-predicted_difference[1234]        0.04    0.18  1393 1.00
-predicted_difference[1235]        0.04    0.17  1390 1.00
-predicted_difference[1236]        0.04    0.17  1390 1.00
-predicted_difference[1237]        0.04    0.17  1388 1.00
-predicted_difference[1238]        0.04    0.17  1388 1.00
-predicted_difference[1239]        0.14    0.26  2608 1.00
-predicted_difference[1240]        0.14    0.26  2608 1.00
-predicted_difference[1241]        0.13    0.26  2627 1.00
-predicted_difference[1242]        0.13    0.26  2627 1.00
-predicted_difference[1243]        0.13    0.25  2640 1.00
-predicted_difference[1244]        0.13    0.25  2640 1.00
-predicted_difference[1245]        0.13    0.25  2637 1.00
-predicted_difference[1246]        0.13    0.25  2637 1.00
-predicted_difference[1247]        0.13    0.25  2641 1.00
-predicted_difference[1248]        0.13    0.25  2641 1.00
-predicted_difference[1249]        0.74    0.89  9495 1.00
-predicted_difference[1250]        0.83    0.94  9600 1.00
-predicted_difference[1251]        0.74    0.89  9495 1.00
-predicted_difference[1252]        0.72    0.85  9465 1.00
-predicted_difference[1253]        0.82    0.92  9585 1.00
-predicted_difference[1254]        0.72    0.85  9465 1.00
-predicted_difference[1255]        0.72    0.85  9474 1.00
-predicted_difference[1256]        0.82    0.92  9589 1.00
-predicted_difference[1257]        0.72    0.85  9474 1.00
-predicted_difference[1258]        0.71    0.85  9486 1.00
-predicted_difference[1259]        0.82    0.92  9594 1.00
-predicted_difference[1260]        0.71    0.85  9486 1.00
-predicted_difference[1261]        0.67    0.82  9385 1.00
-predicted_difference[1262]        0.78    0.90  9521 1.00
-predicted_difference[1263]        0.67    0.82  9385 1.00
-predicted_difference[1264]        0.72    0.86  9571 1.00
-predicted_difference[1265]        0.82    0.92  9653 1.00
-predicted_difference[1266]        0.72    0.86  9571 1.00
-predicted_difference[1267]        0.98    1.00  8413 1.00
-predicted_difference[1268]        0.98    1.00  8410 1.00
-predicted_difference[1269]        0.99    1.00  8395 1.00
-predicted_difference[1270]        0.99    1.00  8388 1.00
-predicted_difference[1271]        0.99    1.00  8234 1.00
-predicted_difference[1272]        0.83    0.94  9682 1.00
-predicted_difference[1273]        0.85    0.95  9701 1.00
-predicted_difference[1274]        0.99    1.00  7970 1.00
-predicted_difference[1275]        0.99    1.00  8003 1.00
-predicted_difference[1276]        0.99    1.00  8004 1.00
-predicted_difference[1277]        0.99    1.00  7962 1.00
-predicted_difference[1278]        0.58    0.95  7146 1.00
-predicted_difference[1279]        0.58    0.95  7146 1.00
-predicted_difference[1280]        0.49    0.96  7331 1.00
-predicted_difference[1281]        0.49    0.96  7331 1.00
-predicted_difference[1282]        1.00    1.00  5491 1.00
-predicted_difference[1283]        1.00    1.00  6282 1.00
-predicted_difference[1284]        1.00    1.00  6364 1.00
-predicted_difference[1285]        1.00    1.00  6382 1.00
-predicted_difference[1286]        1.00    1.00  6130 1.00
-predicted_difference[1287]        1.00    1.00  6123 1.00
-predicted_difference[1288]        1.00    1.00  6127 1.00
-predicted_difference[1289]        1.00    1.00  6163 1.00
-predicted_difference[1290]        1.00    1.00  6406 1.00
-predicted_difference[1291]        1.00    1.00  6408 1.00
-predicted_difference[1292]        1.00    1.00  6418 1.00
-predicted_difference[1293]        0.99    1.00  7914 1.00
-predicted_difference[1294]        0.99    1.00  7923 1.00
-predicted_difference[1295]        0.99    1.00  7862 1.00
-predicted_difference[1296]        0.99    1.00  7878 1.00
-predicted_difference[1297]        0.86    0.96  9510 1.00
-predicted_difference[1298]        0.84    0.94  9444 1.00
-predicted_difference[1299]        0.77    0.91  9152 1.00
-predicted_difference[1300]        0.76    0.90  9162 1.00
-predicted_difference[1301]        0.80    0.93  9379 1.00
-predicted_difference[1302]        0.13    0.64  6030 1.00
-predicted_difference[1303]        0.13    0.64  6030 1.00
-predicted_difference[1304]        0.27    0.69  6343 1.00
-predicted_difference[1305]        0.27    0.69  6343 1.00
-predicted_difference[1306]        0.27    0.68  6343 1.00
-predicted_difference[1307]        0.27    0.68  6343 1.00
-predicted_difference[1308]        0.22    0.72  7116 1.00
-predicted_difference[1309]        0.22    0.72  7116 1.00
-predicted_difference[1310]        0.23    0.72  7022 1.00
-predicted_difference[1311]        0.23    0.72  7022 1.00
-predicted_difference[1312]        0.23    0.74  6925 1.00
-predicted_difference[1313]        0.23    0.74  6925 1.00
-predicted_difference[1314]        0.22    0.74  6922 1.00
-predicted_difference[1315]        0.22    0.74  6922 1.00
-predicted_difference[1316]        0.96    0.99  8915 1.00
-predicted_difference[1317]        0.96    0.99  8761 1.00
-predicted_difference[1318]        0.96    0.99  8742 1.00
-predicted_difference[1319]        0.97    0.99  8708 1.00
-predicted_difference[1320]        0.97    0.99  8641 1.00
-predicted_difference[1321]        0.97    0.99  8627 1.00
-predicted_difference[1322]        0.97    0.99  8612 1.00
-predicted_difference[1323]        0.97    0.99  8609 1.00
-predicted_difference[1324]        0.96    0.99  8915 1.00
-predicted_difference[1325]        0.96    0.99  8761 1.00
-predicted_difference[1326]        0.96    0.99  8742 1.00
-predicted_difference[1327]        0.97    0.99  8708 1.00
-predicted_difference[1328]        0.97    0.99  8640 1.00
-predicted_difference[1329]        0.97    0.99  8627 1.00
-predicted_difference[1330]        0.97    0.99  8609 1.00
-predicted_difference[1331]        0.33    0.75  6936 1.00
-predicted_difference[1332]        0.29    0.78  7544 1.00
-predicted_difference[1333]        0.28    0.79  7511 1.00
-predicted_difference[1334]        0.99    1.00  7939 1.00
-predicted_difference[1335]        0.99    1.00  7942 1.00
-predicted_difference[1336]        0.99    1.00  7913 1.00
-predicted_difference[1337]        0.99    1.00  7915 1.00
-predicted_difference[1338]        0.99    1.00  7917 1.00
-predicted_difference[1339]        0.99    1.00  7920 1.00
-predicted_difference[1340]        0.99    1.00  7921 1.00
-predicted_difference[1341]        0.99    1.00  7921 1.00
-predicted_difference[1342]        0.99    1.00  7925 1.00
-predicted_difference[1343]        0.99    1.00  7896 1.00
-lp__                           -308.95 -262.21   562 1.00
-
-Samples were drawn using NUTS(diag_e) at Sun Apr 21 04:22:05 2024.
-For each parameter, n_eff is a crude measure of effective sample size,
-and Rhat is the potential scale reduction factor on split chains (at 
-convergence, Rhat=1).
-
-
-
-

Investigating parameter distributions

-
-
#g1 <- group_mcmc_areas("beta",beta_list,fit,1)
-
-
-gx <- c()
-
-#grab parameters for every category with more than 8 observations
-for (i in category_count$category_id[category_count$n >= 8]) {
-    print(i)
-    
-    #Print parameter distributions
-    gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
-    ggsave(
-        paste0("./Images/DirectEffects/Parameters/group_",i,"_",gi$name,".png")
-        ,plot=gi$plot
-        )
-    gx <- c(gx,gi)
-
-    #Get Quantiles and means for parameters
-    table <- xtable(gi$quantiles,
-      floating=FALSE
-      ,latex.environments = NULL
-      ,booktabs = TRUE
-      ,zap=getOption("digits")
-      )
-    write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex"))
-}
-
-
[1] 1
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[1] 2
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[1] 5
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[1] 6
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[1] 7
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[1] 11
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[1] 12
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[1] 13
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px <- c()
-
-
-for (i in c(1,2,3,9,10,11,12)) {
-    
-    #Print parameter distributions
-    pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
-    ggsave(
-        paste0("./Images/DirectEffects/Parameters/parameters_",i,"_",pi$name,".png")
-        ,plot=pi$plot
-        )
-    px <- c(px,pi)
-
-    #Get Quantiles and means for parameters
-    table <- xtable(pi$quantiles,
-      floating=FALSE
-      ,latex.environments = NULL
-      ,booktabs = TRUE
-      ,zap=getOption("digits")
-      )
-    write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex"))
-    
-}
-
-
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-
-
-

Note these have 95% outer CI and 80% inner (shaded)

-
1) "Elapsed Duration",
-2) "asinh(Generic Brands)",
-3) "asinh(Competitors USPDC)",
-4) "asinh(High SDI)",
-5) "asinh(High-Medium SDI)",
-6) "asinh(Medium SDI)",
-7) "asinh(Low-Medium SDI)",
-8) "asinh(Low SDI)",
-9) "status_NYR",
-10) "status_EBI",
-11) "status_Rec",
-12) "status_ANR",
-
-
print(px[4]$plot + px[7]$plot)
-
-

-
-
ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png")
-
-
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-
-
-
-
-
-
-

Counterfactuals

-
-
generated_ib <- gqs(
-    fit@stanmodel,
-    data=counterfact_marketing_ib,
-    draws=as.matrix(fit),
-    seed=11021585
-    )
-
-
-
df_ib_p <- data.frame(
-    p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior)
-    ,p_predicted = as.vector(extract(generated_ib, pars="p_predicted")$p_predicted)
-)
-
-df_ib_prior <- data.frame(
-    mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior)
-    ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior)
-)
-
-#p_prior
-ggplot(df_ib_p, aes(x=p_prior)) +
-    geom_density() + 
-    labs(
-        title="Implied Prior Distribution P"
-        ,subtitle=""
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/prior_p.png")
-
-
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-
-
#p_posterior
-ggplot(df_ib_p, aes(x=p_predicted)) +
-    geom_density() + 
-    labs(
-        title="Implied Posterior Distribution P"
-        ,subtitle=""
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/posterior_p.png")
-
-
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-
-
#mu_prior
-ggplot(df_ib_prior) +
-    geom_density(aes(x=mu_prior)) + 
-    labs(
-        title="Prior - Mu"
-        ,subtitle="same prior for all Mu values"
-        ,x="Mu"
-        ,y="Probability"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/prior_mu.png")
-
-
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-
-
#sigma_posterior
-ggplot(df_ib_prior) +
-    geom_density(aes(x=sigma_prior)) + 
-    labs(
-        title="Prior - Sigma"
-        ,subtitle="same prior for all Sigma values"
-        ,x="Sigma"
-        ,y="Probability"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/prior_sigma.png")
-
-
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-
-
-
-
check_hmc_diagnostics(fit)
-
-

-Divergences:
-
-
-
0 of 10000 iterations ended with a divergence.
-
-
-

-Tree depth:
-
-
-
0 of 10000 iterations saturated the maximum tree depth of 10.
-
-
-

-Energy:
-
-
-
E-BFMI indicated possible pathological behavior:
-  Chain 2: E-BFMI = 0.184
-  Chain 4: E-BFMI = 0.192
-E-BFMI below 0.2 indicates you may need to reparameterize your model.
-
-
-
-

Intervention: Alternatives

-
-

Generic Alternative

-
-
counterfact_predicted_ib <- data.frame(
-    p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default)
-    ,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention)
-    ,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference)
-)
-
-
-ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: None"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/default_p_generic_intervention_base.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: Add a single generic competitor"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/default_p_generic_intervention_interv.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of differences 'p'"
-        ,subtitle="Intervention: Add a single generic competitor"
-        ,x="Difference in 'p' under treatment"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/default_p_generic_intervention_distdiff.png")
-
-
Saving 7 x 5 in image
-
-
-
-
pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
-    pivot_longer(X1:X1343)
-
-#TODO: Fix Category names
-pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name))
-pddf_ib["category"] <-  sapply(pddf_ib$entry_idx, function(i) df$category_id[i])
-pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$groups[i])
-
-
-ggplot(pddf_ib, aes(x=value,)) +
-    geom_density(bins=100) +
-    labs(
-        title = "Distribution of predicted differences"
-        ,subtitle = "Intervention: add a single generic competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
-
-
Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`
-
-
-

-
-
ggsave("./Images/DirectEffects/p_generic_intervention_distdiff_styled.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(pddf_ib, aes(x=value,)) +
-    geom_density(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Distribution of predicted differences | By Group"
-        ,subtitle = "Intervention: add a single generic competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-
-
Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`
-
-
-

-
-
ggsave("./Images/DirectEffects/p_generic_intervention_distdiff_by_group.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(pddf_ib, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Histogram of predicted differences | By Group"
-        ,subtitle = "Intervention: add a single generic competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    #xlim(-0.25,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-
-

-
-
ggsave("./Images/DirectEffects/p_generic_intervention_histdiff_by_group.png")
-
-
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-
-
-

Get the probability of increase over probability of a decrease

-
-
mean(counterfact_predicted_ib$predicted_difference)
-
-
[1] 0.1657709
-
-
-

Thus adding a single generic competitor increases the probability of termination by 16.55% on average for the snapshots investigated.

-
-
n = length(counterfact_predicted_ib$p_predicted_intervention)
-mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention)))
-
-
[1] 0.3118685
-
-
mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default)))
-
-
[1] 0.1460717
-
-
-
-
-

USP DC Alternative

-
-
#formulary intervention
-brand_intervention_bnc <- x[c(inherited_cols,"identical_brands")]
-brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand
-
-
-
counterfact_marketing_bnc <- list(
-    D = ncol(x),#
-    N = nrow(x),
-    L = n_categories$count,
-    y = as.vector(y),
-    ll = as.vector(categories),
-    x = as.matrix(x),
-    mu_mean = 0,
-    mu_stdev = 0.05,
-    sigma_shape = 4,
-    sigma_rate = 20,
-    Nx = nrow(x),
-    llx = as.vector(categories),
-    counterfact_x_tilde = as.matrix(brand_intervention_bnc),
-    counterfact_x = as.matrix(x)
-)
-
-
-
generated_bnc <- gqs(
-    fit@stanmodel,
-    data=counterfact_marketing_bnc,
-    draws=as.matrix(fit),
-    seed=11021585
-    )
-
-
-
counterfact_predicted_bnc <- data.frame(
-    p_predicted_default =       as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default)
-    ,p_predicted_intervention = as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention)
-    ,predicted_difference =     as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference)
-)
-
-
-ggplot(counterfact_predicted_bnc, aes(x=p_predicted_default)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: None"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/default_p_uspdc_intervention_base.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(counterfact_predicted_bnc, aes(x=p_predicted_intervention)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: Add a single USP DC competitor"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/default_p_uspdc_intervention_interv.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(counterfact_predicted_bnc, aes(x=predicted_difference)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of differences 'p'"
-        ,subtitle="Intervention: Add a single USP DC competitor"
-        ,x="Difference in 'p' under treatment"
-        ,y="Probability Density"
-    )
-
-

-
-
ggsave("./Images/DirectEffects/default_p_uspdc_intervention_distdiff.png")
-
-
Saving 7 x 5 in image
-
-
-
-
pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |>
-    pivot_longer(X1:X1343)
-
-#Add Category names
-pddf_bnc["entry_idx"] <- as.numeric(gsub("\\D","",pddf_bnc$name))
-pddf_bnc["category"] <-  sapply(pddf_bnc$entry_idx, function(i) df$category_id[i])
-pddf_bnc["category_name"] <- sapply(pddf_bnc$category, function(i) beta_list$groups[i])
-
-
-
-ggplot(pddf_bnc, aes(x=value,)) +
-    geom_density(bins=100) +
-    labs(
-        title = "Distribution of predicted differences"
-        ,subtitle = "Intervention: add a single USP DC competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
-
-
Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`
-
-
-

-
-
ggsave("./Images/DirectEffects/p_uspdc_intervention_distdiff_styled.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(pddf_bnc, aes(x=value,)) +
-    geom_density(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Distribution of predicted differences in 'p' | By Group"
-        ,subtitle = "Intervention: add a single USP DC competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-
-
Warning in geom_density(bins = 100): Ignoring unknown parameters: `bins`
-
-
-

-
-
ggsave("./Images/DirectEffects/p_uspdc_intervention_distdiff_by_group.png")
-
-
Saving 7 x 5 in image
-
-
ggplot(pddf_bnc, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Histogram of predicted differences in 'p' | By Group"
-        ,subtitle = "Intervention: add a single USP DC competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    #xlim(-0.25,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-
-

-
-
ggsave("./Images/DirectEffects/p_uspdc_intervention_histdiff_by_group.png")
-
-
Saving 7 x 5 in image
-
-
-

TODO: add density plot of (x,y,z) (date,value,counts) - with and without faceting

-
-
mean(counterfact_predicted_bnc$predicted_difference)
-
-
[1] 0.1649445
-
-
-

Addin a single USP DC competitor increases/reduces the probability of completion by 16.47% on average for the snapshots of trials that we have.

-
-
-
-

Intervention: Marginal increase in time to finish enrollment

-
-
pddf <- data.frame(extract(generated, pars="predicted_difference")$predicted_difference)  |> pivot_longer(X1:X189)
-pddf["entry_idx"] <- as.numeric(gsub("\\D","",pddf$name))
-
-pddf["category"] <-  sapply(pddf$entry_idx, function(i) counterfact_categories[i])
-pddf["category_name"] <- sapply(pddf$category, function(i) beta_list$groups[i])
- 
-ggplot(pddf, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    labs(
-        title = "Distribution of predicted differences"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    xlim(-0.3,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
-
-ggplot(pddf, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Distribution of predicted differences",
-        subtitle = "By group"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    xlim(-0.25,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-
-

Recall that we had really tight zero priors.

-
-
-
-

Diagnostics

-
-
#trace plots
-plot(fit, pars=c("mu"), plotfun="trace")
-
-
-for (i in 1:4) {
-    print(
-        mcmc_rank_overlay(
-        fit, 
-        pars=c(
-            paste0("mu[",4*i-3,"]"),
-            paste0("mu[",4*i-2,"]"),
-            paste0("mu[",4*i-1,"]"),
-            paste0("mu[",4*i,"]")
-            ), 
-        n_bins=100
-        )+  legend_move("top") +
-             scale_colour_ghibli_d("KikiMedium")
-    )
-}
-
-
-
plot(fit, pars=c("sigma"), plotfun="trace")
-
-for (i in 1:4) {
-    print(
-        mcmc_rank_overlay(
-        fit, 
-        pars=c(
-            paste0("sigma[",4*i-3,"]"),
-            paste0("sigma[",4*i-2,"]"),
-            paste0("sigma[",4*i-1,"]"),
-            paste0("sigma[",4*i,"]")
-            ), 
-        n_bins=100
-        )+  legend_move("top") +
-             scale_colour_ghibli_d("KikiMedium")
-    )
-}
-
-
-
#other diagnostics
-logpost <- log_posterior(fit)
-nuts_prmts <- nuts_params(fit)
-posterior <- as.array(fit)
-
-
-
color_scheme_set("darkgray")
-div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
-mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05)
-
-
-
for (i in 1:4) {
-    mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                mus,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}
-
-
-
mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
-
-
-
for (i in 1:4) {
-    params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                params,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}
-
-
-
for (k in 1:22) {
-for (i in 1:4) {
-    params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                params,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}}
-
-
-
-

TODO

-
    -
  • Double check data flow. (Write summary of this in human readable form) -
      -
    • Is it the data we want from the database -
        -
      • Training
        • -
      • Counterfactual Evaluation -
          -
        • choose a single snapshot per trial.
          • -
        • -
      • -
    • Is the model in STAN well specified.
      • -
    • -
  • work on LOO validation of model
    • -
-
- -
- - -
- - - - \ No newline at end of file diff --git a/r-analysis/EffectsOfMarketConditions.qmd b/r-analysis/EffectsOfMarketConditions.qmd deleted file mode 100644 index 89cade6..0000000 --- a/r-analysis/EffectsOfMarketConditions.qmd +++ /dev/null @@ -1,1111 +0,0 @@ ---- -title: "The Effects of market conditions on recruitment and completion of clinical trials" -author: "Will King" -format: html -editor: source ---- - - -# Setup - -```{r} -library(bayesplot) -available_mcmc(pattern = "_nuts_") -library(ggplot2) -library(patchwork) -library(tidyverse) -library(rstan) -library(tidyr) -library(ghibli) -library(xtable) -#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started - -#save unchanged models instead of recompiling -rstan_options(auto_write = TRUE) -#allow for multithreaded sampling -options(mc.cores = parallel::detectCores()) - -#test installation, shouldn't get any errors -#example(stan_model, package = "rstan", run.dontrun = TRUE) -``` - -```{r} -################ Pull data from database ###################### -library(RPostgreSQL) - -driver <- dbDriver("PostgreSQL") - -get_data <- function(driver) { - -con <- dbConnect( - driver, - user='root', - password='root', - dbname='aact_db', - host='will-office' - ) -on.exit(dbDisconnect(con)) - -query <- dbSendQuery( - con, -# "select * from formatted_data_with_planned_enrollment;" -" -select - fdqpe.nct_id - --,fdqpe.start_date - --,fdqpe.current_enrollment - --,fdqpe.enrollment_category - ,fdqpe.current_status - ,fdqpe.earliest_date_observed - ,fdqpe.elapsed_duration - ,fdqpe.n_brands as identical_brands - ,ntbtu.brand_name_count - ,fdqpe.category_id - ,fdqpe.final_status - ,fdqpe.h_sdi_val - --,fdqpe.h_sdi_u95 - --,fdqpe.h_sdi_l95 - ,fdqpe.hm_sdi_val - --,fdqpe.hm_sdi_u95 - --,fdqpe.hm_sdi_l95 - ,fdqpe.m_sdi_val - --,fdqpe.m_sdi_u95 - --,fdqpe.m_sdi_l95 - ,fdqpe.lm_sdi_val - --,fdqpe.lm_sdi_u95 - --,fdqpe.lm_sdi_l95 - ,fdqpe.l_sdi_val - --,fdqpe.l_sdi_u95 - --,fdqpe.l_sdi_l95 -from formatted_data_with_planned_enrollment fdqpe - join \"Formularies\".nct_to_brands_through_uspdc ntbtu - on fdqpe.nct_id = ntbtu.nct_id -order by fdqpe.nct_id, fdqpe.earliest_date_observed -; -" - ) -df <- fetch(query, n = -1) -df <- na.omit(df) - -query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;") -n_categories <- fetch(query2, n = -1) - -return(list(data=df,ncat=n_categories)) -} - -d <- get_data(driver) -df <- d$data -n_categories <- d$ncat - - - - -################ Format Data ########################### - -data_formatter <- function(df) { -categories <- df["category_id"] - -x <- df["elapsed_duration"] -x["identical_brands"] <- asinh(df$identical_brands) -x["brand_name_counts"] <- asinh(df$brand_name_count) -x["h_sdi_val"] <- asinh(df$h_sdi_val) -x["hm_sdi_val"] <- asinh(df$hm_sdi_val) -x["m_sdi_val"] <- asinh(df$m_sdi_val) -x["lm_sdi_val"] <- asinh(df$lm_sdi_val) -x["l_sdi_val"] <- asinh(df$l_sdi_val) - - -#Setup fixed effects -x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0) -x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0) -x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) -x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0) - - -y <- ifelse(df["final_status"]=="Terminated",1,0) - -#get category list - - -return(list(x=x,y=y)) -} - -train <- data_formatter(df) - -categories <- df$category_id - -x <- train$x -y <- train$y -``` - - - -# Fit Model - - - - -```{r} -################################# FIT MODEL ######################################### -inherited_cols <- c( - "elapsed_duration" - #,"identical_brands" - #,"brand_name_counts" - ,"h_sdi_val" - ,"hm_sdi_val" - ,"m_sdi_val" - ,"lm_sdi_val" - ,"l_sdi_val" - ,"status_NYR" - ,"status_EBI" - ,"status_Rec" - ,"status_ANR" -) -``` - - - -```{r} -beta_list <- list( - groups = c( - `1`="Infections & Parasites", - `2`="Neoplasms", - `3`="Blood & Immune system", - `4`="Endocrine, Nutritional, and Metabolic", - `5`="Mental & Behavioral", - `6`="Nervous System", - `7`="Eye and Adnexa", - `8`="Ear and Mastoid", - `9`="Circulatory", - `10`="Respiratory", - `11`="Digestive", - `12`="Skin & Subcutaneaous tissue", - `13`="Musculoskeletal", - `14`="Genitourinary", - `15`="Pregancy, Childbirth, & Puerperium", - `16`="Perinatal Period", - `17`="Congential", - `18`="Symptoms, Signs etc.", - `19`="Injury etc.", - `20`="External Causes", - `21`="Contact with Healthcare", - `22`="Special Purposes" - ), - parameters = c( - `1`="Elapsed Duration", - # brands - `2`="asinh(Generic Brands)", - `3`="asinh(Competitors USPDC)", - # population - `4`="asinh(High SDI)", - `5`="asinh(High-Medium SDI)", - `6`="asinh(Medium SDI)", - `7`="asinh(Low-Medium SDI)", - `8`="asinh(Low SDI)", - #Status - `9`="status_NYR", - `10`="status_EBI", - `11`="status_Rec", - `12`="status_ANR" - ) -) - -get_parameters <- function(stem,class_list) { - #get categories and lengths - named <- names(class_list) - lengths <- sapply(named, (function (x) length(class_list[[x]]))) - - #describe the grid needed - iter_list <- sapply(named, (function (x) 1:lengths[x])) - - #generate the list of parameters - pardf <- generate_parameter_df(stem, iter_list) - - #add columns with appropriate human-readable names - for (name in named) { - pardf[paste(name,"_hr",sep="")] <- as.factor( - sapply(pardf[name], (function (i) class_list[[name]][i])) - ) - } - - return(pardf) -} - -generate_parameter_df <- function(stem, iter_list) { - grid <- expand.grid(iter_list) - grid["param_name"] <- grid %>% unite(x,colnames(grid),sep=",") - grid["param_name"] <- paste(stem,"[",grid$param_name,"]",sep="") - return(grid) -} - -group_mcmc_areas <- function( - stem,# = "beta" - class_list,# = beta_list - stanfit,# = fit - group_id,# = 2 - rename=TRUE, - filter=NULL - ) { - #get all parameter names - params <- get_parameters(stem,class_list) - - #filter down to parameters of interest - params <- filter(params,groups == group_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr - #get group name for title - group_name <- class_list$groups[group_id] - #create area plot with appropriate title - p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) + - geom_vline(xintercept=0,color="grey",alpha=0.75) - - d <- pivot_longer(filtdata, everything()) |> - group_by(name) |> - summarize( - mean=mean(value) - ,q025 = quantile(value,probs = 0.025) - ,q975 = quantile(value,probs = 0.975) - ,q05 = quantile(value,probs = 0.05) - ,q95 = quantile(value,probs = 0.95) - ) - return(list(plot=p,quantiles=d,name=group_name)) -} - -parameter_mcmc_areas <- function( - stem,# = "beta" - class_list,# = beta_list - stanfit,# = fit - parameter_id,# = 2 - rename=TRUE - ) { - #get all parameter names - params <- get_parameters(stem,class_list) - #filter down to parameters of interest - params <- filter(params,parameters == parameter_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$groups_hr - #get group name for title - parameter_name <- class_list$parameters[parameter_id] - #create area plot with appropriate title - p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(parameter_name,"Parameter Distribution") - - d <- pivot_longer(filtdata, everything()) |> - group_by(name) |> - summarize( - mean=mean(value) - ,q025 = quantile(value,probs = 0.025) - ,q975 = quantile(value,probs = 0.975) - ,q05 = quantile(value,probs = 0.05) - ,q95 = quantile(value,probs = 0.95) - ) - return(list(plot=p,quantiles=d,name=parameter_name)) -} - - -``` - - -```{r} -#generics intervention -brand_intervention_ib <- x[c(inherited_cols,"brand_name_counts")] -brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand -``` - -```{r} -counterfact_marketing_ib <- list( - D = ncol(x),# - N = nrow(x), - L = n_categories$count, - y = as.vector(y), - ll = as.vector(categories), - x = as.matrix(x), - mu_mean = 0, - mu_stdev = 0.05, - sigma_shape = 4, - sigma_rate = 20, - Nx = nrow(x), - llx = as.vector(categories), - counterfact_x_tilde = as.matrix(brand_intervention_ib), - counterfact_x = as.matrix(x) -) -``` - -```{r} -fit <- stan( - file='Hierarchal_Logistic.stan', - data = counterfact_marketing_ib, - chains = 4, - iter = 5000, - seed = 11021585 - ) -``` - - - - - - - - -## Explore data - -```{r} -################################# DATA EXPLORATION ############################ -driver <- dbDriver("PostgreSQL") - -con <- dbConnect( - driver, - user='root', - password='root', - dbname='aact_db', - host='will-office' - ) -#Plot histogram of count of snapshots -df3 <- dbGetQuery( - con, - "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe - group by nct_id,final_status ;" - ) -#df3 <- fetch(query3, n = -1) - -ggplot(data=df3, aes(x=count, fill=final_status)) + - geom_histogram(binwidth=1) + - ggtitle("Histogram of snapshots per trial (matched trials)") + - xlab("Snapshots per trial") -ggsave("./Images/HistSnapshots.png") - -#Plot duration for terminated vs completed -df4 <- dbGetQuery( - con, - " - select - nct_id, - start_date , - primary_completion_date, - overall_status , - primary_completion_date - start_date as duration - from ctgov.studies s - where nct_id in (select distinct nct_id from http.download_status ds) - ;" - ) -#df4 <- fetch(query4, n = -1) - -ggplot(data=df4, aes(x=duration,fill=overall_status)) + - geom_histogram()+ - ggtitle("Histogram of trial durations") + - xlab("duration")+ - facet_wrap(~overall_status) -ggsave("./Images/HistTrialDurations_Faceted.png") - -df5 <- dbGetQuery( - con, - " - with cte1 as ( - select - nct_id, - start_date , - primary_completion_date, - overall_status , - primary_completion_date - start_date as duration - from ctgov.studies s - where nct_id in (select distinct nct_id from http.download_status ds) - ), cte2 as ( - select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe - group by nct_id - ) - select a.nct_id, a.overall_status, a.duration,b.snapshot_count - from cte1 as a - join cte2 as b - on a.nct_id=b.nct_id - ;" - ) -df5$overall_status <- as.factor(df5$overall_status) - -ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) + - geom_jitter() + - ggtitle("Comparison of duration, status, and snapshot_count") + - xlab("duration") + - ylab("snapshot count") -ggsave("./Images/SnapshotsVsDurationVsTermination.png") - -dbDisconnect(con) - -#get number of trials and snapshots in each category -group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max)) -group_trials_by_category <- as.data.frame(group_trials_by_category) - -ggplot(data = group_trials_by_category, aes(x=category_id)) + - geom_bar(binwidth=1,color="black",fill="seagreen") + - scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + - labs( - title="bar chart of trial categories" - ,x="Category ID" - ,y="Count" - ) -ggsave("./Images/CategoryCounts.png") - - - -summary(df5) -``` - - - - - - -```{r} -category_count <- group_trials_by_category |> group_by(category_id) |> count() - -``` - - - - - -## Fit Results - - -```{r} -################################# ANALYZE ##################################### -print(fit) -``` - - - - - - -### Investigating parameter distributions - -```{r} -#g1 <- group_mcmc_areas("beta",beta_list,fit,1) - - -gx <- c() - -#grab parameters for every category with more than 8 observations -for (i in category_count$category_id[category_count$n >= 8]) { - print(i) - - #Print parameter distributions - gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups - ggsave( - paste0("./Images/DirectEffects/Parameters/group_",i,"_",gi$name,".png") - ,plot=gi$plot - ) - gx <- c(gx,gi) - - #Get Quantiles and means for parameters - table <- xtable(gi$quantiles, - floating=FALSE - ,latex.environments = NULL - ,booktabs = TRUE - ,zap=getOption("digits") - ) - write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex")) -} -``` - - - -```{r} -px <- c() - - -for (i in c(1,2,3,9,10,11,12)) { - - #Print parameter distributions - pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups - ggsave( - paste0("./Images/DirectEffects/Parameters/parameters_",i,"_",pi$name,".png") - ,plot=pi$plot - ) - px <- c(px,pi) - - #Get Quantiles and means for parameters - table <- xtable(pi$quantiles, - floating=FALSE - ,latex.environments = NULL - ,booktabs = TRUE - ,zap=getOption("digits") - ) - write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex")) - -} -``` - -Note these have 95% outer CI and 80% inner (shaded) - - - 1) "Elapsed Duration", - 2) "asinh(Generic Brands)", - 3) "asinh(Competitors USPDC)", - 4) "asinh(High SDI)", - 5) "asinh(High-Medium SDI)", - 6) "asinh(Medium SDI)", - 7) "asinh(Low-Medium SDI)", - 8) "asinh(Low SDI)", - 9) "status_NYR", - 10) "status_EBI", - 11) "status_Rec", - 12) "status_ANR", - - - - -```{r} -print(px[4]$plot + px[7]$plot) -ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png") -``` - - - -# Counterfactuals - -```{r} -generated_ib <- gqs( - fit@stanmodel, - data=counterfact_marketing_ib, - draws=as.matrix(fit), - seed=11021585 - ) -``` - -```{r} -df_ib_p <- data.frame( - p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior) - ,p_predicted = as.vector(extract(generated_ib, pars="p_predicted")$p_predicted) -) - -df_ib_prior <- data.frame( - mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior) - ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior) -) - -#p_prior -ggplot(df_ib_p, aes(x=p_prior)) + - geom_density() + - labs( - title="Implied Prior Distribution P" - ,subtitle="" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/prior_p.png") - -#p_posterior -ggplot(df_ib_p, aes(x=p_predicted)) + - geom_density() + - labs( - title="Implied Posterior Distribution P" - ,subtitle="" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/posterior_p.png") - -#mu_prior -ggplot(df_ib_prior) + - geom_density(aes(x=mu_prior)) + - labs( - title="Prior - Mu" - ,subtitle="same prior for all Mu values" - ,x="Mu" - ,y="Probability" - ) -ggsave("./Images/DirectEffects/prior_mu.png") - -#sigma_posterior -ggplot(df_ib_prior) + - geom_density(aes(x=sigma_prior)) + - labs( - title="Prior - Sigma" - ,subtitle="same prior for all Sigma values" - ,x="Sigma" - ,y="Probability" - ) -ggsave("./Images/DirectEffects/prior_sigma.png") -``` - - - -```{r} -check_hmc_diagnostics(fit) -``` - - - - - -### Intervention: Alternatives - -#### Generic Alternative - -```{r} -counterfact_predicted_ib <- data.frame( - p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default) - ,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention) - ,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference) -) - - -ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: None" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_generic_intervention_base.png") - -ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: Add a single generic competitor" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_generic_intervention_interv.png") - -ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) + - geom_density() + - labs( - title="Predicted Distribution of differences 'p'" - ,subtitle="Intervention: Add a single generic competitor" - ,x="Difference in 'p' under treatment" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_generic_intervention_distdiff.png") -``` - - -```{r} - - -pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |> - pivot_longer(X1:X1343) - -#TODO: Fix Category names -pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name)) -pddf_ib["category"] <- sapply(pddf_ib$entry_idx, function(i) df$category_id[i]) -pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$groups[i]) - - -ggplot(pddf_ib, aes(x=value,)) + - geom_density(bins=100) + - labs( - title = "Distribution of predicted differences" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") -ggsave("./Images/DirectEffects/p_generic_intervention_distdiff_styled.png") - -ggplot(pddf_ib, aes(x=value,)) + - geom_density(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Distribution of predicted differences | By Group" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_generic_intervention_distdiff_by_group.png") - -ggplot(pddf_ib, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Histogram of predicted differences | By Group" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - #xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_generic_intervention_histdiff_by_group.png") -``` - -Get the probability of increase over probability of a decrease - -```{r} -mean(counterfact_predicted_ib$predicted_difference) -``` -Thus adding a single generic competitor increases the probability of termination by 16.55% on average for -the snapshots investigated. - - - -```{r} -n = length(counterfact_predicted_ib$p_predicted_intervention) -mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention))) -mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default))) -``` - - -#### USP DC Alternative - -```{r} -#formulary intervention -brand_intervention_bnc <- x[c(inherited_cols,"identical_brands")] -brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand -``` - -```{r} -counterfact_marketing_bnc <- list( - D = ncol(x),# - N = nrow(x), - L = n_categories$count, - y = as.vector(y), - ll = as.vector(categories), - x = as.matrix(x), - mu_mean = 0, - mu_stdev = 0.05, - sigma_shape = 4, - sigma_rate = 20, - Nx = nrow(x), - llx = as.vector(categories), - counterfact_x_tilde = as.matrix(brand_intervention_bnc), - counterfact_x = as.matrix(x) -) -``` - - -```{r} -generated_bnc <- gqs( - fit@stanmodel, - data=counterfact_marketing_bnc, - draws=as.matrix(fit), - seed=11021585 - ) -``` - - -```{r} -counterfact_predicted_bnc <- data.frame( - p_predicted_default = as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default) - ,p_predicted_intervention = as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention) - ,predicted_difference = as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference) -) - - -ggplot(counterfact_predicted_bnc, aes(x=p_predicted_default)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: None" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_uspdc_intervention_base.png") - -ggplot(counterfact_predicted_bnc, aes(x=p_predicted_intervention)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: Add a single USP DC competitor" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_uspdc_intervention_interv.png") - -ggplot(counterfact_predicted_bnc, aes(x=predicted_difference)) + - geom_density() + - labs( - title="Predicted Distribution of differences 'p'" - ,subtitle="Intervention: Add a single USP DC competitor" - ,x="Difference in 'p' under treatment" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_uspdc_intervention_distdiff.png") -``` - - -```{r} -pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |> - pivot_longer(X1:X1343) - -#Add Category names -pddf_bnc["entry_idx"] <- as.numeric(gsub("\\D","",pddf_bnc$name)) -pddf_bnc["category"] <- sapply(pddf_bnc$entry_idx, function(i) df$category_id[i]) -pddf_bnc["category_name"] <- sapply(pddf_bnc$category, function(i) beta_list$groups[i]) - - - -ggplot(pddf_bnc, aes(x=value,)) + - geom_density(bins=100) + - labs( - title = "Distribution of predicted differences" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") -ggsave("./Images/DirectEffects/p_uspdc_intervention_distdiff_styled.png") - -ggplot(pddf_bnc, aes(x=value,)) + - geom_density(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Distribution of predicted differences in 'p' | By Group" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_uspdc_intervention_distdiff_by_group.png") - -ggplot(pddf_bnc, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Histogram of predicted differences in 'p' | By Group" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - #xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_uspdc_intervention_histdiff_by_group.png") -``` - - -TODO: add density plot of (x,y,z) (date,value,counts) - - with and without faceting - - -```{r} -mean(counterfact_predicted_bnc$predicted_difference) -``` -Addin a single USP DC competitor increases/reduces the probability of completion by 16.47% on average -for the snapshots of trials that we have. - - - - -### Intervention: Marginal increase in time to finish enrollment - -```{r} -#| eval: false - -pddf <- data.frame(extract(generated, pars="predicted_difference")$predicted_difference) |> pivot_longer(X1:X189) -pddf["entry_idx"] <- as.numeric(gsub("\\D","",pddf$name)) - -pddf["category"] <- sapply(pddf$entry_idx, function(i) counterfact_categories[i]) -pddf["category_name"] <- sapply(pddf$category, function(i) beta_list$groups[i]) - -ggplot(pddf, aes(x=value,)) + - geom_histogram(bins=100) + - labs( - title = "Distribution of predicted differences" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - xlim(-0.3,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") - -ggplot(pddf, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Distribution of predicted differences", - subtitle = "By group" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) - -``` - - -Recall that we had really tight zero priors. - - - -# Diagnostics - -```{r} -#| eval: false -#trace plots -plot(fit, pars=c("mu"), plotfun="trace") - - -for (i in 1:4) { - print( - mcmc_rank_overlay( - fit, - pars=c( - paste0("mu[",4*i-3,"]"), - paste0("mu[",4*i-2,"]"), - paste0("mu[",4*i-1,"]"), - paste0("mu[",4*i,"]") - ), - n_bins=100 - )+ legend_move("top") + - scale_colour_ghibli_d("KikiMedium") - ) -} -``` - -```{r} -#| eval: false -plot(fit, pars=c("sigma"), plotfun="trace") - -for (i in 1:4) { - print( - mcmc_rank_overlay( - fit, - pars=c( - paste0("sigma[",4*i-3,"]"), - paste0("sigma[",4*i-2,"]"), - paste0("sigma[",4*i-1,"]"), - paste0("sigma[",4*i,"]") - ), - n_bins=100 - )+ legend_move("top") + - scale_colour_ghibli_d("KikiMedium") - ) -} -``` - -```{r} -#| eval: false -#other diagnostics -logpost <- log_posterior(fit) -nuts_prmts <- nuts_params(fit) -posterior <- as.array(fit) - -``` - -```{r} -#| eval: false -color_scheme_set("darkgray") -div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4) -mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05) -``` - -```{r} -#| eval: false -for (i in 1:4) { - mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]")) - print( - mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - mus, - "lp__" - ), - off_diag_args = list(size = 0.75) - ) - ) -} - - - -``` - -```{r} -#| eval: false -mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05) -``` - -```{r} -#| eval: false - -for (i in 1:4) { - params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]")) - print( - mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - params, - "lp__" - ), - off_diag_args = list(size = 0.75) - ) - ) -} -``` - - -```{r} -#| eval: false -for (k in 1:22) { -for (i in 1:4) { - params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]")) - print( - mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - params, - "lp__" - ), - off_diag_args = list(size = 0.75) - ) - ) -}} -``` - - - - - - -# TODO -- [ ] Double check data flow. (Write summary of this in human readable form) - - Is it the data we want from the database - - Training - - Counterfactual Evaluation - - choose a single snapshot per trial. - - Is the model in STAN well specified. -- [ ] work on LOO validation of model diff --git a/r-analysis/EffectsOfMarketConditions_no_enrollment_status.qmd b/r-analysis/EffectsOfMarketConditions_no_enrollment_status.qmd deleted file mode 100644 index 1044951..0000000 --- a/r-analysis/EffectsOfMarketConditions_no_enrollment_status.qmd +++ /dev/null @@ -1,850 +0,0 @@ ---- -title: "The Effects of market conditions on recruitment and completion of clinical trials" -author: "Will King" -format: html -editor: source ---- - - -# Setup - -```{r} -library(bayesplot) -available_mcmc(pattern = "_nuts_") -library(ggplot2) -library(patchwork) -library(tidyverse) -library(rstan) -library(tidyr) -library(ghibli) -#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started - -#save unchanged models instead of recompiling -rstan_options(auto_write = TRUE) -#allow for multithreaded sampling -options(mc.cores = parallel::detectCores()) - -#test installation, shouldn't get any errors -#example(stan_model, package = "rstan", run.dontrun = TRUE) -``` - -```{r} -################ Pull data from database ###################### -library(RPostgreSQL) - -driver <- dbDriver("PostgreSQL") - -get_data <- function(driver) { - -con <- dbConnect( - driver, - user='root', - password='root', - dbname='aact_db', - host='will-office' - ) -on.exit(dbDisconnect(con)) - -query <- dbSendQuery( - con, -# "select * from formatted_data_with_planned_enrollment;" -" -select - fdqpe.nct_id - --,fdqpe.start_date - --,fdqpe.current_enrollment - --,fdqpe.enrollment_category - ,fdqpe.current_status - ,fdqpe.earliest_date_observed - ,fdqpe.elapsed_duration - ,fdqpe.n_brands as identical_brands - ,ntbtu.brand_name_count - ,fdqpe.category_id - ,fdqpe.final_status - ,fdqpe.h_sdi_val - --,fdqpe.h_sdi_u95 - --,fdqpe.h_sdi_l95 - ,fdqpe.hm_sdi_val - --,fdqpe.hm_sdi_u95 - --,fdqpe.hm_sdi_l95 - ,fdqpe.m_sdi_val - --,fdqpe.m_sdi_u95 - --,fdqpe.m_sdi_l95 - ,fdqpe.lm_sdi_val - --,fdqpe.lm_sdi_u95 - --,fdqpe.lm_sdi_l95 - ,fdqpe.l_sdi_val - --,fdqpe.l_sdi_u95 - --,fdqpe.l_sdi_l95 -from formatted_data_with_planned_enrollment fdqpe - join \"Formularies\".nct_to_brands_through_uspdc ntbtu - on fdqpe.nct_id = ntbtu.nct_id -order by fdqpe.nct_id, fdqpe.earliest_date_observed -; -" - ) -df <- fetch(query, n = -1) -df <- na.omit(df) - -query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;") -n_categories <- fetch(query2, n = -1) - -return(list(data=df,ncat=n_categories)) -} - -d <- get_data(driver) -df <- d$data -n_categories <- d$ncat - - - - -################ Format Data ########################### - -data_formatter <- function(df) { - -x <- df["category_id"] -x["identical_brands"] <- asinh(df$identical_brands) -x["brand_name_counts"] <- asinh(df$brand_name_count) -x["h_sdi_val"] <- asinh(df$h_sdi_val) -x["hm_sdi_val"] <- asinh(df$hm_sdi_val) -x["m_sdi_val"] <- asinh(df$m_sdi_val) -x["lm_sdi_val"] <- asinh(df$lm_sdi_val) -x["l_sdi_val"] <- asinh(df$l_sdi_val) - - - -y <- ifelse(df["final_status"]=="Terminated",1,0) - - - -return(list(x=x,y=y)) -} - -train <- data_formatter(df) - -categories <- df$category_id - -x <- train$x -y <- train$y -x$category_id <- NULL -``` - - - -### Intervention: Adding a single competitor -```{r} -inherited_cols <- c( - "identical_brands" - ,"brand_name_counts" - ,"h_sdi_val" - ,"hm_sdi_val" - ,"m_sdi_val" - ,"lm_sdi_val" - ,"l_sdi_val" -) -``` - - - -#### Generics - -```{r} -#generics intervention -brand_intervention_ib <- x[inherited_cols] -brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand -``` - -```{r} -counterfact_marketing_ib <- list( - D = ncol(x),# - N = nrow(x), - L = n_categories$count, - y = as.vector(y), - ll = as.vector(categories), - x = as.matrix(x), - mu_mean = 0, - mu_stdev = 0.05, - sigma_shape = 4, - sigma_rate = 20, - Nx = nrow(x), - llx = as.vector(categories), - counterfact_x_tilde = as.matrix(brand_intervention_ib), - counterfact_x = as.matrix(x) -) -``` - -### USP DC - - -```{r} -#formulary intervention -brand_intervention_bnc <- x[inherited_cols] -brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand -``` - -```{r} -counterfact_marketing_bnc <- list( - D = ncol(x),# - N = nrow(x), - L = n_categories$count, - y = as.vector(y), - ll = as.vector(categories), - x = as.matrix(x), - mu_mean = 0, - mu_stdev = 0.05, - sigma_shape = 4, - sigma_rate = 20, - Nx = nrow(x), - llx = as.vector(categories), - counterfact_x_tilde = as.matrix(brand_intervention_bnc), - counterfact_x = as.matrix(x) -) -``` - - -# Fit Model - - -```{r} -################################# FIT MODEL ######################################### - - - -fit <- stan( - file='Hierarchal_Logistic.stan', - data = counterfact_marketing_ib, - chains = 4, - iter = 5000, - seed = 11021585 - ) -``` - - -```{r} -generated_bi <- gqs( - fit@stanmodel, - data=counterfact_marketing_ib, - draws=as.matrix(fit), - seed=11021585 - ) -``` - -## Priors - -```{r} -#| eval: false -hist(as.vector(extract(generated_bi, pars="p_prior")$p_prior)) -hist(as.vector(extract(generated_bi, pars="mu_prior")$mu_prior), ) -hist(as.vector(extract(generated_bi, pars="sigma_prior")$sigma_prior)) -``` - -```{r} -df_ib_p <- data.frame( - p_prior=as.vector(extract(generated_bi, pars="p_prior")$p_prior) - ,p_predicted = as.vector(extract(generated_bi, pars="p_predicted")$p_predicted) -) - -df_ib_prior <- data.frame( - mu_prior = as.vector(extract(generated_bi, pars="mu_prior")$mu_prior) - ,sigma_prior = as.vector(extract(generated_bi, pars="sigma_prior")$sigma_prior) -) - -#p_prior -ggplot(df_ib_p, aes(x=p_prior)) + - geom_density() + - labs( - title="Implied Prior Distribution P" - ,subtitle="" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/TotalEffects/prior_p.png") - -#p_posterior -ggplot(df_ib_p, aes(x=p_predicted)) + - geom_density() + - labs( - title="Implied Posterior Distribution P" - ,subtitle="" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/TotalEffects/posterior_p.png") - -#mu_prior -ggplot(df_ib_prior) + - geom_density(aes(x=mu_prior)) + - labs( - title="Prior - Mu" - ,subtitle="same prior for all Mu values" - ,x="Mu" - ,y="Probability" - ) -ggsave("./Images/TotalEffects/prior_mu.png") - -#sigma_posterior -ggplot(df_ib_prior) + - geom_density(aes(x=sigma_prior)) + - labs( - title="Prior - Sigma" - ,subtitle="same prior for all Sigma values" - ,x="Sigma" - ,y="Probability" - ) -ggsave("./Images/TotalEffects/prior_sigma.png") -``` - - - -```{r} -check_hmc_diagnostics(fit) -#hist(as.vector(extract(generated_bi, pars="p_predicted")$p_predicted)) -``` - - - - - - -# Diagnostics - -```{r} -#| eval: false -#trace plots -plot(fit, pars=c("mu"), plotfun="trace") - - -mcmc_rank_overlay( -fit, -pars=sapply(1:7, function(i) paste0("mu[",i,"]")) -,n_bins=100 -)+ legend_move("top") + - scale_colour_ghibli_d("KikiMedium") - -``` - -```{r} -#| eval: false -plot(fit, pars=c("sigma"), plotfun="trace") - - -mcmc_rank_overlay( -fit, -pars=sapply(1:7, function(i) paste0("sigma[",i,"]")) -,n_bins=100 -)+ legend_move("top") + - scale_colour_ghibli_d("KikiMedium") -``` - -```{r} -#| eval: false -#other diagnostics -logpost <- log_posterior(fit) -nuts_prmts <- nuts_params(fit) -posterior <- as.array(fit) - -``` - -```{r} -#| eval: false -color_scheme_set("darkgray") -div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4) -mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05) -``` - -```{r} -#| eval: false -mus = sapply(1:7, function(j) paste0("mu[",j ,"]")) -mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - mus, - "lp__" - ), - off_diag_args = list(size = 0.75) -) - - - -``` - -```{r} -#| eval: false -mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05) -``` - -```{r} -#| eval: false -sigmas = sapply(1:7, function(j) paste0("sigma[",j ,"]")) -mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - sigmas, - "lp__" - ), - off_diag_args = list(size = 0.75) -) -``` - - -```{r} -#| eval: false -#for (k in 1:22) { -# params = sapply(1:7, function(j) paste0("beta[",k,",",j ,"]")) -# print( -# mcmc_pairs( -# posterior, -# np = nuts_prmts, -# pars=c( -# params, -# "lp__" -# ), -# off_diag_args = list(size = 0.75) -# ) -# ) -#} -``` - - -# Results - - -```{r} -################################# ANALYZE ##################################### -print(fit) -``` - -## Result Plots - - -Note the regular large difference in variance. -I would guess those are the beta[1:22,2] values. -I wonder if a lot of the variance is due to the 2 values that are sitting out. - - - -```{r} -beta_list <- list( - groups = c( - `1`="Infections & Parasites", - `2`="Neoplasms", - `3`="Blood & Immune system", - `4`="Endocrine, Nutritional, and Metabolic", - `5`="Mental & Behavioral", - `6`="Nervous System", - `7`="Eye and Adnexa", - `8`="Ear and Mastoid", - `9`="Circulatory", - `10`="Respiratory", - `11`="Digestive", - `12`="Skin & Subcutaneaous tissue", - `13`="Musculoskeletal", - `14`="Genitourinary", - `15`="Pregancy, Childbirth, & Puerperium", - `16`="Perinatal Period", - `17`="Congential", - `18`="Symptoms, Signs etc.", - `19`="Injury etc.", - `20`="External Causes", - `21`="Contact with Healthcare", - `22`="Special Purposes" - ), - parameters = c( - # brands - `1`="asinh(Generic Brands)", - `2`="asinh(Competitors USPDC)", - # population - `3`="asinh(High SDI)", - `4`="asinh(High-Medium SDI)", - `5`="asinh(Medium SDI)", - `6`="asinh(Low-Medium SDI)", - `7`="asinh(Low SDI)" - ) -) - -get_parameters <- function(stem,class_list) { - #get categories and lengths - named <- names(class_list) - lengths <- sapply(named, (function (x) length(class_list[[x]]))) - - #describe the grid needed - iter_list <- sapply(named, (function (x) 1:lengths[x])) - - #generate the list of parameters - pardf <- generate_parameter_df(stem, iter_list) - - #add columns with appropriate human-readable names - for (name in named) { - pardf[paste(name,"_hr",sep="")] <- as.factor( - sapply(pardf[name], (function (i) class_list[[name]][i])) - ) - } - - return(pardf) -} - -generate_parameter_df <- function(stem, iter_list) { - grid <- expand.grid(iter_list) - grid["param_name"] <- grid %>% unite(x,colnames(grid),sep=",") - grid["param_name"] <- paste(stem,"[",grid$param_name,"]",sep="") - return(grid) -} - -group_mcmc_areas <- function( - stem,# = "beta" - class_list,# = beta_list - stanfit,# = fit - group_id,# = 2 - rename=TRUE - ) { - #get all parameter names - params <- get_parameters(stem,class_list) - #filter down to parameters of interest - params <- filter(params,groups == group_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr - #get group name for title - group_name <- class_list$groups[group_id] - #create area plot with appropriate title - mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) -} - -parameter_mcmc_areas <- function( - stem,# = "beta" - class_list,# = beta_list - stanfit,# = fit - parameter_id,# = 2 - rename=TRUE - ) { - #get all parameter names - params <- get_parameters(stem,class_list) - #filter down to parameters of interest - params <- filter(params,parameters == parameter_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$groups_hr - #get group name for title - parameter_name <- class_list$parameters[parameter_id] - #create area plot with appropriate title - mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(parameter_name,"Parameter Distribution") -} - - -``` - -```{r} -#mcmc_intervals(fit, pars=get_parameters("beta",beta_list)$param_name) -``` - -### Investigating parameter distributions - -```{r} -#g1 <- group_mcmc_areas("beta",beta_list,fit,2) -#g2 <- group_mcmc_areas("beta",beta_list,fit,2) -#g3 <- group_mcmc_areas("beta",beta_list,fit,2) -#g4 <- group_mcmc_areas("beta",beta_list,fit,2) -#g5 <- group_mcmc_areas("beta",beta_list,fit,2) -#g6 <- group_mcmc_areas("beta",beta_list,fit,2) -#g7 <- group_mcmc_areas("beta",beta_list,fit,2) -#g8 <- group_mcmc_areas("beta",beta_list,fit,2) -#g9 <- group_mcmc_areas("beta",beta_list,fit,2) -#g10 <- group_mcmc_areas("beta",beta_list,fit,2) -#g11 <- group_mcmc_areas("beta",beta_list,fit,2) -#g12 <- group_mcmc_areas("beta",beta_list,fit,2) -#g13 <- group_mcmc_areas("beta",beta_list,fit,2) -#g14 <- group_mcmc_areas("beta",beta_list,fit,2) -#g15 <- group_mcmc_areas("beta",beta_list,fit,2) -#g16 <- group_mcmc_areas("beta",beta_list,fit,2) -#g17 <- group_mcmc_areas("beta",beta_list,fit,2) -#g18 <- group_mcmc_areas("beta",beta_list,fit,2) -#g19 <- group_mcmc_areas("beta",beta_list,fit,2) -#g20 <- group_mcmc_areas("beta",beta_list,fit,2) -#g21 <- group_mcmc_areas("beta",beta_list,fit,2) -#g22 <- group_mcmc_areas("beta",beta_list,fit,2) - - -p1 <- parameter_mcmc_areas("beta",beta_list,fit,1) -ggsave("./Images/TotalEffects/Parameters/01_generics.png") -p2 <- parameter_mcmc_areas("beta",beta_list,fit,2) -ggsave("./Images/TotalEffects/Parameters/02_uspdc.png") -#p3 <- parameter_mcmc_areas("beta",beta_list,fit,3) -#p4 <- parameter_mcmc_areas("beta",beta_list,fit,4) -#p5 <- parameter_mcmc_areas("beta",beta_list,fit,5) -#p6 <- parameter_mcmc_areas("beta",beta_list,fit,6) -#p7 <- parameter_mcmc_areas("beta",beta_list,fit,7) - -``` - -Note these have 95% outer CI and 80% inner (shaded) - - - 1) "asinh(Generic Brands)", - 2) "asinh(Competitors USPDC)", - 3) "asinh(High SDI)", - 4) "asinh(High-Medium SDI)", - 5) "asinh(Medium SDI)", - 6) "asinh(Low-Medium SDI)", - 7) "asinh(Low SDI)", - -of interest -- p1 + p2 - - -```{r} -p1 + p2 -ggsave("./Images/TotalEffects/Parameters/1&2_generics_and_uspdc.png") -``` - - - -# Posterior Prediction - - - - -## Distribution of Predicted Differences - - - -### Intervention: Adding a single competitor - - -#### Generics - - - - - -```{r} -#| eval: false -#TODO: Convert to ggplot, stabilize y axis -hist(as.vector(extract(generated_bi, pars="p_predicted_default")$p_predicted_default)) -hist(as.vector(extract(generated_bi, pars="p_predicted_intervention")$p_predicted_intervention)) -hist(as.vector(extract(generated_bi, pars="predicted_difference")$predicted_difference)) -``` - - -```{r} -counterfact_predicted_ib <- data.frame( - p_predicted_default = as.vector(extract(generated_bi, pars="p_predicted_default")$p_predicted_default) - ,p_predicted_intervention = as.vector(extract(generated_bi, pars="p_predicted_intervention")$p_predicted_intervention) - ,predicted_difference = as.vector(extract(generated_bi, pars="predicted_difference")$predicted_difference) -) -``` - - -```{r} -ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: None" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/TotalEffects/default_p_generic_intervention_base.png") - -ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: Add a single generic competitor" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/TotalEffects/default_p_generic_intervention_interv.png") - -ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) + - geom_density() + - labs( - title="Predicted Distribution of differences 'p'" - ,subtitle="Intervention: Add a single generic competitor" - ,x="Difference in 'p' under treatment" - ,y="Probability Density" - ) -ggsave("./Images/TotalEffects/default_p_generic_intervention_distdiff.png") -``` - - -```{r} -pddf_ib <- data.frame(extract(generated_bi, pars="predicted_difference")$predicted_difference) |> - pivot_longer(X1:X1343) - -#TODO: Fix Category names -pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name)) -pddf_ib["category"] <- sapply(pddf_ib$entry_idx, function(i) df$category_id[i]) -pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$groups[i]) -``` - - -```{r} -ggplot(pddf_ib, aes(x=value,)) + - geom_density() + - labs( - title = "Distribution of predicted differences" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") -ggsave("./Images/TotalEffects/p_generic_intervention_distdiff_styled.png") - -ggplot(pddf_ib, aes(x=value,)) + - geom_density() + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5 - ,scales="free" - ) + - xlim(-1,1)+ - labs( - title = "Distribution of predicted differences | By Group" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/TotalEffects/p_generic_intervention_distdiff_by_group.png") - -ggplot(pddf_ib, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Histogram of predicted differences | By Group" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - #xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/TotalEffects/p_generic_intervention_histdiff_by_group.png") -``` - - - - - -#### USP DC - - -```{r} -generated_bnc <- gqs( - fit@stanmodel, - data=counterfact_marketing_bnc, - draws=as.matrix(fit), - seed=11021585 - ) -``` - -```{r} -#| eval: false -#TODO: Convert to ggplot, stabilize y axis -hist(as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default), bins=100) -hist(as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention), bins=100) -hist(as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference), bins=100) -``` - -```{r} -counterfact_predicted_bnc <- data.frame( - p_predicted_default = as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default) - ,p_predicted_intervention = as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention) - ,predicted_difference = as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference) -) -``` - - - - - - - - - -```{r} -pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |> - pivot_longer(X1:X1343) - -#TODO: Fix Category names -pddf_bnc["entry_idx"] <- as.numeric(gsub("\\D","",pddf_bnc$name)) -pddf_bnc["category"] <- sapply(pddf_bnc$entry_idx, function(i) df$category_id[i]) -pddf_bnc["category_name"] <- sapply(pddf_bnc$category, function(i) beta_list$groups[i]) -``` - - -```{r} -ggplot(pddf_bnc, aes(x=value,)) + - geom_density() + - labs( - title = "Distribution of predicted differences" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") -ggsave("./Images/TotalEffects/p_uspdc_intervention_distdiff_styled.png") - -ggplot(pddf_bnc, aes(x=value,)) + - geom_density() + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5 - ,scales="free" - ) + - labs( - title = "Distribution of predicted differences | By Group" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/TotalEffects/p_uspdc_intervention_distdiff_by_group.png") - -ggplot(pddf_bnc, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Histogram of predicted differences | By Group" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - #xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/TotalEffects/p_uspdc_intervention_histdiff_by_group.png") -``` - - - - diff --git a/r-analysis/EffectsOfMarketConditions_no_pop.qmd b/r-analysis/EffectsOfMarketConditions_no_pop.qmd deleted file mode 100644 index 59d4b81..0000000 --- a/r-analysis/EffectsOfMarketConditions_no_pop.qmd +++ /dev/null @@ -1,1113 +0,0 @@ ---- -title: "The Effects of market conditions on recruitment and completion of clinical trials" -author: "Will King" -format: html -editor: source ---- - -IMPORTANT: Not setup yet - - -# Setup - -```{r} -library(bayesplot) -available_mcmc(pattern = "_nuts_") -library(ggplot2) -library(patchwork) -library(tidyverse) -library(rstan) -library(tidyr) -library(ghibli) -library(xtable) -#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started - -#save unchanged models instead of recompiling -rstan_options(auto_write = TRUE) -#allow for multithreaded sampling -options(mc.cores = parallel::detectCores()) - -#test installation, shouldn't get any errors -#example(stan_model, package = "rstan", run.dontrun = TRUE) -``` - -```{r} -################ Pull data from database ###################### -library(RPostgreSQL) - -driver <- dbDriver("PostgreSQL") - -get_data <- function(driver) { - -con <- dbConnect( - driver, - user='root', - password='root', - dbname='aact_db', - host='will-office' - ) -on.exit(dbDisconnect(con)) - -query <- dbSendQuery( - con, -# "select * from formatted_data_with_planned_enrollment;" -" -select - fdqpe.nct_id - --,fdqpe.start_date - --,fdqpe.current_enrollment - --,fdqpe.enrollment_category - ,fdqpe.current_status - ,fdqpe.earliest_date_observed - ,fdqpe.elapsed_duration - ,fdqpe.n_brands as identical_brands - ,ntbtu.brand_name_count - ,fdqpe.category_id - ,fdqpe.final_status - ,fdqpe.h_sdi_val - --,fdqpe.h_sdi_u95 - --,fdqpe.h_sdi_l95 - ,fdqpe.hm_sdi_val - --,fdqpe.hm_sdi_u95 - --,fdqpe.hm_sdi_l95 - ,fdqpe.m_sdi_val - --,fdqpe.m_sdi_u95 - --,fdqpe.m_sdi_l95 - ,fdqpe.lm_sdi_val - --,fdqpe.lm_sdi_u95 - --,fdqpe.lm_sdi_l95 - ,fdqpe.l_sdi_val - --,fdqpe.l_sdi_u95 - --,fdqpe.l_sdi_l95 -from formatted_data_with_planned_enrollment fdqpe - join \"Formularies\".nct_to_brands_through_uspdc ntbtu - on fdqpe.nct_id = ntbtu.nct_id -order by fdqpe.nct_id, fdqpe.earliest_date_observed -; -" - ) -df <- fetch(query, n = -1) -df <- na.omit(df) - -query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;") -n_categories <- fetch(query2, n = -1) - -return(list(data=df,ncat=n_categories)) -} - -d <- get_data(driver) -df <- d$data -n_categories <- d$ncat - - - - -################ Format Data ########################### - -data_formatter <- function(df) { -categories <- df["category_id"] - -x <- df["elapsed_duration"] -x["identical_brands"] <- asinh(df$identical_brands) -x["brand_name_counts"] <- asinh(df$brand_name_count) -x["h_sdi_val"] <- asinh(df$h_sdi_val) -x["hm_sdi_val"] <- asinh(df$hm_sdi_val) -x["m_sdi_val"] <- asinh(df$m_sdi_val) -x["lm_sdi_val"] <- asinh(df$lm_sdi_val) -x["l_sdi_val"] <- asinh(df$l_sdi_val) - - -#Setup fixed effects -x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0) -x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0) -x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) -x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0) - - -y <- ifelse(df["final_status"]=="Terminated",1,0) - -#get category list - - -return(list(x=x,y=y)) -} - -train <- data_formatter(df) - -categories <- df$category_id - -x <- train$x -y <- train$y -``` - - - -# Fit Model - - - - -```{r} -################################# FIT MODEL ######################################### -inherited_cols <- c( - "elapsed_duration" - #,"identical_brands" - #,"brand_name_counts" - ,"h_sdi_val" - ,"hm_sdi_val" - ,"m_sdi_val" - ,"lm_sdi_val" - ,"l_sdi_val" - ,"status_NYR" - ,"status_EBI" - ,"status_Rec" - ,"status_ANR" -) -``` - - - -```{r} -beta_list <- list( - groups = c( - `1`="Infections & Parasites", - `2`="Neoplasms", - `3`="Blood & Immune system", - `4`="Endocrine, Nutritional, and Metabolic", - `5`="Mental & Behavioral", - `6`="Nervous System", - `7`="Eye and Adnexa", - `8`="Ear and Mastoid", - `9`="Circulatory", - `10`="Respiratory", - `11`="Digestive", - `12`="Skin & Subcutaneaous tissue", - `13`="Musculoskeletal", - `14`="Genitourinary", - `15`="Pregancy, Childbirth, & Puerperium", - `16`="Perinatal Period", - `17`="Congential", - `18`="Symptoms, Signs etc.", - `19`="Injury etc.", - `20`="External Causes", - `21`="Contact with Healthcare", - `22`="Special Purposes" - ), - parameters = c( - `1`="Elapsed Duration", - # brands - `2`="asinh(Generic Brands)", - `3`="asinh(Competitors USPDC)", - # population - `4`="asinh(High SDI)", - `5`="asinh(High-Medium SDI)", - `6`="asinh(Medium SDI)", - `7`="asinh(Low-Medium SDI)", - `8`="asinh(Low SDI)", - #Status - `9`="status_NYR", - `10`="status_EBI", - `11`="status_Rec", - `12`="status_ANR" - ) -) - -get_parameters <- function(stem,class_list) { - #get categories and lengths - named <- names(class_list) - lengths <- sapply(named, (function (x) length(class_list[[x]]))) - - #describe the grid needed - iter_list <- sapply(named, (function (x) 1:lengths[x])) - - #generate the list of parameters - pardf <- generate_parameter_df(stem, iter_list) - - #add columns with appropriate human-readable names - for (name in named) { - pardf[paste(name,"_hr",sep="")] <- as.factor( - sapply(pardf[name], (function (i) class_list[[name]][i])) - ) - } - - return(pardf) -} - -generate_parameter_df <- function(stem, iter_list) { - grid <- expand.grid(iter_list) - grid["param_name"] <- grid %>% unite(x,colnames(grid),sep=",") - grid["param_name"] <- paste(stem,"[",grid$param_name,"]",sep="") - return(grid) -} - -group_mcmc_areas <- function( - stem,# = "beta" - class_list,# = beta_list - stanfit,# = fit - group_id,# = 2 - rename=TRUE, - filter=NULL - ) { - #get all parameter names - params <- get_parameters(stem,class_list) - - #filter down to parameters of interest - params <- filter(params,groups == group_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr - #get group name for title - group_name <- class_list$groups[group_id] - #create area plot with appropriate title - p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) + - geom_vline(xintercept=0,color="grey",alpha=0.75) - - d <- pivot_longer(filtdata, everything()) |> - group_by(name) |> - summarize( - mean=mean(value) - ,q025 = quantile(value,probs = 0.025) - ,q975 = quantile(value,probs = 0.975) - ,q05 = quantile(value,probs = 0.05) - ,q95 = quantile(value,probs = 0.95) - ) - return(list(plot=p,quantiles=d,name=group_name)) -} - -parameter_mcmc_areas <- function( - stem,# = "beta" - class_list,# = beta_list - stanfit,# = fit - parameter_id,# = 2 - rename=TRUE - ) { - #get all parameter names - params <- get_parameters(stem,class_list) - #filter down to parameters of interest - params <- filter(params,parameters == parameter_id) - #Get dataframe with only the rows of interest - filtdata <- as.data.frame(stanfit)[params$param_name] - #rename columns - if (rename) dimnames(filtdata)[[2]] <- params$groups_hr - #get group name for title - parameter_name <- class_list$parameters[parameter_id] - #create area plot with appropriate title - p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) + - ggtitle(parameter_name,"Parameter Distribution") - - d <- pivot_longer(filtdata, everything()) |> - group_by(name) |> - summarize( - mean=mean(value) - ,q025 = quantile(value,probs = 0.025) - ,q975 = quantile(value,probs = 0.975) - ,q05 = quantile(value,probs = 0.05) - ,q95 = quantile(value,probs = 0.95) - ) - return(list(plot=p,quantiles=d,name=parameter_name)) -} - - -``` - - -```{r} -#generics intervention -brand_intervention_ib <- x[c(inherited_cols,"brand_name_counts")] -brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand -``` - -```{r} -counterfact_marketing_ib <- list( - D = ncol(x),# - N = nrow(x), - L = n_categories$count, - y = as.vector(y), - ll = as.vector(categories), - x = as.matrix(x), - mu_mean = 0, - mu_stdev = 0.05, - sigma_shape = 4, - sigma_rate = 20, - Nx = nrow(x), - llx = as.vector(categories), - counterfact_x_tilde = as.matrix(brand_intervention_ib), - counterfact_x = as.matrix(x) -) -``` - -```{r} -fit <- stan( - file='Hierarchal_Logistic.stan', - data = counterfact_marketing_ib, - chains = 4, - iter = 5000, - seed = 11021585 - ) -``` - - - - - - - - -## Explore data - -```{r} -################################# DATA EXPLORATION ############################ -driver <- dbDriver("PostgreSQL") - -con <- dbConnect( - driver, - user='root', - password='root', - dbname='aact_db', - host='will-office' - ) -#Plot histogram of count of snapshots -df3 <- dbGetQuery( - con, - "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe - group by nct_id,final_status ;" - ) -#df3 <- fetch(query3, n = -1) - -ggplot(data=df3, aes(x=count, fill=final_status)) + - geom_histogram(binwidth=1) + - ggtitle("Histogram of snapshots per trial (matched trials)") + - xlab("Snapshots per trial") -ggsave("./Images/HistSnapshots.png") - -#Plot duration for terminated vs completed -df4 <- dbGetQuery( - con, - " - select - nct_id, - start_date , - primary_completion_date, - overall_status , - primary_completion_date - start_date as duration - from ctgov.studies s - where nct_id in (select distinct nct_id from http.download_status ds) - ;" - ) -#df4 <- fetch(query4, n = -1) - -ggplot(data=df4, aes(x=duration,fill=overall_status)) + - geom_histogram()+ - ggtitle("Histogram of trial durations") + - xlab("duration")+ - facet_wrap(~overall_status) -ggsave("./Images/HistTrialDurations_Faceted.png") - -df5 <- dbGetQuery( - con, - " - with cte1 as ( - select - nct_id, - start_date , - primary_completion_date, - overall_status , - primary_completion_date - start_date as duration - from ctgov.studies s - where nct_id in (select distinct nct_id from http.download_status ds) - ), cte2 as ( - select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe - group by nct_id - ) - select a.nct_id, a.overall_status, a.duration,b.snapshot_count - from cte1 as a - join cte2 as b - on a.nct_id=b.nct_id - ;" - ) -df5$overall_status <- as.factor(df5$overall_status) - -ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) + - geom_jitter() + - ggtitle("Comparison of duration, status, and snapshot_count") + - xlab("duration") + - ylab("snapshot count") -ggsave("./Images/SnapshotsVsDurationVsTermination.png") - -dbDisconnect(con) - -#get number of trials and snapshots in each category -group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max)) -group_trials_by_category <- as.data.frame(group_trials_by_category) - -ggplot(data = group_trials_by_category, aes(x=category_id)) + - geom_bar(binwidth=1,color="black",fill="seagreen") + - scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + - labs( - title="bar chart of trial categories" - ,x="Category ID" - ,y="Count" - ) -ggsave("./Images/CategoryCounts.png") - - - -summary(df5) -``` - - - - - - -```{r} -category_count <- group_trials_by_category |> group_by(category_id) |> count() - -``` - - - - - -## Fit Results - - -```{r} -################################# ANALYZE ##################################### -print(fit) -``` - - - - - - -### Investigating parameter distributions - -```{r} -#g1 <- group_mcmc_areas("beta",beta_list,fit,1) - - -gx <- c() - -#grab parameters for every category with more than 8 observations -for (i in category_count$category_id[category_count$n >= 8]) { - print(i) - - #Print parameter distributions - gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups - ggsave( - paste0("./Images/DirectEffects/Parameters/group_",i,"_",gi$name,".png") - ,plot=gi$plot - ) - gx <- c(gx,gi) - - #Get Quantiles and means for parameters - table <- xtable(gi$quantiles, - floating=FALSE - ,latex.environments = NULL - ,booktabs = TRUE - ,zap=getOption("digits") - ) - write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex")) -} -``` - - - -```{r} -px <- c() - - -for (i in c(1,2,3,9,10,11,12)) { - - #Print parameter distributions - pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups - ggsave( - paste0("./Images/DirectEffects/Parameters/parameters_",i,"_",pi$name,".png") - ,plot=pi$plot - ) - px <- c(px,pi) - - #Get Quantiles and means for parameters - table <- xtable(pi$quantiles, - floating=FALSE - ,latex.environments = NULL - ,booktabs = TRUE - ,zap=getOption("digits") - ) - write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex")) - -} -``` - -Note these have 95% outer CI and 80% inner (shaded) - - - 1) "Elapsed Duration", - 2) "asinh(Generic Brands)", - 3) "asinh(Competitors USPDC)", - 4) "asinh(High SDI)", - 5) "asinh(High-Medium SDI)", - 6) "asinh(Medium SDI)", - 7) "asinh(Low-Medium SDI)", - 8) "asinh(Low SDI)", - 9) "status_NYR", - 10) "status_EBI", - 11) "status_Rec", - 12) "status_ANR", - - - - -```{r} -print(px[4]$plot + px[7]$plot) -ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png") -``` - - - -# Counterfactuals - -```{r} -generated_ib <- gqs( - fit@stanmodel, - data=counterfact_marketing_ib, - draws=as.matrix(fit), - seed=11021585 - ) -``` - -```{r} -df_ib_p <- data.frame( - p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior) - ,p_predicted = as.vector(extract(generated_ib, pars="p_predicted")$p_predicted) -) - -df_ib_prior <- data.frame( - mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior) - ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior) -) - -#p_prior -ggplot(df_ib_p, aes(x=p_prior)) + - geom_density() + - labs( - title="Implied Prior Distribution P" - ,subtitle="" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/prior_p.png") - -#p_posterior -ggplot(df_ib_p, aes(x=p_predicted)) + - geom_density() + - labs( - title="Implied Posterior Distribution P" - ,subtitle="" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/posterior_p.png") - -#mu_prior -ggplot(df_ib_prior) + - geom_density(aes(x=mu_prior)) + - labs( - title="Prior - Mu" - ,subtitle="same prior for all Mu values" - ,x="Mu" - ,y="Probability" - ) -ggsave("./Images/DirectEffects/prior_mu.png") - -#sigma_posterior -ggplot(df_ib_prior) + - geom_density(aes(x=sigma_prior)) + - labs( - title="Prior - Sigma" - ,subtitle="same prior for all Sigma values" - ,x="Sigma" - ,y="Probability" - ) -ggsave("./Images/DirectEffects/prior_sigma.png") -``` - - - -```{r} -check_hmc_diagnostics(fit) -``` - - - - - -### Intervention: Alternatives - -#### Generic Alternative - -```{r} -counterfact_predicted_ib <- data.frame( - p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default) - ,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention) - ,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference) -) - - -ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: None" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_generic_intervention_base.png") - -ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: Add a single generic competitor" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_generic_intervention_interv.png") - -ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) + - geom_density() + - labs( - title="Predicted Distribution of differences 'p'" - ,subtitle="Intervention: Add a single generic competitor" - ,x="Difference in 'p' under treatment" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_generic_intervention_distdiff.png") -``` - - -```{r} - - -pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |> - pivot_longer(X1:X1343) - -#TODO: Fix Category names -pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name)) -pddf_ib["category"] <- sapply(pddf_ib$entry_idx, function(i) df$category_id[i]) -pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$groups[i]) - - -ggplot(pddf_ib, aes(x=value,)) + - geom_density(bins=100) + - labs( - title = "Distribution of predicted differences" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") -ggsave("./Images/DirectEffects/p_generic_intervention_distdiff_styled.png") - -ggplot(pddf_ib, aes(x=value,)) + - geom_density(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Distribution of predicted differences | By Group" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_generic_intervention_distdiff_by_group.png") - -ggplot(pddf_ib, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Histogram of predicted differences | By Group" - ,subtitle = "Intervention: add a single generic competitor" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - #xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_generic_intervention_histdiff_by_group.png") -``` - -Get the probability of increase over probability of a decrease - -```{r} -mean(counterfact_predicted_ib$predicted_difference) -``` -Thus adding a single generic competitor increases the probability of termination by 16.55% on average for -the snapshots investigated. - - - -```{r} -n = length(counterfact_predicted_ib$p_predicted_intervention) -mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention))) -mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default))) -``` - - -#### USP DC Alternative - -```{r} -#formulary intervention -brand_intervention_bnc <- x[c(inherited_cols,"identical_brands")] -brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand -``` - -```{r} -counterfact_marketing_bnc <- list( - D = ncol(x),# - N = nrow(x), - L = n_categories$count, - y = as.vector(y), - ll = as.vector(categories), - x = as.matrix(x), - mu_mean = 0, - mu_stdev = 0.05, - sigma_shape = 4, - sigma_rate = 20, - Nx = nrow(x), - llx = as.vector(categories), - counterfact_x_tilde = as.matrix(brand_intervention_bnc), - counterfact_x = as.matrix(x) -) -``` - - -```{r} -generated_bnc <- gqs( - fit@stanmodel, - data=counterfact_marketing_bnc, - draws=as.matrix(fit), - seed=11021585 - ) -``` - - -```{r} -counterfact_predicted_bnc <- data.frame( - p_predicted_default = as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default) - ,p_predicted_intervention = as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention) - ,predicted_difference = as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference) -) - - -ggplot(counterfact_predicted_bnc, aes(x=p_predicted_default)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: None" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_uspdc_intervention_base.png") - -ggplot(counterfact_predicted_bnc, aes(x=p_predicted_intervention)) + - geom_density() + - labs( - title="Predicted Distribution of 'p'" - ,subtitle="Intervention: Add a single USP DC competitor" - ,x="Probability Domain 'p'" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_uspdc_intervention_interv.png") - -ggplot(counterfact_predicted_bnc, aes(x=predicted_difference)) + - geom_density() + - labs( - title="Predicted Distribution of differences 'p'" - ,subtitle="Intervention: Add a single USP DC competitor" - ,x="Difference in 'p' under treatment" - ,y="Probability Density" - ) -ggsave("./Images/DirectEffects/default_p_uspdc_intervention_distdiff.png") -``` - - -```{r} -pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |> - pivot_longer(X1:X1343) - -#Add Category names -pddf_bnc["entry_idx"] <- as.numeric(gsub("\\D","",pddf_bnc$name)) -pddf_bnc["category"] <- sapply(pddf_bnc$entry_idx, function(i) df$category_id[i]) -pddf_bnc["category_name"] <- sapply(pddf_bnc$category, function(i) beta_list$groups[i]) - - - -ggplot(pddf_bnc, aes(x=value,)) + - geom_density(bins=100) + - labs( - title = "Distribution of predicted differences" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") -ggsave("./Images/DirectEffects/p_uspdc_intervention_distdiff_styled.png") - -ggplot(pddf_bnc, aes(x=value,)) + - geom_density(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Distribution of predicted differences in 'p' | By Group" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Probability Density" - ) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_uspdc_intervention_distdiff_by_group.png") - -ggplot(pddf_bnc, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Histogram of predicted differences in 'p' | By Group" - ,subtitle = "Intervention: add a single USP DC competitor" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - #xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) -ggsave("./Images/DirectEffects/p_uspdc_intervention_histdiff_by_group.png") -``` - - -TODO: add density plot of (x,y,z) (date,value,counts) - - with and without faceting - - -```{r} -mean(counterfact_predicted_bnc$predicted_difference) -``` -Addin a single USP DC competitor increases/reduces the probability of completion by 16.47% on average -for the snapshots of trials that we have. - - - - -### Intervention: Marginal increase in time to finish enrollment - -```{r} -#| eval: false - -pddf <- data.frame(extract(generated, pars="predicted_difference")$predicted_difference) |> pivot_longer(X1:X189) -pddf["entry_idx"] <- as.numeric(gsub("\\D","",pddf$name)) - -pddf["category"] <- sapply(pddf$entry_idx, function(i) counterfact_categories[i]) -pddf["category_name"] <- sapply(pddf$category, function(i) beta_list$groups[i]) - -ggplot(pddf, aes(x=value,)) + - geom_histogram(bins=100) + - labs( - title = "Distribution of predicted differences" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - xlim(-0.3,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") - -ggplot(pddf, aes(x=value,)) + - geom_histogram(bins=100) + - facet_wrap( - ~factor( - category_name, - levels=beta_list$groups - ) - , labeller = label_wrap_gen(multi_line = TRUE) - , ncol=5) + - labs( - title = "Distribution of predicted differences", - subtitle = "By group" - ,x = "Difference in probability due to intervention" - ,y = "Predicted counts" - ) + - xlim(-0.25,0.1) + - geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") + - theme(strip.text.x = element_text(size = 8)) - -``` - - -Recall that we had really tight zero priors. - - - -# Diagnostics - -```{r} -#| eval: false -#trace plots -plot(fit, pars=c("mu"), plotfun="trace") - - -for (i in 1:4) { - print( - mcmc_rank_overlay( - fit, - pars=c( - paste0("mu[",4*i-3,"]"), - paste0("mu[",4*i-2,"]"), - paste0("mu[",4*i-1,"]"), - paste0("mu[",4*i,"]") - ), - n_bins=100 - )+ legend_move("top") + - scale_colour_ghibli_d("KikiMedium") - ) -} -``` - -```{r} -#| eval: false -plot(fit, pars=c("sigma"), plotfun="trace") - -for (i in 1:4) { - print( - mcmc_rank_overlay( - fit, - pars=c( - paste0("sigma[",4*i-3,"]"), - paste0("sigma[",4*i-2,"]"), - paste0("sigma[",4*i-1,"]"), - paste0("sigma[",4*i,"]") - ), - n_bins=100 - )+ legend_move("top") + - scale_colour_ghibli_d("KikiMedium") - ) -} -``` - -```{r} -#| eval: false -#other diagnostics -logpost <- log_posterior(fit) -nuts_prmts <- nuts_params(fit) -posterior <- as.array(fit) - -``` - -```{r} -#| eval: false -color_scheme_set("darkgray") -div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4) -mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05) -``` - -```{r} -#| eval: false -for (i in 1:4) { - mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]")) - print( - mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - mus, - "lp__" - ), - off_diag_args = list(size = 0.75) - ) - ) -} - - - -``` - -```{r} -#| eval: false -mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05) -``` - -```{r} -#| eval: false - -for (i in 1:4) { - params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]")) - print( - mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - params, - "lp__" - ), - off_diag_args = list(size = 0.75) - ) - ) -} -``` - - -```{r} -#| eval: false -for (k in 1:22) { -for (i in 1:4) { - params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]")) - print( - mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - params, - "lp__" - ), - off_diag_args = list(size = 0.75) - ) - ) -}} -``` - - - - - - -# TODO -- [ ] Double check data flow. (Write summary of this in human readable form) - - Is it the data we want from the database - - Training - - Counterfactual Evaluation - - choose a single snapshot per trial. - - Is the model in STAN well specified. -- [ ] work on LOO validation of model diff --git a/r-analysis/FullAnalysis.R b/r-analysis/FullAnalysis.R deleted file mode 100644 index 27bd5d6..0000000 --- a/r-analysis/FullAnalysis.R +++ /dev/null @@ -1,239 +0,0 @@ -library(bayesplot) -available_mcmc(pattern = "_nuts_") -library(ggplot2) -library(posterior) -library(cmdstanr) -library(rstan) # for stanfit -#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started - -#save unchanged models instead of recompiling -rstan_options(auto_write = TRUE) -#allow for multithreaded sampling -options(mc.cores = parallel::detectCores()) - -#test installation, shouldn't get any errors -#example(stan_model, package = "rstan", run.dontrun = TRUE) - -################ Pull data from database ###################### -library(RPostgreSQL) - -driver <- dbDriver("PostgreSQL") - -con <- dbConnect( - driver, - user='root', - password='root', - dbname='aact_db', - host='will-office' - ) - -query <- dbSendQuery( - con, - "select * from formatted_data_with_planned_enrollment;" - ) -df <- fetch(query,Inf) -df <- na.omit(df) - -n_categories <- 22 #number of categories in ICD-10 - -################ Format Data ########################### - - -categories <- df["category_id"] - -#Convert log(x+1) to arcsinh - -x <- df["elapsed_duration"] -x["log_n_brands"] <- log(df$n_brands + 1) -x["h_sdi_val"] <- log(df$h_sdi_val + 1) -x["hm_sdi_val"] <- log(df$hm_sdi_val + 1) -x["m_sdi_val"] <- log(df$m_sdi_val + 1) -x["lm_sdi_val"] <- log(df$lm_sdi_val + 1) -x["l_sdi_val"] <- log(df$l_sdi_val + 1) -#x["enrollment"] <- df["current_enrollment"] / df["planned_enrollment"] -#square terms -#x["enrollment^2"] <- x["enrollment"]^2 -#x["elapsed_duration^2"] <- x["elapsed_duration"]^2 -#x["n_brands^2"] <- x["n_brands"]^2 -#break out -x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0) -x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) -x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0) -x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0) - -y <- ifelse(df["final_status"]=="Terminated",1,0) - -################################# DATA EXPLORATION ############################ - -#Plot terminated vs completed -#Plot duration for terminated vs completed -#Plot different times of - -################################# FIT ######################################### -#setup data (named list) -trials_data <- list( - D = ncol(x),# - N = nrow(x), - L = n_categories, - y = as.vector(y), - ll = categories$category_id, - x = as.matrix(x), - mu_mean = 0, - mu_stdev = 0.5, - sigma_shape = 6, - sigma_rate = 12 -) - -model <- cmdstan_model(file.path("Hierarchal_Logistic.stan")) - -fit <- model$sample( - data = trials_data, - seed = 11021585, - chains = 4, - parallel_chains = 4, - refresh = 500 -) - -################################# ANALYZE ##################################### -color_scheme_set("darkgray") -div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4) - -print(fit$summary(),n=265) - -stanfitted <- rstan::read_stan_csv(fit$output_files()) - -#analyze mu values -draw_mu <- fit$draws("mu") -mcmc_hist(draw_mu) -mcmc_trace(draw_mu) -mcmc_pairs(draw_mu) -mcmc_parcoord(posterior,pars=c( - "mu[1]", - "mu[2]", - "mu[3]", - "mu[4]", - "mu[5]", - "mu[6]", - "mu[7]", - "mu[8]", - "mu[9]", - "mu[10]", - "mu[11]" -), -np=nuts_prmts, -np_style = div_style -) - - -#check sigma -draw_sigma <- fit$draws("sigma") -mcmc_hist(draw_sigma) -mcmc_trace(draw_sigma) - -mcmc_parcoord(posterior,pars=c( - "sigma[1]", - "sigma[2]", - "sigma[3]", - "sigma[4]", - "sigma[5]", - "sigma[6]", - "sigma[7]", - "sigma[8]", - "sigma[9]", - "sigma[10]", - "sigma[11]" -), -np=nuts_prmts, -np_style = div_style -) - -#other diagnostics -logpost <- log_posterior(fit) -nuts_prmts <- nuts_params(fit) -posterior <- fit$draws() - -mcmc_pairs( - posterior, - np = nuts_prmts, - pars=c( - "mu[1]", - "mu[2]", - "mu[3]", - "mu[4]", - "mu[5]", - "mu[6]", - "mu[7]", - "mu[8]", - "mu[9]", - "mu[10]", - "mu[11]" - ), - off_diag_args = list(size = 0.75) -) - -#Interpretation -mcmc_areas(posterior, - pars=c( - "mu[1]", - "mu[2]", - "mu[3]", - "mu[4]", - "mu[5]", - "mu[6]", - "mu[7]", - "mu[8]", - "mu[9]", - "mu[10]", - "mu[11]" - ), - prob = 0.95 -) - -#Interpretation -mcmc_areas(posterior, - pars=c( - "sigma[1]", - "sigma[2]", - "sigma[3]", - "sigma[4]", - "sigma[5]", - "sigma[6]", - "sigma[7]", - "sigma[8]", - "sigma[9]", - "sigma[10]", - "sigma[11]" - ), - prob = 0.95 -) - -#iterate through betas - - -mcmc_areas(posterior, - pars=c( - "beta[1,*]" - ), - prob = 0.95 -) - -#generate array of betas -betas_array <- sapply(1:11, function(param) sapply(1:22, function(group) paste0("beta[",group,",",param,"]"))) - -for (group in 1:22) { - print( - mcmc_areas(posterior, - pars=betas_array[group,], - prob = 0.95 - ) - ) -} - -for (param in 1:11) { - print( - mcmc_areas(posterior, - pars=betas_array[,param], - prob = 0.95 - ) - ) -} diff --git a/r-analysis/Hierarchal_Logistic_prior.rds b/r-analysis/Hierarchal_Logistic_prior.rds deleted file mode 100644 index 115318f..0000000 Binary files a/r-analysis/Hierarchal_Logistic_prior.rds and /dev/null differ diff --git a/r-analysis/Hierarchal_Logistic_prior.stan b/r-analysis/Hierarchal_Logistic_prior.stan deleted file mode 100644 index 23a2927..0000000 --- a/r-analysis/Hierarchal_Logistic_prior.stan +++ /dev/null @@ -1,47 +0,0 @@ -// -// This Stan program defines a simple model, with a -// vector of values 'y' modeled as normally distributed -// with mean 'mu' and standard deviation 'sigma'. -// -// Learn more about model development with Stan at: -// -// http://mc-stan.org/users/interfaces/rstan.html -// https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started -// -// The input data is a vector 'y' of length 'N'. -data { - int D; //Number of parameters - int N; // Number of observations - int L; //Number of categories - int ll[N]; - row_vector[D] x[N]; - real mu_m; - real mu_sd; - real sigma_shape; - real sigma_rate; -} -generated quantities { - //preallocate - real mu_prior[D]; - real sigma_prior[D]; - vector[D] beta_prior[L]; - real p_prior[N]; // what I have priors about - //sample parameters - for (d in 1:D) { - mu_prior[d] = normal_rng(0,1); - sigma_prior[d] = gamma_rng(2,1); - } - for (l in 1:L) { - for (d in 1:D) { - beta_prior[l,d] = normal_rng(mu_prior[d],sigma_prior[d]); - } - } - //generate probabilities - { - vector[D] b_prior[N];//local var - for (n in 1:N){ - b_prior[n] = beta_prior[ll[n]]; - p_prior[n] = inv_logit( x[n] * b_prior[n] ); - } - } -} diff --git a/r-analysis/Images/CategoryCounts.png b/r-analysis/Images/CategoryCounts.png deleted file mode 100644 index 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b/r-analysis/Images/TotalEffects/prior_sigma.png deleted file mode 100644 index ae86349..0000000 Binary files a/r-analysis/Images/TotalEffects/prior_sigma.png and /dev/null differ diff --git a/r-analysis/Logistic.stan b/r-analysis/Logistic.stan deleted file mode 100644 index fe342bb..0000000 --- a/r-analysis/Logistic.stan +++ /dev/null @@ -1,30 +0,0 @@ -// -// This Stan program defines a simple model, with a -// vector of values 'y' modeled as normally distributed -// with mean 'mu' and standard deviation 'sigma'. -// -// Learn more about model development with Stan at: -// -// http://mc-stan.org/users/interfaces/rstan.html -// https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started -// - -// The input data is a vector 'y' of length 'N'. -data { - int D; - int N; - int y[N]; - row_vector[D] x[N]; -} -parameters { - real mu[D]; - real sigma[D]; -} -model { - sigma ~ gamma(2,0.1); - mu ~ normal(0, sigma); //convert to mvnormal - - for (n in 1:N) { - y[n] ~ bernoulli_logit(x[n] * mu); - } -} diff --git a/r-analysis/basic_logit.stan b/r-analysis/basic_logit.stan deleted file mode 100644 index 5b619e9..0000000 --- a/r-analysis/basic_logit.stan +++ /dev/null @@ -1,32 +0,0 @@ -// -// This Stan program defines a simple model, with a -// vector of values 'y' modeled as normally distributed -// with mean 'mu' and standard deviation 'sigma'. -// -// Learn more about model development with Stan at: -// -// http://mc-stan.org/users/interfaces/rstan.html -// https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started -// - -// The input data is a vector 'y' of length 'N'. -data { - int N; - int k; - matrix[N,k] X; - vector[N] int y; -} - -// The parameters accepted by the model. Our model -// accepts two parameters 'mu' and 'sigma'. -parameters { - vector[k] beta; -} - -// The model to be estimated. We model the output -// 'y' to be normally distributed with mean 'mu' -// and standard deviation 'sigma'. -model { - y ~ bernoulli_logit( X * beta); -} - diff --git a/r-analysis/latex_output/DirectEffects/group_Digestive.tex b/r-analysis/latex_output/DirectEffects/group_Digestive.tex deleted file mode 100644 index 9faf663..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Digestive.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.0333554671965411, -0.0816048428798888, -0.14822283281126, -0.266661171653673, -0.122811504866503, -0.0730682464154404, -0.0948854818270773, -0.121457456523795, -0.0405372878690479, -0.00533663159113219, -0.000585600111380386, -0.00557138538681874) -c(`2.5%` = -0.650918866487675, `2.5%` = -1.46567361056997, `2.5%` = -2.00373524711419, `2.5%` = -0.884014357912585, `2.5%` = -0.589078622487128, `2.5%` = -0.515799157139537, `2.5%` = -0.575525591134666, `2.5%` = -0.619592982366338, `2.5%` = -0.700807445098139, `2.5%` = -0.478359030066064, `2.5%` = -0.747738822953945, `2.5%` = -0.547236884821695) -c(`97.5%` = 0.586408105812293, `97.5%` = 1.25686631205631, `97.5%` = 1.63450278223622, `97.5%` = 0.239311394065014, `97.5%` = 0.226457215642141, `97.5%` = 0.2713042034849, `97.5%` = 0.267732959365188, `97.5%` = 0.239658080544198, `97.5%` = 0.626632265835835, `97.5%` = 0.452833279685459, `97.5%` = 0.722592110325922, `97.5%` = 0.521616619266678) -c(`5%` = -0.521557208692412, `5%` = -1.20092272626125, `5%` = -1.62666028114303, `5%` = -0.750874732929316, `5%` = -0.479625923706422, `5%` = -0.418969780537693, `5%` = -0.465685567964911, `5%` = -0.507989133896294, `5%` = -0.561400134198236, `5%` = -0.354956928534609, `5%` = -0.571443956067767, `5%` = -0.425566130863293) -c(`95%` = 0.458661660656351, `95%` = 1.0103835099505, `95%` = 1.31428822112951, `95%` = 0.163309336653743, `95%` = 0.160084526533093, `95%` = 0.208252318625455, `95%` = 0.196998436891251, `95%` = 0.176224129361429, `95%` = 0.475022207136582, `95%` = 0.34504055978718, `95%` = 0.568906077580551, `95%` = 0.402307136842525) diff --git a/r-analysis/latex_output/DirectEffects/group_Endocrine, Nutritional, and Metabolic.tex b/r-analysis/latex_output/DirectEffects/group_Endocrine, Nutritional, and Metabolic.tex deleted file mode 100644 index d08d502..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Endocrine, Nutritional, and Metabolic.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.0353319092798262, -0.667659588573525, -0.426662961246809, 0.0627606452845482, -0.032122876366859, 0.0692372274974577, 0.00333854196734669, -0.0796819135752826, -0.20912536018799, -0.0110760315296536, -0.13143407853664, 0.208644014756123) -c(`2.5%` = -0.617973586189941, `2.5%` = -1.84852850564476, `2.5%` = -1.58611083836008, `2.5%` = -0.417049157935813, `2.5%` = -0.387151005859201, `2.5%` = -0.255831609193942, `2.5%` = -0.371336426680338, `2.5%` = -0.492590738435841, `2.5%` = -0.945439609291323, `2.5%` = -0.485658627931781, `2.5%` = -0.907802053311157, `2.5%` = -0.233024875361211) -c(`97.5%` = 0.557141349785682, `97.5%` = 0.333296222901717, `97.5%` = 0.676762640081955, `97.5%` = 0.580578562892596, `97.5%` = 0.330507422600221, `97.5%` = 0.472199940957423, `97.5%` = 0.38886451402152, `97.5%` = 0.275313479272503, `97.5%` = 0.315669435320966, `97.5%` = 0.447738518135558, `97.5%` = 0.469638387064318, `97.5%` = 0.947611094270466) -c(`5%` = -0.508832891153791, `5%` = -1.61987740240911, `5%` = -1.374497411387, `5%` = -0.335726433843714, `5%` = -0.320537590648167, `5%` = -0.196582716266943, `5%` = -0.291245691126286, `5%` = -0.405842777306539, `5%` = -0.770582950456033, `5%` = -0.37653437100647, `5%` = -0.733995288939712, `5%` = -0.159905839522464) -c(`95%` = 0.445024272718964, `95%` = 0.192677426246452, `95%` = 0.491906288928273, `95%` = 0.483104060696174, `95%` = 0.256114038078104, `95%` = 0.388274645012249, `95%` = 0.30403376761348, `95%` = 0.203557356084469, `95%` = 0.224931630208054, `95%` = 0.328780688143198, `95%` = 0.361698139473533, `95%` = 0.766303524326403) diff --git a/r-analysis/latex_output/DirectEffects/group_Eye and Adnexa.tex b/r-analysis/latex_output/DirectEffects/group_Eye and Adnexa.tex deleted file mode 100644 index 04fe3a1..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Eye and Adnexa.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.1350166586327, 1.42004536167462, -0.180236471981648, -0.0834031170778047, -0.114425712850253, -0.0624505479275495, -0.0770062362507839, -0.12325792019416, -0.062701663439268, -0.00370684105270185, 0.051697644596875, -0.0297390835449691) -c(`2.5%` = -0.841164238146927, `2.5%` = 0.166228321710892, `2.5%` = -1.24185484456687, `2.5%` = -0.575720317140294, `2.5%` = -0.544731233105528, `2.5%` = -0.498602083969468, `2.5%` = -0.4980741080226, `2.5%` = -0.568847042903758, `2.5%` = -0.680839523831996, `2.5%` = -0.462623364969651, `2.5%` = -0.622045682166618, `2.5%` = -0.577014239447581) -c(`97.5%` = 0.422370584061102, `97.5%` = 3.03047315901632, `97.5%` = 0.850790497217344, `97.5%` = 0.417839736473721, `97.5%` = 0.224427289664008, `97.5%` = 0.271253324519114, `97.5%` = 0.263581453569408, `97.5%` = 0.219054459221291, `97.5%` = 0.522372341682048, `97.5%` = 0.450642071977278, `97.5%` = 0.802350604222116, `97.5%` = 0.439020973201874) -c(`5%` = -0.674921145513311, `5%` = 0.325020098610294, `5%` = -1.07057709615347, `5%` = -0.490154573212924, `5%` = -0.455866770748054, `5%` = -0.400298559553039, `5%` = -0.40994240294468, `5%` = -0.482628439569884, `5%` = -0.557990156682396, `5%` = -0.364772370803702, `5%` = -0.481699496200734, `5%` = -0.449023424748452) -c(`95%` = 0.315064862057188, `95%` = 2.71868187996996, `95%` = 0.6825129130599, `95%` = 0.322993762960079, `95%` = 0.158724278745239, `95%` = 0.210081698558979, `95%` = 0.204675097314049, `95%` = 0.160771578813489, `95%` = 0.412334948233884, `95%` = 0.35762381742188, `95%` = 0.636739588170121, `95%` = 0.341991702173203) diff --git a/r-analysis/latex_output/DirectEffects/group_Infections & Parasites.tex b/r-analysis/latex_output/DirectEffects/group_Infections & Parasites.tex deleted file mode 100644 index 5287668..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Infections & Parasites.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.0993758150556964, 0.683653688372939, -0.420548263203837, -0.462678784625764, 0.00186329456065648, 0.0597795199040114, 0.0681148600699909, 0.0461761490923264, -0.219385658942507, -0.026301838156295, 0.32478426535094, 0.020489615259928) -c(`2.5%` = -0.649453534111085, `2.5%` = -0.0715480521081878, `2.5%` = -1.22916631970868, `2.5%` = -0.709034383494051, `2.5%` = -0.348811194923399, `2.5%` = -0.23351441505003, `2.5%` = -0.241101891952905, `2.5%` = -0.292204493477622, `2.5%` = -0.81900840565343, `2.5%` = -0.52584405348817, `2.5%` = -0.243402705068146, `2.5%` = -0.429251090289663) -c(`97.5%` = 0.368644030362086, `97.5%` = 1.47768415582758, `97.5%` = 0.39318191253476, `97.5%` = -0.23735311291066, `97.5%` = 0.391318541907379, `97.5%` = 0.385985888234964, `97.5%` = 0.428550766860501, `97.5%` = 0.438579304744015, `97.5%` = 0.239595636184448, `97.5%` = 0.404201638523238, `97.5%` = 1.23628887068288, `97.5%` = 0.487394572761673) -c(`5%` = -0.52192353020523, `5%` = 0.0437719361245331, `5%` = -1.10318164735297, `5%` = -0.662637668672948, `5%` = -0.280412083599109, `5%` = -0.17959411845604, `5%` = -0.181850570524538, `5%` = -0.231355058698291, `5%` = -0.702502858669705, `5%` = -0.40133526307109, `5%` = -0.164285973529124, `5%` = -0.340507185356601) -c(`95%` = 0.282609130587132, `95%` = 1.35108734959306, `95%` = 0.262600483963485, `95%` = -0.27266375346642, `95%` = 0.307352752974449, `95%` = 0.318231328297662, `95%` = 0.362736140529989, `95%` = 0.358037869811668, `95%` = 0.169351071503387, `95%` = 0.316946023033097, `95%` = 1.05804976340078, `95%` = 0.386482609592056) diff --git a/r-analysis/latex_output/DirectEffects/group_Mental & Behavioral.tex b/r-analysis/latex_output/DirectEffects/group_Mental & Behavioral.tex deleted file mode 100644 index 016ab3a..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Mental & Behavioral.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.101687967791986, 0.260625956246919, -1.41311346802269, 0.0234773335237628, -0.0167675265218763, 0.0856177665463779, 0.0541663300254734, -0.0489442451897826, -0.175613009906426, -0.0129978772390341, 0.0136500560555857, 0.0828587018493729) -c(`2.5%` = -0.75353018780598, `2.5%` = -1.00766752361942, `2.5%` = -3.401492690184, `2.5%` = -0.449334233697236, `2.5%` = -0.3813420073426, `2.5%` = -0.239325630096086, `2.5%` = -0.294701408616, `2.5%` = -0.44544207912836, `2.5%` = -0.892050792681191, `2.5%` = -0.494462890468011, `2.5%` = -0.666840078342412, `2.5%` = -0.376439176914794) -c(`97.5%` = 0.456963518658231, `97.5%` = 1.69446395582928, `97.5%` = 0.199302958070319, `97.5%` = 0.507180220455301, `97.5%` = 0.368701157216287, `97.5%` = 0.517238144007455, `97.5%` = 0.484238515353489, `97.5%` = 0.312151961605374, `97.5%` = 0.370881206753106, `97.5%` = 0.435596577978869, `97.5%` = 0.721254548490607, `97.5%` = 0.696764948267981) -c(`5%` = -0.598975479040644, `5%` = -0.774388773799267, `5%` = -3.03473408528801, `5%` = -0.369579631037732, `5%` = -0.302781726817594, `5%` = -0.180751834428207, `5%` = -0.227459855411846, `5%` = -0.359959289739994, `5%` = -0.726956896133474, `5%` = -0.37607446715096, `5%` = -0.511640963000957, `5%` = -0.283655680952849) -c(`95%` = 0.334682947415185, `95%` = 1.39113469911214, `95%` = -0.0370570732959479, `95%` = 0.424927943281204, `95%` = 0.290563883495334, `95%` = 0.420212397505738, `95%` = 0.392316891142529, `95%` = 0.240788422786828, `95%` = 0.268210851959822, `95%` = 0.333041306872968, `95%` = 0.570750639989039, `95%` = 0.544517445336076) diff --git a/r-analysis/latex_output/DirectEffects/group_Musculoskeletal.tex b/r-analysis/latex_output/DirectEffects/group_Musculoskeletal.tex deleted file mode 100644 index 13c9fdc..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Musculoskeletal.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(0.100124327907652, -1.41625310169202, 1.52446064588643, -0.0873137500894338, -0.0677464856346281, 0.0108650892063307, 0.00612669940351482, -0.0314187576539557, -0.211716341147135, -0.00183935600163672, -0.0738493576307788, 0.115533405660669) -c(`2.5%` = -0.406446255373983, `2.5%` = -2.60148475745005, `2.5%` = 0.527275445018903, `2.5%` = -0.584022199941087, `2.5%` = -0.463154561951676, `2.5%` = -0.346315618199531, `2.5%` = -0.362381508509159, `2.5%` = -0.409819426261724, `2.5%` = -0.936591248518692, `2.5%` = -0.457818242374006, `2.5%` = -0.798071774552942, `2.5%` = -0.329263294357893) -c(`97.5%` = 0.797719164264997, `97.5%` = -0.457781574665431, `97.5%` = 2.69049448523487, `97.5%` = 0.374989682835585, `97.5%` = 0.295740868485436, `97.5%` = 0.377261709291995, `97.5%` = 0.395888264864696, `97.5%` = 0.341241054883828, `97.5%` = 0.314398740452913, `97.5%` = 0.452102307502102, `97.5%` = 0.547821159342026, `97.5%` = 0.740236931779179) -c(`5%` = -0.316960032380135, `5%` = -2.39673285825223, `5%` = 0.677198726234082, `5%` = -0.48370456159241, `5%` = -0.380333066540771, `5%` = -0.271668438834234, `5%` = -0.28575489564353, `5%` = -0.331549343941397, `5%` = -0.776805336768321, `5%` = -0.356984822002284, `5%` = -0.649098498864267, `5%` = -0.244951545599123) -c(`95%` = 0.636944255371381, `95%` = -0.579922080756115, `95%` = 2.46984046267875, `95%` = 0.301174279247803, `95%` = 0.219720584710747, `95%` = 0.305577531163344, `95%` = 0.314025671253092, `95%` = 0.263968986148209, `95%` = 0.22352188185812, `95%` = 0.343376182190238, `95%` = 0.424983622057134, `95%` = 0.586186029847553) diff --git a/r-analysis/latex_output/DirectEffects/group_Neoplasms.tex b/r-analysis/latex_output/DirectEffects/group_Neoplasms.tex deleted file mode 100644 index 318b89f..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Neoplasms.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.412146324077768, 0.470951452129627, -1.2444367751168, 0.245268419893298, -0.0946502363520193, 0.0500740339796919, -0.0704987792914352, -0.124433125807786, -0.358974070196284, -0.00267878134821512, -0.476611882301335, -0.140862225354917) -c(`2.5%` = -0.990046725218716, `2.5%` = 0.0825563723532098, `2.5%` = -1.78375367793506, `2.5%` = -0.144576058853997, `2.5%` = -0.509816834681553, `2.5%` = -0.248086617381755, `2.5%` = -0.463337090435845, `2.5%` = -0.545151665330706, `2.5%` = -0.959715545078129, `2.5%` = -0.477380824369578, `2.5%` = -1.42904260420137, `2.5%` = -0.614365567191018) -c(`97.5%` = 0.0176791224477116, `97.5%` = 0.869227382956849, `97.5%` = -0.71625139500421, `97.5%` = 0.711161632402175, `97.5%` = 0.235929001218496, `97.5%` = 0.403217442159893, `97.5%` = 0.250453043701491, `97.5%` = 0.208799589086939, `97.5%` = 0.0761919118896935, `97.5%` = 0.485090234449767, `97.5%` = 0.0901610144027345, `97.5%` = 0.205381626274404) -c(`5%` = -0.880856751562785, `5%` = 0.140776593909285, `5%` = -1.69975002339859, `5%` = -0.0851775550449366, `5%` = -0.413144404534561, `5%` = -0.190555995277503, `5%` = -0.374730282423007, `5%` = -0.457195436456302, `5%` = -0.860866809127253, `5%` = -0.371058543649785, `5%` = -1.24192236504004, `5%` = -0.521580687834855) -c(`95%` = -0.0294487230376872, `95%` = 0.805026483916245, `95%` = -0.79680171298815, `95%` = 0.622920880640266, `95%` = 0.180557342384185, `95%` = 0.328458422894137, `95%` = 0.191388773507259, `95%` = 0.144775027690501, `95%` = 0.0202098111117333, `95%` = 0.368527660640565, `95%` = 0.0339253948301201, `95%` = 0.150938308973965) diff --git a/r-analysis/latex_output/DirectEffects/group_Nervous System.tex b/r-analysis/latex_output/DirectEffects/group_Nervous System.tex deleted file mode 100644 index 363a4b4..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Nervous System.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.0605258874521727, 0.208110428258286, 2.932306880193, -0.476036244602958, -0.138197079911931, 0.0472640116153194, -0.0108024185768078, -0.0673889444925787, 0.0439801430116134, -0.00220242035797092, 0.0222560987441026, -0.0954924005742835) -c(`2.5%` = -0.668423764362022, `2.5%` = -0.483919695463572, `2.5%` = 1.32770940293854, `2.5%` = -1.08972255835219, `2.5%` = -0.588355736454172, `2.5%` = -0.288061459094765, `2.5%` = -0.390261122641681, `2.5%` = -0.480037769561072, `2.5%` = -0.52071567059202, `2.5%` = -0.467981470659442, `2.5%` = -0.64696023851359, `2.5%` = -0.702986558104766) -c(`97.5%` = 0.524706569957862, `97.5%` = 0.860804399758541, `97.5%` = 4.8161142366925, `97.5%` = 0.00951208071252858, `97.5%` = 0.205030274179544, `97.5%` = 0.447777447414387, `97.5%` = 0.375894039808643, `97.5%` = 0.309831367516505, `97.5%` = 0.714311594742576, `97.5%` = 0.46197268399125, `97.5%` = 0.724174566356542, `97.5%` = 0.352194753343582) -c(`5%` = -0.540693659717227, `5%` = -0.358913993804725, `5%` = 1.56455100307583, `5%` = -0.970987430695906, `5%` = -0.492652682388389, `5%` = -0.226103001674273, `5%` = -0.310685728948158, `5%` = -0.401058959418401, `5%` = -0.416255094218329, `5%` = -0.352812984615348, `5%` = -0.514000899450722, `5%` = -0.55490968224003) -c(`95%` = 0.404566643306311, `95%` = 0.757665883111782, `95%` = 4.4435402069247, `95%` = -0.0535686895330286, `95%` = 0.144052434219696, `95%` = 0.356322084893283, `95%` = 0.295168504418982, `95%` = 0.235723096029231, `95%` = 0.569378098960284, `95%` = 0.353283663339729, `95%` = 0.576987914910334, `95%` = 0.268009451996814) diff --git a/r-analysis/latex_output/DirectEffects/group_Skin & Subcutaneaous tissue.tex b/r-analysis/latex_output/DirectEffects/group_Skin & Subcutaneaous tissue.tex deleted file mode 100644 index b79ec37..0000000 --- a/r-analysis/latex_output/DirectEffects/group_Skin & Subcutaneaous tissue.tex +++ /dev/null @@ -1,6 +0,0 @@ -c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec") -c(-0.174849840107956, 0.344654754591335, -0.71289290741607, -0.185341893373765, -0.0612704269574426, 0.0475554801866666, 0.0209325549818404, 0.00823090691189444, -0.140469476402341, -0.000991729207921393, 0.0426395752164606, 0.0531332616653892) -c(`2.5%` = -0.853225149285778, `2.5%` = -0.81212639023518, `2.5%` = -2.47632399275718, `2.5%` = -0.700340720981917, `2.5%` = -0.459624700583444, `2.5%` = -0.299576900991634, `2.5%` = -0.329465385054038, `2.5%` = -0.35530723101666, `2.5%` = -0.818237407714228, `2.5%` = -0.457007790658148, `2.5%` = -0.626446320243322, `2.5%` = -0.42852558763013) -c(`97.5%` = 0.313015825195173, `97.5%` = 1.64628843970952, `97.5%` = 0.803673159441113, `97.5%` = 0.264097087549475, `97.5%` = 0.301714093464364, `97.5%` = 0.457951093623432, `97.5%` = 0.41718558607108, `97.5%` = 0.42368030495614, `97.5%` = 0.415680090003177, `97.5%` = 0.473772689053127, `97.5%` = 0.774791684477986, `97.5%` = 0.622393397809352) -c(`5%` = -0.69853877504723, `5%` = -0.619677989159712, `5%` = -2.16966249843568, `5%` = -0.602161468147168, `5%` = -0.370652637547503, `5%` = -0.233289235150446, `5%` = -0.267307968491817, `5%` = -0.289828310680018, `5%` = -0.66997803833358, `5%` = -0.350248519811252, `5%` = -0.483515522092064, `5%` = -0.330171694279594) -c(`95%` = 0.228050943041459, `95%` = 1.41171899585957, `95%` = 0.585314360782336, `95%` = 0.187637916801343, `95%` = 0.231239446941901, `95%` = 0.360332344911533, `95%` = 0.329385758977443, `95%` = 0.33895575065099, `95%` = 0.315101103321463, `95%` = 0.355492422245274, `95%` = 0.614652923649533, `95%` = 0.486396526448576) diff --git a/r-analysis/latex_output/DirectEffects/group_status_ANR.tex b/r-analysis/latex_output/DirectEffects/group_status_ANR.tex deleted file mode 100644 index 64aeac1..0000000 --- a/r-analysis/latex_output/DirectEffects/group_status_ANR.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.0379222039996637, -0.00951521433050643, -0.0357669209565548, -0.0289858802339238, -0.0384045782213899, -0.0354852935888951, -0.195806801668196, -0.0319728051993748, -0.064502474892061, -0.0359000714018003, -0.209385718567549, -0.0293405266823335, -0.166256613851307, -0.204154479367649, -0.343021435135372, 0.0429675640807533, -0.031540048708067, -0.0346985641986963, -0.0303375281259717, -0.13391213882268, -0.0315874427691754, -0.0292508480128129) -c(`2.5%` = -0.670177498740691, `2.5%` = -0.606574716528012, `2.5%` = -0.660124076867341, `2.5%` = -0.658500223161749, `2.5%` = -0.673266724039317, `2.5%` = -0.693646849412836, `2.5%` = -0.917059388708302, `2.5%` = -0.662394912651839, `2.5%` = -0.67991481986874, `2.5%` = -0.670316698080103, `2.5%` = -0.83183723815622, `2.5%` = -0.663476379211969, `2.5%` = -0.854735257874132, `2.5%` = -0.918576287776926, `2.5%` = -0.963532341357661, `2.5%` = -0.495917243186253, `2.5%` = -0.64751983879636, `2.5%` = -0.673951139603439, -`2.5%` = -0.665804518054749, `2.5%` = -0.791664538636436, `2.5%` = -0.643761100670645, `2.5%` = -0.680737220370752) -c(`97.5%` = 0.579428469021833, `97.5%` = 0.640138775494956, `97.5%` = 0.596180548733038, `97.5%` = 0.619030332938483, `97.5%` = 0.597678927212641, `97.5%` = 0.6121441149317, `97.5%` = 0.323295322658126, `97.5%` = 0.590730699845894, `97.5%` = 0.503098249043251, `97.5%` = 0.607349406190736, `97.5%` = 0.228895375416542, `97.5%` = 0.624129832779025, `97.5%` = 0.342536899260382, `97.5%` = 0.307896298234294, `97.5%` = 0.0903633166835597, `97.5%` = 0.686527591660775, `97.5%` = 0.616181981700487, `97.5%` = 0.599647736473311, -`97.5%` = 0.609123918623858, `97.5%` = 0.415337198000103, `97.5%` = 0.597111489383847, `97.5%` = 0.632428409170173) -c(`5%` = -0.530855657109975, `5%` = -0.487829039222839, `5%` = -0.530023192811465, `5%` = -0.516610981668542, `5%` = -0.524022832574353, `5%` = -0.537218989651104, `5%` = -0.75728605285178, `5%` = -0.517067859106373, `5%` = -0.547235882003818, `5%` = -0.536626649173697, `5%` = -0.691946442016388, `5%` = -0.52266339334223, `5%` = -0.70021531860572, `5%` = -0.769560225018392, `5%` = -0.844315621869912, `5%` = -0.393051567766974, `5%` = -0.519010088416499, `5%` = -0.529941857869653, `5%` = -0.52218968602785, -`5%` = -0.648665425674694, `5%` = -0.520022794035758, `5%` = -0.521893002755623) -c(`95%` = 0.449851048402911, `95%` = 0.496036928523184, `95%` = 0.452249441742121, `95%` = 0.47353271148754, `95%` = 0.454890426424996, `95%` = 0.474673790264394, `95%` = 0.22238953047732, `95%` = 0.44333608430914, `95%` = 0.387054472484903, `95%` = 0.475167927437824, `95%` = 0.156987753663085, `95%` = 0.473536190521047, `95%` = 0.254381363435638, `95%` = 0.217911529700368, `95%` = 0.0348859178585696, `95%` = 0.552104217589191, `95%` = 0.462090261872998, `95%` = 0.456310261272674, `95%` = 0.474214855342067, -`95%` = 0.307884811241422, `95%` = 0.465192052817642, `95%` = 0.472198045319103) diff --git a/r-analysis/latex_output/DirectEffects/parameters_10_status_EBI.tex b/r-analysis/latex_output/DirectEffects/parameters_10_status_EBI.tex deleted file mode 100644 index 2cc26ab..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_10_status_EBI.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.00232413253272223, -0.00152293417694882, -0.00353563813667985, -0.00310660613711883, -0.00533663159113219, -0.00305599644590309, -0.0110760315296536, -0.00588960985826809, -0.00370684105270185, -0.00470552774062355, -0.026301838156295, -0.00292165177766262, -0.0129978772390341, -0.00183935600163672, -0.00267878134821512, -0.00220242035797092, -0.000914878424206371, -0.00258882299014672, 0.000504336438586614, -0.000991729207921393, -0.000800806458059062, -0.00386686775250893) -c(`2.5%` = -0.479474973075092, `2.5%` = -0.472669740624549, `2.5%` = -0.464039194626794, `2.5%` = -0.473392603483428, `2.5%` = -0.478359030066064, `2.5%` = -0.455950775505214, `2.5%` = -0.485658627931781, `2.5%` = -0.485882001168744, `2.5%` = -0.462623364969651, `2.5%` = -0.464633556660333, `2.5%` = -0.52584405348817, `2.5%` = -0.46165946058897, `2.5%` = -0.494462890468011, `2.5%` = -0.457818242374006, `2.5%` = -0.477380824369578, `2.5%` = -0.467981470659442, `2.5%` = -0.462006993832474, `2.5%` = -0.475538447435748, -`2.5%` = -0.459119942403029, `2.5%` = -0.457007790658148, `2.5%` = -0.457457906185736, `2.5%` = -0.463105985034812) -c(`97.5%` = 0.472157783917002, `97.5%` = 0.465334754332652, `97.5%` = 0.444423983236463, `97.5%` = 0.450919265047758, `97.5%` = 0.452833279685459, `97.5%` = 0.451291140159942, `97.5%` = 0.447738518135558, `97.5%` = 0.47182876476933, `97.5%` = 0.450642071977278, `97.5%` = 0.460733730501955, `97.5%` = 0.404201638523238, `97.5%` = 0.463075602426935, `97.5%` = 0.435596577978869, `97.5%` = 0.452102307502102, `97.5%` = 0.485090234449767, `97.5%` = 0.46197268399125, `97.5%` = 0.471768785447442, `97.5%` = 0.469214434976901, -`97.5%` = 0.458559371773605, `97.5%` = 0.473772689053127, `97.5%` = 0.468011769036541, `97.5%` = 0.460260492516532) -c(`5%` = -0.362566524131353, `5%` = -0.365391855607623, `5%` = -0.356688829996404, `5%` = -0.360094627174451, `5%` = -0.354956928534609, `5%` = -0.35377118365327, `5%` = -0.37653437100647, `5%` = -0.370648036848808, `5%` = -0.364772370803702, `5%` = -0.363091670495567, `5%` = -0.40133526307109, `5%` = -0.359038021511569, `5%` = -0.37607446715096, `5%` = -0.356984822002284, `5%` = -0.371058543649785, `5%` = -0.352812984615348, `5%` = -0.357581652703343, `5%` = -0.357584782489892, `5%` = -0.358080523145794, -`5%` = -0.350248519811252, `5%` = -0.359275569693152, `5%` = -0.355239427293753) -c(`95%` = 0.349080687253274, `95%` = 0.362582528037606, `95%` = 0.344628062144889, `95%` = 0.343429488771613, `95%` = 0.34504055978718, `95%` = 0.347420792879581, `95%` = 0.328780688143198, `95%` = 0.352000827804009, `95%` = 0.35762381742188, `95%` = 0.353870410467441, `95%` = 0.316946023033097, `95%` = 0.352512087776552, `95%` = 0.333041306872968, `95%` = 0.343376182190238, `95%` = 0.368527660640565, `95%` = 0.353283663339729, `95%` = 0.354267255995946, `95%` = 0.347884235465927, `95%` = 0.352698507186818, -`95%` = 0.355492422245274, `95%` = 0.354508762801702, `95%` = 0.347938178694997) diff --git a/r-analysis/latex_output/DirectEffects/parameters_11_status_Rec.tex b/r-analysis/latex_output/DirectEffects/parameters_11_status_Rec.tex deleted file mode 100644 index c88e566..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_11_status_Rec.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.00224061119544955, -0.0498803962359136, 0.0029866500758097, 0.0092020834745846, -0.00557138538681874, 0.00139701841247001, 0.208644014756123, 0.00486415637216138, -0.0297390835449691, 0.00664076692247885, 0.020489615259928, 0.00270098452440913, 0.0828587018493729, 0.115533405660669, -0.140862225354917, -0.0954924005742835, 0.00218728416741836, 0.00490518108019053, -0.000597898274967985, 0.0531332616653892, 0.00140665840745891, 0.00267108484868849) -c(`2.5%` = -0.573130925585206, `2.5%` = -0.631300654586586, `2.5%` = -0.549248794254278, `2.5%` = -0.526524124449695, `2.5%` = -0.547236884821695, `2.5%` = -0.525794262806427, `2.5%` = -0.233024875361211, `2.5%` = -0.531351924132048, `2.5%` = -0.577014239447581, `2.5%` = -0.526231597802405, `2.5%` = -0.429251090289663, `2.5%` = -0.539844609215633, `2.5%` = -0.376439176914794, `2.5%` = -0.329263294357893, `2.5%` = -0.614365567191018, `2.5%` = -0.702986558104766, `2.5%` = -0.548813703529005, `2.5%` = -0.549035834183446, -`2.5%` = -0.55240500338214, `2.5%` = -0.42852558763013, `2.5%` = -0.532991842266845, `2.5%` = -0.561981734264935) -c(`97.5%` = 0.535870607314021, `97.5%` = 0.439183977577692, `97.5%` = 0.531820860628844, `97.5%` = 0.557228636508852, `97.5%` = 0.521616619266678, `97.5%` = 0.521530978763819, `97.5%` = 0.947611094270466, `97.5%` = 0.551470726780118, `97.5%` = 0.439020973201874, `97.5%` = 0.543800507198643, `97.5%` = 0.487394572761673, `97.5%` = 0.537619293317655, `97.5%` = 0.696764948267981, `97.5%` = 0.740236931779179, `97.5%` = 0.205381626274404, `97.5%` = 0.352194753343582, `97.5%` = 0.553383244915363, `97.5%` = 0.559402688668633, -`97.5%` = 0.550312950172821, `97.5%` = 0.622393397809352, `97.5%` = 0.536869755062453, `97.5%` = 0.557804559325436) -c(`5%` = -0.428266097170789, `5%` = -0.491562554012717, `5%` = -0.411292594357374, `5%` = -0.403177595011685, `5%` = -0.425566130863293, `5%` = -0.404058425652893, `5%` = -0.159905839522464, `5%` = -0.416161496643271, `5%` = -0.449023424748452, `5%` = -0.399764467982059, `5%` = -0.340507185356601, `5%` = -0.420233862640142, `5%` = -0.283655680952849, `5%` = -0.244951545599123, `5%` = -0.521580687834855, `5%` = -0.55490968224003, `5%` = -0.422336361660143, `5%` = -0.422015963935169, `5%` = -0.420900175948769, -`5%` = -0.330171694279594, `5%` = -0.41323722677094, `5%` = -0.42744212405137) -c(`95%` = 0.404392809444781, `95%` = 0.333871293266063, `95%` = 0.417529613693018, `95%` = 0.430463617558602, `95%` = 0.402307136842525, `95%` = 0.410955507727826, `95%` = 0.766303524326403, `95%` = 0.430330128267061, `95%` = 0.341991702173203, `95%` = 0.419913772461363, `95%` = 0.386482609592056, `95%` = 0.421100502019285, `95%` = 0.544517445336076, `95%` = 0.586186029847553, `95%` = 0.150938308973965, `95%` = 0.268009451996814, `95%` = 0.425628186353177, `95%` = 0.433826523724275, `95%` = 0.417184121248096, -`95%` = 0.486396526448576, `95%` = 0.410430471444198, `95%` = 0.43788787573263) diff --git a/r-analysis/latex_output/DirectEffects/parameters_12_status_ANR.tex b/r-analysis/latex_output/DirectEffects/parameters_12_status_ANR.tex deleted file mode 100644 index 799c746..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_12_status_ANR.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.033444959026225, -0.00793522059592051, -0.0336009736151623, -0.0316339144412533, -0.0405372878690479, -0.0414663539452286, -0.20912536018799, -0.0266756188322575, -0.062701663439268, -0.0331874740057613, -0.219385658942507, -0.0325137781481118, -0.175613009906426, -0.211716341147135, -0.358974070196284, 0.0439801430116134, -0.0306047651482687, -0.032498476375438, -0.0371883825844407, -0.140469476402341, -0.0333940650001783, -0.0354885056744574) -c(`2.5%` = -0.666923716865872, `2.5%` = -0.64102067023692, `2.5%` = -0.677795179892062, `2.5%` = -0.670757215379559, `2.5%` = -0.700807445098139, `2.5%` = -0.693814265711575, `2.5%` = -0.945439609291323, `2.5%` = -0.649489899228185, `2.5%` = -0.680839523831996, `2.5%` = -0.69007268009126, `2.5%` = -0.81900840565343, `2.5%` = -0.679848629284621, `2.5%` = -0.892050792681191, `2.5%` = -0.936591248518692, `2.5%` = -0.959715545078129, `2.5%` = -0.52071567059202, `2.5%` = -0.684856132016299, `2.5%` = -0.682381096206198, -`2.5%` = -0.682121426892847, `2.5%` = -0.818237407714228, `2.5%` = -0.644091572578507, `2.5%` = -0.695950746544654) -c(`97.5%` = 0.62590334980134, `97.5%` = 0.653919501490395, `97.5%` = 0.641015835184623, `97.5%` = 0.629010798078368, `97.5%` = 0.626632265835835, `97.5%` = 0.605986429897587, `97.5%` = 0.315669435320966, `97.5%` = 0.629421610550102, `97.5%` = 0.522372341682048, `97.5%` = 0.611617883076929, `97.5%` = 0.239595636184448, `97.5%` = 0.614136402646953, `97.5%` = 0.370881206753106, `97.5%` = 0.314398740452913, `97.5%` = 0.0761919118896935, `97.5%` = 0.714311594742576, `97.5%` = 0.62707456073199, `97.5%` = 0.660256568215515, -`97.5%` = 0.614029672287225, `97.5%` = 0.415680090003177, `97.5%` = 0.590770974069619, `97.5%` = 0.638335616472465) -c(`5%` = -0.534614253816882, `5%` = -0.492652438031023, `5%` = -0.534510689945915, `5%` = -0.537204763734244, `5%` = -0.561400134198236, `5%` = -0.554901107934352, `5%` = -0.770582950456033, `5%` = -0.526625109409884, `5%` = -0.557990156682396, `5%` = -0.545095270800367, `5%` = -0.702502858669705, `5%` = -0.532227460109175, `5%` = -0.726956896133474, `5%` = -0.776805336768321, `5%` = -0.860866809127253, `5%` = -0.416255094218329, `5%` = -0.540848631429852, `5%` = -0.545406019188074, `5%` = -0.556323223126082, -`5%` = -0.66997803833358, `5%` = -0.528524246946612, `5%` = -0.557925242336448) -c(`95%` = 0.477502832552862, `95%` = 0.513718992636229, `95%` = 0.489098182794968, `95%` = 0.480711435248938, `95%` = 0.475022207136582, `95%` = 0.474127651026557, `95%` = 0.224931630208054, `95%` = 0.491419189883086, `95%` = 0.412334948233884, `95%` = 0.482084498841415, `95%` = 0.169351071503387, `95%` = 0.478552110978265, `95%` = 0.268210851959822, `95%` = 0.22352188185812, `95%` = 0.0202098111117333, `95%` = 0.569378098960284, `95%` = 0.485945600247666, `95%` = 0.497377449520452, `95%` = 0.472090491001111, -`95%` = 0.315101103321463, `95%` = 0.458319237867322, `95%` = 0.490829889891898) diff --git a/r-analysis/latex_output/DirectEffects/parameters_1_Elapsed Duration.tex b/r-analysis/latex_output/DirectEffects/parameters_1_Elapsed Duration.tex deleted file mode 100644 index 99433ae..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_1_Elapsed Duration.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.0295164133163417, -0.0430143836673774, -0.0339902806330816, -0.0260159056108803, -0.0333554671965411, -0.028359266287689, -0.0353319092798262, -0.0187357101597514, -0.1350166586327, -0.0260977976963767, -0.0993758150556964, -0.0236033005860729, -0.101687967791986, 0.100124327907652, -0.412146324077768, -0.0605258874521727, -0.024078230002875, -0.0269598652137639, -0.0274504939892296, -0.174849840107956, -0.0222497943593906, -0.0274902413057739) -c(`2.5%` = -0.646070710340414, `2.5%` = -0.683290321643176, `2.5%` = -0.675081729811003, `2.5%` = -0.648421169748859, `2.5%` = -0.650918866487675, `2.5%` = -0.668055634029354, `2.5%` = -0.617973586189941, `2.5%` = -0.640444657164909, `2.5%` = -0.841164238146927, `2.5%` = -0.670733739121956, `2.5%` = -0.649453534111085, `2.5%` = -0.635643676510193, `2.5%` = -0.75353018780598, `2.5%` = -0.406446255373983, `2.5%` = -0.990046725218716, `2.5%` = -0.668423764362022, `2.5%` = -0.65459523058199, `2.5%` = -0.623518595242253, -`2.5%` = -0.639580335809163, `2.5%` = -0.853225149285778, `2.5%` = -0.651372688023807, `2.5%` = -0.665950740608084) -c(`97.5%` = 0.622118536783365, `97.5%` = 0.567754232733557, `97.5%` = 0.594665290830079, `97.5%` = 0.595712650833978, `97.5%` = 0.586408105812293, `97.5%` = 0.61702506930373, `97.5%` = 0.557141349785682, `97.5%` = 0.64037824695388, `97.5%` = 0.422370584061102, `97.5%` = 0.619806919714738, `97.5%` = 0.368644030362086, `97.5%` = 0.596438871658726, `97.5%` = 0.456963518658231, `97.5%` = 0.797719164264997, `97.5%` = 0.0176791224477116, `97.5%` = 0.524706569957862, `97.5%` = 0.622703525731803, `97.5%` = 0.570789155361791, -`97.5%` = 0.59747717452121, `97.5%` = 0.313015825195173, `97.5%` = 0.627269012162122, `97.5%` = 0.615198351898292) -c(`5%` = -0.510251564339674, `5%` = -0.52441458740886, `5%` = -0.532441957391025, `5%` = -0.518410421043954, `5%` = -0.521557208692412, `5%` = -0.521871400612245, `5%` = -0.508832891153791, `5%` = -0.518203720240489, `5%` = -0.674921145513311, `5%` = -0.531996138266231, `5%` = -0.52192353020523, `5%` = -0.500496765978635, `5%` = -0.598975479040644, `5%` = -0.316960032380135, `5%` = -0.880856751562785, `5%` = -0.540693659717227, `5%` = -0.519375237016899, `5%` = -0.509904935942235, `5%` = -0.513849598148613, -`5%` = -0.69853877504723, `5%` = -0.511181165016275, `5%` = -0.520466766457042) -c(`95%` = 0.471281041217163, `95%` = 0.423703175708398, `95%` = 0.466378617261805, `95%` = 0.454221478113165, `95%` = 0.458661660656351, `95%` = 0.460036483429395, `95%` = 0.445024272718964, `95%` = 0.492401099446282, `95%` = 0.315064862057188, `95%` = 0.475963967540287, `95%` = 0.282609130587132, `95%` = 0.45986621483045, `95%` = 0.334682947415185, `95%` = 0.636944255371381, `95%` = -0.0294487230376872, `95%` = 0.404566643306311, `95%` = 0.478649635433352, `95%` = 0.441853123918437, `95%` = 0.463352362829126, -`95%` = 0.228050943041459, `95%` = 0.482992956431603, `95%` = 0.466730867021221) diff --git a/r-analysis/latex_output/DirectEffects/parameters_2_asinh(Generic Brands).tex b/r-analysis/latex_output/DirectEffects/parameters_2_asinh(Generic Brands).tex deleted file mode 100644 index 3ed15f3..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_2_asinh(Generic Brands).tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.12331418463673, -0.643524712834449, -0.155293145618607, -0.00107765414463224, -0.14822283281126, 0.00881755884088804, -0.426662961246809, -0.0178268855590179, -0.180236471981648, -0.290840496837507, -0.420548263203837, -0.00380033220537924, -1.41311346802269, 1.52446064588643, -1.2444367751168, 2.932306880193, 0.00169846910876418, -0.00893420267820256, -0.235385994622511, -0.71289290741607, -0.00820736853904142, -0.12592765159372) -c(`2.5%` = -2.02564979096055, `2.5%` = -2.39439013494348, `2.5%` = -1.99208722130077, `2.5%` = -1.83809201587386, `2.5%` = -2.00373524711419, `2.5%` = -1.87235054261414, `2.5%` = -1.58611083836008, `2.5%` = -1.81806137603714, `2.5%` = -1.24185484456687, `2.5%` = -2.12331233705335, `2.5%` = -1.22916631970868, `2.5%` = -1.87838853425636, `2.5%` = -3.401492690184, `2.5%` = 0.527275445018903, `2.5%` = -1.78375367793506, `2.5%` = 1.32770940293854, `2.5%` = -1.89529361773991, `2.5%` = -1.9106750132439, -`2.5%` = -2.02803126368439, `2.5%` = -2.47632399275718, `2.5%` = -1.86122689687105, `2.5%` = -2.00542308533653) -c(`97.5%` = 1.67969744861974, `97.5%` = 0.935428242477581, `97.5%` = 1.6348058321464, `97.5%` = 1.81710270358056, `97.5%` = 1.63450278223622, `97.5%` = 1.88045146010562, `97.5%` = 0.676762640081955, `97.5%` = 1.78906314682073, `97.5%` = 0.850790497217344, `97.5%` = 1.47057766493624, `97.5%` = 0.39318191253476, `97.5%` = 1.88452418451776, `97.5%` = 0.199302958070319, `97.5%` = 2.69049448523487, `97.5%` = -0.71625139500421, `97.5%` = 4.8161142366925, `97.5%` = 1.90068098208742, `97.5%` = 1.88773849773307, -`97.5%` = 1.47639661532028, `97.5%` = 0.803673159441113, `97.5%` = 1.86049650391514, `97.5%` = 1.71917016589104) -c(`5%` = -1.66549179341294, `5%` = -2.05805403972291, `5%` = -1.66084826903427, `5%` = -1.50609037865979, `5%` = -1.62666028114303, `5%` = -1.53803827380169, `5%` = -1.374497411387, `5%` = -1.50464058833537, `5%` = -1.07057709615347, `5%` = -1.79986867754153, `5%` = -1.10318164735297, `5%` = -1.56027911708145, `5%` = -3.03473408528801, `5%` = 0.677198726234082, `5%` = -1.69975002339859, `5%` = 1.56455100307583, `5%` = -1.54500435851042, `5%` = -1.54650472228178, `5%` = -1.71550508080056, `5%` = -2.16966249843568, -`5%` = -1.51253234614188, `5%` = -1.68286480200484) -c(`95%` = 1.38012867025627, `95%` = 0.661680376811838, `95%` = 1.31572988103649, `95%` = 1.49439855925108, `95%` = 1.31428822112951, `95%` = 1.54140325707139, `95%` = 0.491906288928273, `95%` = 1.47557433066007, `95%` = 0.6825129130599, `95%` = 1.15276028747074, `95%` = 0.262600483963485, `95%` = 1.54587386125945, `95%` = -0.0370570732959479, `95%` = 2.46984046267875, `95%` = -0.79680171298815, `95%` = 4.4435402069247, `95%` = 1.53642174870284, `95%` = 1.54093350572683, `95%` = 1.19508577316775, -`95%` = 0.585314360782336, `95%` = 1.5156111408874, `95%` = 1.37175745000343) diff --git a/r-analysis/latex_output/DirectEffects/parameters_3_asinh(Competitors USPDC).tex b/r-analysis/latex_output/DirectEffects/parameters_3_asinh(Competitors USPDC).tex deleted file mode 100644 index 48e2679..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_3_asinh(Competitors USPDC).tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.101703799949395, -0.564637906246216, -0.0956910914293675, -0.00337692444405644, -0.0816048428798888, -6.35740684684093e-05, -0.667659588573525, -0.00405812351552042, 1.42004536167462, -0.171009890912524, 0.683653688372939, 0.00344017290368172, 0.260625956246919, -1.41625310169202, 0.470951452129627, 0.208110428258286, 0.00250209506601534, -0.00562874748733102, -0.105857797157013, 0.344654754591335, 0.0027807037509892, -0.0552183959035103) -c(`2.5%` = -1.51590135737519, `2.5%` = -1.842080521195, `2.5%` = -1.52784116663569, `2.5%` = -1.39142200782843, `2.5%` = -1.46567361056997, `2.5%` = -1.42253864934177, `2.5%` = -1.84852850564476, `2.5%` = -1.35828695365276, `2.5%` = 0.166228321710892, `2.5%` = -1.56024033377577, `2.5%` = -0.0715480521081878, `2.5%` = -1.39463661876756, `2.5%` = -1.00766752361942, `2.5%` = -2.60148475745005, `2.5%` = 0.0825563723532098, `2.5%` = -0.483919695463572, `2.5%` = -1.40349956302864, `2.5%` = -1.38403675822353, -`2.5%` = -1.53199462919355, `2.5%` = -0.81212639023518, `2.5%` = -1.34556223161126, `2.5%` = -1.46894825626173) -c(`97.5%` = 1.23043134810689, `97.5%` = 0.516158819481392, `97.5%` = 1.27757713500863, `97.5%` = 1.34564557876177, `97.5%` = 1.25686631205631, `97.5%` = 1.4133810381728, `97.5%` = 0.333296222901717, `97.5%` = 1.3394057445905, `97.5%` = 3.03047315901632, `97.5%` = 1.09705630948954, `97.5%` = 1.47768415582758, `97.5%` = 1.42305354019587, `97.5%` = 1.69446395582928, `97.5%` = -0.457781574665431, `97.5%` = 0.869227382956849, `97.5%` = 0.860804399758541, `97.5%` = 1.39777584880611, `97.5%` = 1.32536002712745, -`97.5%` = 1.22054546483929, `97.5%` = 1.64628843970952, `97.5%` = 1.40043610000601, `97.5%` = 1.32705054827579) -c(`5%` = -1.23554421440395, `5%` = -1.60865719854905, `5%` = -1.26990351126472, `5%` = -1.10623630944065, `5%` = -1.20092272626125, `5%` = -1.14277805533658, `5%` = -1.61987740240911, `5%` = -1.10114220997093, `5%` = 0.325020098610294, `5%` = -1.28688609021933, `5%` = 0.0437719361245331, `5%` = -1.13051681084733, `5%` = -0.774388773799267, `5%` = -2.39673285825223, `5%` = 0.140776593909285, `5%` = -0.358913993804725, `5%` = -1.12240343354847, `5%` = -1.13264764154031, `5%` = -1.25936194261107, `5%` = -0.619677989159712, -`5%` = -1.12306907889816, `5%` = -1.19574516476068) -c(`95%` = 0.9962993915319, `95%` = 0.344271026201751, `95%` = 1.02585852524287, `95%` = 1.11395208896461, `95%` = 1.0103835099505, `95%` = 1.11961350598891, `95%` = 0.192677426246452, `95%` = 1.10063510284539, `95%` = 2.71868187996996, `95%` = 0.899896640181501, `95%` = 1.35108734959306, `95%` = 1.14893128770879, `95%` = 1.39113469911214, `95%` = -0.579922080756115, `95%` = 0.805026483916245, `95%` = 0.757665883111782, `95%` = 1.12853607517645, `95%` = 1.09611509546288, `95%` = 0.961250543645123, -`95%` = 1.41171899585957, `95%` = 1.10399402315835, `95%` = 1.04723154370974) diff --git a/r-analysis/latex_output/DirectEffects/parameters_9_status_NYR.tex b/r-analysis/latex_output/DirectEffects/parameters_9_status_NYR.tex deleted file mode 100644 index 6961a2c..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_9_status_NYR.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.000505821125346685, 0.045620786014229, -0.00525743090751, -0.00499600218282098, -0.000585600111380386, -0.00276881425069573, -0.13143407853664, -0.0036185203171912, 0.051697644596875, -0.0107891870271464, 0.32478426535094, -0.00145937361475847, 0.0136500560555857, -0.0738493576307788, -0.476611882301335, 0.0222560987441026, -0.00414548910371886, -0.0104742177218119, -0.00809846939246779, 0.0426395752164606, -0.0115019802718463, -0.00996274304864821) -c(`2.5%` = -0.701450360691855, `2.5%` = -0.63003373124748, `2.5%` = -0.715622145882045, `2.5%` = -0.727044467579978, `2.5%` = -0.747738822953945, `2.5%` = -0.735270274057596, `2.5%` = -0.907802053311157, `2.5%` = -0.733860049506474, `2.5%` = -0.622045682166618, `2.5%` = -0.742120219363362, `2.5%` = -0.243402705068146, `2.5%` = -0.735852738037434, `2.5%` = -0.666840078342412, `2.5%` = -0.798071774552942, `2.5%` = -1.42904260420137, `2.5%` = -0.64696023851359, `2.5%` = -0.746859618917552, `2.5%` = -0.739101938071361, -`2.5%` = -0.730727303837492, `2.5%` = -0.626446320243322, `2.5%` = -0.750902816931559, `2.5%` = -0.743675008324717) -c(`97.5%` = 0.732116835481222, `97.5%` = 0.789641535896855, `97.5%` = 0.73726328358774, `97.5%` = 0.702602048400459, `97.5%` = 0.722592110325922, `97.5%` = 0.745772208274965, `97.5%` = 0.469638387064318, `97.5%` = 0.740059158395697, `97.5%` = 0.802350604222116, `97.5%` = 0.729903013043148, `97.5%` = 1.23628887068288, `97.5%` = 0.735322757301528, `97.5%` = 0.721254548490607, `97.5%` = 0.547821159342026, `97.5%` = 0.0901610144027345, `97.5%` = 0.724174566356542, `97.5%` = 0.75034324181327, `97.5%` = 0.709982748482133, -`97.5%` = 0.719605886783329, `97.5%` = 0.774791684477986, `97.5%` = 0.735072184820506, `97.5%` = 0.723815080592094) -c(`5%` = -0.558975272641589, `5%` = -0.481208746850505, `5%` = -0.558122801520996, `5%` = -0.566607488065583, `5%` = -0.571443956067767, `5%` = -0.560863708568709, `5%` = -0.733995288939712, `5%` = -0.573157666695002, `5%` = -0.481699496200734, `5%` = -0.584184368190383, `5%` = -0.164285973529124, `5%` = -0.561811722478321, `5%` = -0.511640963000957, `5%` = -0.649098498864267, `5%` = -1.24192236504004, `5%` = -0.514000899450722, `5%` = -0.573055024907531, `5%` = -0.589897724557502, `5%` = -0.576009342907356, -`5%` = -0.483515522092064, `5%` = -0.59377439249432, `5%` = -0.589781985563434) -c(`95%` = 0.558241074532826, `95%` = 0.6343107609265, `95%` = 0.564064518165769, `95%` = 0.554270165096566, `95%` = 0.568906077580551, `95%` = 0.564421378888805, `95%` = 0.361698139473533, `95%` = 0.556521770858677, `95%` = 0.636739588170121, `95%` = 0.554481744418514, `95%` = 1.05804976340078, `95%` = 0.564219400738208, `95%` = 0.570750639989039, `95%` = 0.424983622057134, `95%` = 0.0339253948301201, `95%` = 0.576987914910334, `95%` = 0.588745135323456, `95%` = 0.565535664200477, `95%` = 0.572292114386171, -`95%` = 0.614652923649533, `95%` = 0.564155780832837, `95%` = 0.568721991063375) diff --git a/r-analysis/latex_output/DirectEffects/parameters_Elapsed Duration.tex b/r-analysis/latex_output/DirectEffects/parameters_Elapsed Duration.tex deleted file mode 100644 index f8f7d70..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_Elapsed Duration.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.0338934915059191, -0.0424617202550975, -0.0299850538975256, -0.0228882519097089, -0.0337281404548882, -0.0258485686453499, -0.0402405594871021, -0.0253886763112486, -0.136200903561077, -0.0212623461765324, -0.102097548934776, -0.0229996389940294, -0.106049590275806, 0.103092606051222, -0.431577879697863, -0.0589099972525265, -0.0270810088184279, -0.0260112735850109, -0.0325654806803472, -0.182061588936881, -0.0261135283573324, -0.0227456753432774) -c(`2.5%` = -0.690100665844187, `2.5%` = -0.671202369164881, `2.5%` = -0.666088312766176, `2.5%` = -0.666578142141343, `2.5%` = -0.678544709698103, `2.5%` = -0.669196847024182, `2.5%` = -0.655923003454131, `2.5%` = -0.655413313800275, `2.5%` = -0.845732099866594, `2.5%` = -0.653192374517221, `2.5%` = -0.64122257390992, `2.5%` = -0.659159394086157, `2.5%` = -0.787764109870441, `2.5%` = -0.425148647750428, `2.5%` = -1.00072959307916, `2.5%` = -0.69270592355035, `2.5%` = -0.685883418294076, `2.5%` = -0.679342853679393, -`2.5%` = -0.681272654592773, `2.5%` = -0.837786199816108, `2.5%` = -0.665616824031369, `2.5%` = -0.6589068176532) -c(`97.5%` = 0.607853806397433, `97.5%` = 0.575243002929989, `97.5%` = 0.623466621187216, `97.5%` = 0.641896362473495, `97.5%` = 0.611672327879679, `97.5%` = 0.621633709372348, `97.5%` = 0.556891230159617, `97.5%` = 0.626653736033388, `97.5%` = 0.426452209102124, `97.5%` = 0.604814400002199, `97.5%` = 0.370375731808055, `97.5%` = 0.656507843091209, `97.5%` = 0.463728458888126, `97.5%` = 0.803945161990884, `97.5%` = 0.00182348352747238, `97.5%` = 0.534212402242803, `97.5%` = 0.609945240239135, `97.5%` = 0.637145158270836, -`97.5%` = 0.595960590167125, `97.5%` = 0.325103240011011, `97.5%` = 0.639977001719915, `97.5%` = 0.645740444211412) -c(`5%` = -0.543756319725243, `5%` = -0.53599965672411, `5%` = -0.53861642350383, `5%` = -0.536297908680718, `5%` = -0.535626704345617, `5%` = -0.534014891414532, `5%` = -0.515645442393892, `5%` = -0.521060513099755, `5%` = -0.698732119314075, `5%` = -0.513828973026094, `5%` = -0.536163536290694, `5%` = -0.520608887905471, `5%` = -0.637108697299896, `5%` = -0.333645087620771, `5%` = -0.901047800865227, `5%` = -0.558977042638121, `5%` = -0.528351288300235, `5%` = -0.531154576765547, `5%` = -0.535401625627599, -`5%` = -0.704974120731683, `5%` = -0.530102478693174, `5%` = -0.519892009691237) -c(`95%` = 0.476219882900217, `95%` = 0.43869836398252, `95%` = 0.487869109380049, `95%` = 0.495857205487324, `95%` = 0.477771545962831, `95%` = 0.479824548055006, `95%` = 0.431579111164995, `95%` = 0.489104423628694, `95%` = 0.318624951651975, `95%` = 0.475052338519103, `95%` = 0.285016338923033, `95%` = 0.498321179113614, `95%` = 0.356019293245049, `95%` = 0.645249978382246, `95%` = -0.0484915873069474, `95%` = 0.422234919504667, `95%` = 0.474969816313099, `95%` = 0.495469548426488, `95%` = 0.459482254828791, -`95%` = 0.237533499815169, `95%` = 0.491700443982918, `95%` = 0.504132013362862) diff --git a/r-analysis/latex_output/DirectEffects/parameters_asinh(Competitors USPDC).tex b/r-analysis/latex_output/DirectEffects/parameters_asinh(Competitors USPDC).tex deleted file mode 100644 index bed5234..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_asinh(Competitors USPDC).tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.101589582550983, -0.55509837146517, -0.0876297898120394, -0.00424167706294526, -0.0856815547958816, -0.000891733412756743, -0.656064432872784, -0.00724344466650777, 1.39799100967958, -0.162272605765493, 0.674878170950668, 0.00679859198995776, 0.256393632879033, -1.39692218698997, 0.469486930453339, 0.201756832022176, -0.000743046330130356, -0.00239703676867723, -0.101293376110023, 0.334667664170298, 0.0036268316699096, -0.0553940979488865) -c(`2.5%` = -1.50442203323384, `2.5%` = -1.84877532048521, `2.5%` = -1.4700191365573, `2.5%` = -1.37500277258178, `2.5%` = -1.4848837602478, `2.5%` = -1.32756197431566, `2.5%` = -1.85894539233901, `2.5%` = -1.33085969325607, `2.5%` = 0.16999404397094, `2.5%` = -1.53594319207073, `2.5%` = -0.0683721763680483, `2.5%` = -1.33523195430546, `2.5%` = -0.987723094632562, `2.5%` = -2.58976230693659, `2.5%` = 0.0834766521350615, `2.5%` = -0.475822349223494, `2.5%` = -1.33731236431054, `2.5%` = -1.35448275771273, -`2.5%` = -1.42832958185923, `2.5%` = -0.797988116140275, `2.5%` = -1.3168218907587, `2.5%` = -1.41268885278703) -c(`97.5%` = 1.22021244556502, `97.5%` = 0.475219087766019, `97.5%` = 1.2205452457074, `97.5%` = 1.38725107457264, `97.5%` = 1.24763911953619, `97.5%` = 1.36828112117342, `97.5%` = 0.304797046306457, `97.5%` = 1.2809727123191, `97.5%` = 2.97850864225959, `97.5%` = 1.1200916681713, `97.5%` = 1.48172994699915, `97.5%` = 1.35752400157911, `97.5%` = 1.59974497777273, `97.5%` = -0.420429798817082, `97.5%` = 0.874242905367939, `97.5%` = 0.869912231337424, `97.5%` = 1.32550284173821, `97.5%` = 1.35888949832736, -`97.5%` = 1.1936539738932, `97.5%` = 1.609867105785, `97.5%` = 1.34949094435626, `97.5%` = 1.22944948085911) -c(`5%` = -1.22562889252442, `5%` = -1.59673376165319, `5%` = -1.21885811476843, `5%` = -1.11813404961243, `5%` = -1.20918844446486, `5%` = -1.07055731145333, `5%` = -1.6236745264581, `5%` = -1.07506861425794, `5%` = 0.318380069304849, `5%` = -1.29675430841515, `5%` = 0.041344205634408, `5%` = -1.10591252208745, `5%` = -0.740508923444193, `5%` = -2.36760582705321, `5%` = 0.147263440981433, `5%` = -0.359590775683247, `5%` = -1.09935756841891, `5%` = -1.10179682888056, `5%` = -1.19332341912876, `5%` = -0.607636638304879, -`5%` = -1.08485266813522, `5%` = -1.13445756592737) -c(`95%` = 0.972806121648346, `95%` = 0.338841318556515, `95%` = 0.988158619006595, `95%` = 1.09780676333781, `95%` = 1.00794578824477, `95%` = 1.1020724405486, `95%` = 0.184033329014398, `95%` = 1.04560505760623, `95%` = 2.69222620971344, `95%` = 0.884777865331596, `95%` = 1.35166837167041, `95%` = 1.11527199108615, `95%` = 1.36231797243719, `95%` = -0.54782380504844, `95%` = 0.80963853468214, `95%` = 0.767827618485196, `95%` = 1.08512970421361, `95%` = 1.08918975608724, `95%` = 0.953418962916104, -`95%` = 1.37967304828955, `95%` = 1.09273387336662, `95%` = 1.00375347161432) diff --git a/r-analysis/latex_output/DirectEffects/parameters_asinh(Generic Brands).tex b/r-analysis/latex_output/DirectEffects/parameters_asinh(Generic Brands).tex deleted file mode 100644 index 73c48a1..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_asinh(Generic Brands).tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.107172841753111, -0.652217546171737, -0.158153468999919, -0.00872754000664511, -0.152469836074415, -0.00511048990810721, -0.432756950030333, -0.0176343397968463, -0.174941414466581, -0.312199818256478, -0.421462561969054, -0.00543187548165017, -1.41347612539297, 1.52465722253264, -1.24155955461637, 2.9333026125354, -0.0260562265370799, -0.00708575237043839, -0.236014521608801, -0.708583530845461, -0.00537911254785814, -0.115835951645073) -c(`2.5%` = -1.98999201263705, `2.5%` = -2.39214090710107, `2.5%` = -2.01846766579584, `2.5%` = -1.8757515797304, `2.5%` = -2.0334114088637, `2.5%` = -1.85212654123702, `2.5%` = -1.60516533489668, `2.5%` = -1.81993228600129, `2.5%` = -1.25821009021598, `2.5%` = -2.16646127648176, `2.5%` = -1.25120863703686, `2.5%` = -1.88684117832516, `2.5%` = -3.34866909602091, `2.5%` = 0.47643422496699, `2.5%` = -1.7911797031523, `2.5%` = 1.31489061844565, `2.5%` = -1.85712798081873, `2.5%` = -1.91898409412556, -`2.5%` = -2.07179758391882, `2.5%` = -2.45975286581684, `2.5%` = -1.8878079254812, `2.5%` = -2.01296251650662) -c(`97.5%` = 1.7025058387162, `97.5%` = 0.881408586766731, `97.5%` = 1.65331806761982, `97.5%` = 1.81774079148411, `97.5%` = 1.66674910435877, `97.5%` = 1.86647987416102, `97.5%` = 0.645259664512326, `97.5%` = 1.80599008456882, `97.5%` = 0.856678839564855, `97.5%` = 1.41839221738041, `97.5%` = 0.360026899921426, `97.5%` = 1.89267030844417, `97.5%` = 0.201003318675158, `97.5%` = 2.69056887105094, `97.5%` = -0.715192981105914, `97.5%` = 4.78775173338893, `97.5%` = 1.83490876984593, `97.5%` = 1.88931228937232, -`97.5%` = 1.5091000822795, `97.5%` = 0.802989438020258, `97.5%` = 1.86093048637463, `97.5%` = 1.70786218772968) -c(`5%` = -1.62973704571072, `5%` = -2.07294410260449, `5%` = -1.69801214255266, `5%` = -1.53184108821626, `5%` = -1.68584731697327, `5%` = -1.54481245603677, `5%` = -1.39759449940044, `5%` = -1.49900355048193, `5%` = -1.06880137117197, `5%` = -1.83763159160532, `5%` = -1.11103379908195, `5%` = -1.53234039460221, `5%` = -3.00363339456024, `5%` = 0.638951221037764, `5%` = -1.70700005124097, `5%` = 1.54171189173018, `5%` = -1.54285125721537, `5%` = -1.58329512208634, `5%` = -1.76844032673346, `5%` = -2.13894081007506, -`5%` = -1.54263378199832, `5%` = -1.67735076532346) -c(`95%` = 1.41879257978458, `95%` = 0.648571235197482, `95%` = 1.33700306317695, `95%` = 1.49389338436903, `95%` = 1.3461526982071, `95%` = 1.50194445717269, `95%` = 0.481912048276356, `95%` = 1.48993841444006, `95%` = 0.70167186942968, `95%` = 1.12285471327414, `95%` = 0.252246900593004, `95%` = 1.55595453136487, `95%` = -0.0371324515642565, `95%` = 2.48090816148459, `95%` = -0.797626914298678, `95%` = 4.47581369319019, `95%` = 1.48349835052459, `95%` = 1.55512904554564, `95%` = 1.23036073907808, -`95%` = 0.570893677970625, `95%` = 1.51891783905047, `95%` = 1.38398983193578) diff --git a/r-analysis/latex_output/DirectEffects/parameters_status_ANR.tex b/r-analysis/latex_output/DirectEffects/parameters_status_ANR.tex deleted file mode 100644 index 64aeac1..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_status_ANR.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.0379222039996637, -0.00951521433050643, -0.0357669209565548, -0.0289858802339238, -0.0384045782213899, -0.0354852935888951, -0.195806801668196, -0.0319728051993748, -0.064502474892061, -0.0359000714018003, -0.209385718567549, -0.0293405266823335, -0.166256613851307, -0.204154479367649, -0.343021435135372, 0.0429675640807533, -0.031540048708067, -0.0346985641986963, -0.0303375281259717, -0.13391213882268, -0.0315874427691754, -0.0292508480128129) -c(`2.5%` = -0.670177498740691, `2.5%` = -0.606574716528012, `2.5%` = -0.660124076867341, `2.5%` = -0.658500223161749, `2.5%` = -0.673266724039317, `2.5%` = -0.693646849412836, `2.5%` = -0.917059388708302, `2.5%` = -0.662394912651839, `2.5%` = -0.67991481986874, `2.5%` = -0.670316698080103, `2.5%` = -0.83183723815622, `2.5%` = -0.663476379211969, `2.5%` = -0.854735257874132, `2.5%` = -0.918576287776926, `2.5%` = -0.963532341357661, `2.5%` = -0.495917243186253, `2.5%` = -0.64751983879636, `2.5%` = -0.673951139603439, -`2.5%` = -0.665804518054749, `2.5%` = -0.791664538636436, `2.5%` = -0.643761100670645, `2.5%` = -0.680737220370752) -c(`97.5%` = 0.579428469021833, `97.5%` = 0.640138775494956, `97.5%` = 0.596180548733038, `97.5%` = 0.619030332938483, `97.5%` = 0.597678927212641, `97.5%` = 0.6121441149317, `97.5%` = 0.323295322658126, `97.5%` = 0.590730699845894, `97.5%` = 0.503098249043251, `97.5%` = 0.607349406190736, `97.5%` = 0.228895375416542, `97.5%` = 0.624129832779025, `97.5%` = 0.342536899260382, `97.5%` = 0.307896298234294, `97.5%` = 0.0903633166835597, `97.5%` = 0.686527591660775, `97.5%` = 0.616181981700487, `97.5%` = 0.599647736473311, -`97.5%` = 0.609123918623858, `97.5%` = 0.415337198000103, `97.5%` = 0.597111489383847, `97.5%` = 0.632428409170173) -c(`5%` = -0.530855657109975, `5%` = -0.487829039222839, `5%` = -0.530023192811465, `5%` = -0.516610981668542, `5%` = -0.524022832574353, `5%` = -0.537218989651104, `5%` = -0.75728605285178, `5%` = -0.517067859106373, `5%` = -0.547235882003818, `5%` = -0.536626649173697, `5%` = -0.691946442016388, `5%` = -0.52266339334223, `5%` = -0.70021531860572, `5%` = -0.769560225018392, `5%` = -0.844315621869912, `5%` = -0.393051567766974, `5%` = -0.519010088416499, `5%` = -0.529941857869653, `5%` = -0.52218968602785, -`5%` = -0.648665425674694, `5%` = -0.520022794035758, `5%` = -0.521893002755623) -c(`95%` = 0.449851048402911, `95%` = 0.496036928523184, `95%` = 0.452249441742121, `95%` = 0.47353271148754, `95%` = 0.454890426424996, `95%` = 0.474673790264394, `95%` = 0.22238953047732, `95%` = 0.44333608430914, `95%` = 0.387054472484903, `95%` = 0.475167927437824, `95%` = 0.156987753663085, `95%` = 0.473536190521047, `95%` = 0.254381363435638, `95%` = 0.217911529700368, `95%` = 0.0348859178585696, `95%` = 0.552104217589191, `95%` = 0.462090261872998, `95%` = 0.456310261272674, `95%` = 0.474214855342067, -`95%` = 0.307884811241422, `95%` = 0.465192052817642, `95%` = 0.472198045319103) diff --git a/r-analysis/latex_output/DirectEffects/parameters_status_EBI.tex b/r-analysis/latex_output/DirectEffects/parameters_status_EBI.tex deleted file mode 100644 index bbe4bb3..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_status_EBI.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.00486381271611535, -0.00234177649674774, -0.00515183815786496, -0.00421305648644563, -0.00389136209748, -0.00610673537139904, -0.0126249322314229, -0.00563946285209699, -0.00713553939645088, -0.00556109229654605, -0.0296681294218427, -0.00302065971753859, -0.0160603020475268, -0.0033204482574487, -0.00366894940571103, -0.0033782308159864, -0.00147263938060779, -0.00281902338049175, -0.00516465945474794, 0.00117504910800777, -0.00536819164938224, -0.00341714311862774) -c(`2.5%` = -0.4928902240148, `2.5%` = -0.471597568252128, `2.5%` = -0.507060592786285, `2.5%` = -0.496187329730551, `2.5%` = -0.493846364631587, `2.5%` = -0.512035468722232, `2.5%` = -0.503922991423901, `2.5%` = -0.499136678960227, `2.5%` = -0.498255016423811, `2.5%` = -0.479550877030934, `2.5%` = -0.527710057117981, `2.5%` = -0.47738578213782, `2.5%` = -0.504170004794057, `2.5%` = -0.488027036715767, `2.5%` = -0.508311086739842, `2.5%` = -0.488325893335097, `2.5%` = -0.500350365353871, `2.5%` = -0.489797593321065, -`2.5%` = -0.493614948527939, `2.5%` = -0.478099940085293, `2.5%` = -0.480388926446991, `2.5%` = -0.480339298649014) -c(`97.5%` = 0.477181388655603, `97.5%` = 0.478253799598711, `97.5%` = 0.483897884400919, `97.5%` = 0.477733634248101, `97.5%` = 0.480671053527334, `97.5%` = 0.483026873590512, `97.5%` = 0.455687812451121, `97.5%` = 0.488335048561587, `97.5%` = 0.476215783846475, `97.5%` = 0.466429687492193, `97.5%` = 0.423734438029339, `97.5%` = 0.474465088221571, `97.5%` = 0.458565492328125, `97.5%` = 0.493372345702204, `97.5%` = 0.494169616877378, `97.5%` = 0.483648553653476, `97.5%` = 0.505031717406501, `97.5%` = 0.46569700208092, -`97.5%` = 0.471415999038097, `97.5%` = 0.49621435196537, `97.5%` = 0.476743752937605, `97.5%` = 0.467403287438688) -c(`5%` = -0.384839256524113, `5%` = -0.370890653819468, `5%` = -0.389717242753722, `5%` = -0.382138541945515, `5%` = -0.371384438942403, `5%` = -0.390763667720738, `5%` = -0.387699039445137, `5%` = -0.394477291159571, `5%` = -0.39220012720177, `5%` = -0.380637632210929, `5%` = -0.411827047834269, `5%` = -0.374609901237044, `5%` = -0.39083922063065, `5%` = -0.387483535903527, `5%` = -0.390162763770864, `5%` = -0.37365571697494, `5%` = -0.381743220627302, `5%` = -0.369783694172149, `5%` = -0.385379188161294, -`5%` = -0.373496126525493, `5%` = -0.377291769622258, `5%` = -0.368630128560942) -c(`95%` = 0.363815583651208, `95%` = 0.370227548106358, `95%` = 0.373524054930431, `95%` = 0.369698098361814, `95%` = 0.369300634866769, `95%` = 0.36794145288606, `95%` = 0.358137811206345, `95%` = 0.368909612747601, `95%` = 0.364170778673988, `95%` = 0.365633719092603, `95%` = 0.33048200014893, `95%` = 0.363053774138134, `95%` = 0.347229606528999, `95%` = 0.382089305066404, `95%` = 0.387514695717931, `95%` = 0.375728674172833, `95%` = 0.385485941672613, `95%` = 0.361659448576342, `95%` = 0.368353921113658, -`95%` = 0.388511185198981, `95%` = 0.369886822399706, `95%` = 0.365593471311159) diff --git a/r-analysis/latex_output/DirectEffects/parameters_status_NYR.tex b/r-analysis/latex_output/DirectEffects/parameters_status_NYR.tex deleted file mode 100644 index 0af286e..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_status_NYR.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(-0.00832576787627327, 0.0502181175509121, -0.00254797568859221, 0.00037391254389325, -0.00114134588173891, -0.00451497853572214, -0.142153374440972, -0.00195287948710614, 0.0654719447924103, -0.00677887728466109, 0.345662047693858, -0.00157110819962499, 0.0149688400470326, -0.0782677724551585, -0.49791727768667, 0.0367700783674713, -0.00716063945233656, -0.00176465495021884, -0.00435528924328807, 0.043863405759711, -0.00548445340847891, -0.0100421324092617) -c(`2.5%` = -0.798176858708199, `2.5%` = -0.649287090985632, `2.5%` = -0.752180190601171, `2.5%` = -0.765720288201341, `2.5%` = -0.761310269145469, `2.5%` = -0.769404872006371, `2.5%` = -0.962752437012212, `2.5%` = -0.768468256092748, `2.5%` = -0.613672917773224, `2.5%` = -0.741396156335141, `2.5%` = -0.240225670397011, `2.5%` = -0.778550541201842, `2.5%` = -0.671882552677685, `2.5%` = -0.829702544391712, `2.5%` = -1.47884841599921, `2.5%` = -0.653709569493313, `2.5%` = -0.752391272912992, `2.5%` = -0.725938326026952, -`2.5%` = -0.785753969844037, `2.5%` = -0.654434721761801, `2.5%` = -0.772385114493806, `2.5%` = -0.781547218625338) -c(`97.5%` = 0.76034469875563, `97.5%` = 0.845633947832787, `97.5%` = 0.770407348684183, `97.5%` = 0.814474757996063, `97.5%` = 0.781292873345278, `97.5%` = 0.779826237909694, `97.5%` = 0.494695385934057, `97.5%` = 0.766089116079086, `97.5%` = 0.858482468878284, `97.5%` = 0.747665756417337, `97.5%` = 1.31119870717426, `97.5%` = 0.791026840863526, `97.5%` = 0.72899311565112, `97.5%` = 0.558765218546179, `97.5%` = 0.0976524892433262, `97.5%` = 0.781833224643643, `97.5%` = 0.741470213490854, `97.5%` = 0.780312796382334, -`97.5%` = 0.776445672109217, `97.5%` = 0.835951227522058, `97.5%` = 0.75578833553135, `97.5%` = 0.750101827954997) -c(`5%` = -0.616116918100389, `5%` = -0.512051758484746, `5%` = -0.589817478032612, `5%` = -0.60546319491298, `5%` = -0.583839532437199, `5%` = -0.599673916128779, `5%` = -0.771236336079563, `5%` = -0.595759768355798, `5%` = -0.47175814689012, `5%` = -0.585717774179175, `5%` = -0.156763845257426, `5%` = -0.590593736012543, `5%` = -0.529938547912805, `5%` = -0.669829197453793, `5%` = -1.26963007145184, `5%` = -0.508859660216102, `5%` = -0.602929326426733, `5%` = -0.573320601674407, `5%` = -0.611359700135997, -`5%` = -0.497215940632518, `5%` = -0.609562003721472, `5%` = -0.603191340531708) -c(`95%` = 0.597088301775022, `95%` = 0.672111603595297, `95%` = 0.597359235347557, `95%` = 0.61918916388514, `95%` = 0.595716780090434, `95%` = 0.597426850327021, `95%` = 0.368655084080813, `95%` = 0.592458408903632, `95%` = 0.689843781266554, `95%` = 0.577350192007013, `95%` = 1.10666629752813, `95%` = 0.608146006908894, `95%` = 0.567469287801582, `95%` = 0.431936020918889, `95%` = 0.0306194413604995, `95%` = 0.619341630421946, `95%` = 0.574033327341637, `95%` = 0.591738000435572, `95%` = 0.598312121323889, -`95%` = 0.645311247919002, `95%` = 0.587439430679619, `95%` = 0.583648949072352) diff --git a/r-analysis/latex_output/DirectEffects/parameters_status_Rec.tex b/r-analysis/latex_output/DirectEffects/parameters_status_Rec.tex deleted file mode 100644 index 2892f34..0000000 --- a/r-analysis/latex_output/DirectEffects/parameters_status_Rec.tex +++ /dev/null @@ -1,10 +0,0 @@ -c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.") -c(0.00150784514582254, -0.0497907368054486, 0.00434380644445338, 0.00218718976679278, -0.00434291403887011, 0.00302001293551759, 0.20336581169622, 0.00343296396767198, -0.0385920705103708, -0.00141334075132648, 0.0207373662562078, 0.0015000850140683, 0.0801600329536096, 0.113121841705728, -0.136274450655838, -0.0938571304025214, 0.00282123851288503, -0.00146833112673362, -0.00082584116000937, 0.0483281653119099, 0.00659062980994563, -0.00055535206472268) -c(`2.5%` = -0.550531966199477, `2.5%` = -0.629394891363134, `2.5%` = -0.527672919300448, `2.5%` = -0.509227038923741, `2.5%` = -0.550868934343651, `2.5%` = -0.542855202488396, `2.5%` = -0.225399121304215, `2.5%` = -0.534213624588921, `2.5%` = -0.57231251252735, `2.5%` = -0.552100213849103, `2.5%` = -0.419418356555291, `2.5%` = -0.567178469443596, `2.5%` = -0.370748780309252, `2.5%` = -0.318655190350014, `2.5%` = -0.614817809004343, `2.5%` = -0.703951782706409, `2.5%` = -0.524221538466178, `2.5%` = -0.541042128309509, -`2.5%` = -0.553979099390109, `2.5%` = -0.429137673995, `2.5%` = -0.534659084619921, `2.5%` = -0.53134970116874) -c(`97.5%` = 0.566388836665101, `97.5%` = 0.445760030666272, `97.5%` = 0.550712495508801, `97.5%` = 0.510548679450175, `97.5%` = 0.543799039346651, `97.5%` = 0.546171328528686, `97.5%` = 0.932547573554047, `97.5%` = 0.562478415721886, `97.5%` = 0.429713264446548, `97.5%` = 0.509501648868077, `97.5%` = 0.482434252392398, `97.5%` = 0.552388649630916, `97.5%` = 0.655194288743027, `97.5%` = 0.700748091326741, `97.5%` = 0.205549966757929, `97.5%` = 0.348679606266848, `97.5%` = 0.523973004291571, `97.5%` = 0.52237026080622, -`97.5%` = 0.551330298928471, `97.5%` = 0.607445504451599, `97.5%` = 0.557102597425807, `97.5%` = 0.509379389425598) -c(`5%` = -0.429366635609993, `5%` = -0.496218717595005, `5%` = -0.411147660889218, `5%` = -0.398570977236643, `5%` = -0.422007458084124, `5%` = -0.412200344995762, `5%` = -0.160763338289191, `5%` = -0.423552637832629, `5%` = -0.458333301038196, `5%` = -0.418097069598191, `5%` = -0.324235933898656, `5%` = -0.417261044253087, `5%` = -0.281885257093864, `5%` = -0.234540125743699, `5%` = -0.508687221040413, `5%` = -0.56179500309088, `5%` = -0.411556667615538, `5%` = -0.418938395029271, `5%` = -0.433526826335861, -`5%` = -0.33703866498282, `5%` = -0.415046863367997, `5%` = -0.417110901717416) -c(`95%` = 0.433864705383823, `95%` = 0.34493920495178, `95%` = 0.421270429380312, `95%` = 0.402340857877479, `95%` = 0.396223107432888, `95%` = 0.417603923761695, `95%` = 0.749712378119009, `95%` = 0.423198808398514, `95%` = 0.339524038845502, `95%` = 0.410767571164308, `95%` = 0.384778572752778, `95%` = 0.427718825229051, `95%` = 0.522116792789822, `95%` = 0.572014784561829, `95%` = 0.15192983211524, `95%` = 0.262927135744447, `95%` = 0.408590937183379, `95%` = 0.414061379519885, `95%` = 0.424759632515887, -`95%` = 0.478613866408021, `95%` = 0.43229047773505, `95%` = 0.400833024334571) diff --git a/r-analysis/melting_stanfit.r b/r-analysis/melting_stanfit.r deleted file mode 100644 index f1b4df7..0000000 --- a/r-analysis/melting_stanfit.r +++ /dev/null @@ -1,98 +0,0 @@ -library(stringr) -library(reshape2) -#goal here is to take a given stanfit object (titled fit here) -#and extract, mark up, and melt a given set of parameters so that -#the data can be processed easily with ggplot2 - -extract_nums <- function(str) -{ - #https://stackoverflow.com/a/12727871 - str_extract_all(str,"\\(?[0-9]+\\)?")[[1]] -} -remap <- function(mapping) { - function(index) { - mapping[index] - } -} - - - -parameter_list <- c( - `1`="Elapsed Duration", - `2`="asinh(Number Brands)", - `3`="asinh(High SDI)", - `4`="asinh(High-Medium SDI)", - `5`="asinh(Medium SDI)", - `6`="asinh(Low-Medium SDI)", - `7`="asinh(Low SDI)" -) -group_list <- c( - `1`="Infections & Parasites", - `2`="Neoplasms", - `3`="Blood & Immune system", - `4`="Endocrine, Nutritional, and Metabolic", - `5`="Mental & Behavioral", - `6`="Nervous System", - `7`="Eye and Adnexa", - `8`="Ear and Mastoid", - `9`="Circulatory", - `10`="Respiratory", - `11`="Digestive", - `12`="Skin & Subcutaneaous tissue", - `13`="Musculoskeletal", - `14`="Genitourinary", - `15`="Pregancy, Childbirth, & Puerperium", - `16`="Perinatal Period", - `17`="Congential", - `18`="Symptoms, Signs etc.", - `19`="Injury etc.", - `20`="External Causes", - `21`="Contact with Healthcare", - `22`="Special Purposes" -) - -param <- "beta" -param_cols <- grep(param,fit@sim$fnames_oi) -param_col_names <- fit@sim$fnames_oi[param_cols] - - - -#TODO: get it to collect all the data. - -itt = 1 -#extract data for iteration -data <- as.data.frame(fit@sim$samples[[itt]][beta_cols]) -#add col number -data <- cbind(id=rownames(data),data) -#drop warmups -sample_start <- fit@sim$warmup2[itt]+1 -sample_size <- fit@sim$n_save[itt] -data <- data[sample_start:sample_size,] -#add chain -data["chain"] <- itt - - -#melt it -id.vars <- c("id","chain") -data <- melt(data,id.vars) - -#extract group and parameter identifiers -a <- t(as.data.frame(mapply(extract_nums,as.character(data$variable)))) -a <- as.data.frame(a) -#add group and parameter identifiers back in -data["group"] <- a[1] -data["group"] <- as.integer(data$group) -data["parameter"] <- a[2] - - - - -ggplot(subset(data,parameter <=2), aes(x=value)) + - stat_density() + - xlim(-2,3) + - facet_grid(group ~ parameter, - labeller = labeller(group=group_list,parameter=parameter_list) - ) + - scale_y_discrete(labels = NULL) + - theme(strip.text.y = element_text(angle = 0)) -