From 7a1090b6036d04c9faac1cb070d322f6a85a5823 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Tue, 5 Nov 2024 01:43:40 +0000 Subject: [PATCH] differences for PR #6 --- config.yaml | 85 - ...her-topics-rendered-unnamed-chunk-14-1.png | Bin 21229 -> 21221 bytes ...her-topics-rendered-unnamed-chunk-15-1.png | Bin 19593 -> 23868 bytes ...her-topics-rendered-unnamed-chunk-18-1.png | Bin 10359 -> 10363 bytes gaussian-processes.md | 20 +- md5sum.txt | 36 +- other-topics.md | 92 +- renv.lock | 2470 ----------------- stan.md | 20 +- 9 files changed, 108 insertions(+), 2615 deletions(-) delete mode 100644 config.yaml delete mode 100644 renv.lock diff --git a/config.yaml b/config.yaml deleted file mode 100644 index 7503294..0000000 --- a/config.yaml +++ /dev/null @@ -1,85 +0,0 @@ -#------------------------------------------------------------ -# Values for this lesson. -#------------------------------------------------------------ - -# Which carpentry is this (swc, dc, lc, or cp)? -# swc: Software Carpentry -# dc: Data Carpentry -# lc: Library Carpentry -# cp: Carpentries (to use for instructor training for instance) -# incubator: The Carpentries Incubator -carpentry: 'incubator' - -# Overall title for pages. -title: 'Introduction to Probabilistic Programming in R' - -# Date the lesson was created (YYYY-MM-DD, this is empty by default) -created: - -# Comma-separated list of keywords for the lesson -keywords: 'software, data, lesson, The Carpentries' - -# Life cycle stage of the lesson -# possible values: pre-alpha, alpha, beta, stable -life_cycle: 'pre-alpha' - -# License of the lesson materials (recommended CC-BY 4.0) -license: 'CC-BY 4.0' - -# Link to the source repository for this lesson -source: 'https://github.com/carpentries-incubator/statistical-probabilistic-programming-r' - -# Default branch of your lesson -branch: 'main' - -# Who to contact if there are any issues -contact: 'velait@utu.fi' - -# Navigation ------------------------------------------------ -# -# Use the following menu items to specify the order of -# individual pages in each dropdown section. Leave blank to -# include all pages in the folder. -# -# Example ------------- -# -# episodes: -# - introduction.md -# - first-steps.md -# -# learners: -# - setup.md -# -# instructors: -# - instructor-notes.md -# -# profiles: -# - one-learner.md -# - another-learner.md - -# Order of episodes in your lesson -episodes: -- bayesian-statistics.Rmd -- stan.Rmd -- mcmc.Rmd -- hierarchical-models.Rmd -- model-comparison.Rmd -- gaussian-processes.Rmd -- other-topics.Rmd -- exercises.Rmd - -# Information for Learners -learners: - -# Information for Instructors -instructors: - -# Learner Profiles -profiles: - -# Customisation --------------------------------------------- -# -# This space below is where custom yaml items (e.g. pinning -# sandpaper and varnish versions) should live - - diff --git a/fig/other-topics-rendered-unnamed-chunk-14-1.png b/fig/other-topics-rendered-unnamed-chunk-14-1.png index 5fe8a10c730063e450b287be4a46c177e7ebddbc..3c28138e39557705bf6a0a1c027292ac788a75a1 100644 GIT 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vector[N] Y; // response variable - array[N] int cens; // indicates censoring + // censoring indicator: 0 = event, 1 = right, -1 = left, 2 = interval censored + array[N] int cens; int K; // number of population-level effects matrix[N, K] X; // population-level design matrix int Kc; // number of population-level effects after centering @@ -588,8 +621,28 @@ data { int prior_only; // should the likelihood be ignored? } transformed data { + // indices of censored data + int Nevent = 0; + int Nrcens = 0; + int Nlcens = 0; + array[N] int Jevent; + array[N] int Jrcens; + array[N] int Jlcens; matrix[N, Kc] Xc; // centered version of X without an intercept vector[Kc] means_X; // column means of X before centering + // collect indices of censored data + for (n in 1:N) { + if (cens[n] == 0) { + Nevent += 1; + Jevent[Nevent] = n; + } else if (cens[n] == 1) { + Nrcens += 1; + Jrcens[Nrcens] = n; + } else if (cens[n] == -1) { + Nlcens += 1; + Jlcens[Nlcens] = n; + } + } for (i in 2:K) { means_X[i - 1] = mean(X[, i]); Xc[, i - 1] = X[, i] - means_X[i - 1]; @@ -598,7 +651,8 @@ transformed data { parameters { vector[Kc] b; // regression coefficients real Intercept; // temporary intercept for centered predictors - simplex[Kbhaz] sbhaz; // baseline coefficients + // baseline hazard coefficients + simplex[Kbhaz] sbhaz; } transformed parameters { real lprior = 0; // prior contributions to the log posterior @@ -616,16 +670,10 @@ model { // initialize linear predictor term vector[N] mu = rep_vector(0.0, N); mu += Intercept + Xc * b; - for (n in 1:N) { - // special treatment of censored data - if (cens[n] == 0) { - target += cox_log_lpdf(Y[n] | mu[n], bhaz[n], cbhaz[n]); - } else if (cens[n] == 1) { - target += cox_log_lccdf(Y[n] | mu[n], bhaz[n], cbhaz[n]); - } else if (cens[n] == -1) { - target += cox_log_lcdf(Y[n] | mu[n], bhaz[n], cbhaz[n]); - } - } + // vectorized log-likelihood contributions of censored data + target += cox_log_lpdf(Y[Jevent[1:Nevent]] | mu[Jevent[1:Nevent]], bhaz[Jevent[1:Nevent]], cbhaz[Jevent[1:Nevent]]); + target += cox_log_lccdf(Y[Jrcens[1:Nrcens]] | mu[Jrcens[1:Nrcens]], bhaz[Jrcens[1:Nrcens]], cbhaz[Jrcens[1:Nrcens]]); + target += cox_log_lcdf(Y[Jlcens[1:Nlcens]] | mu[Jlcens[1:Nlcens]], bhaz[Jlcens[1:Nlcens]], cbhaz[Jlcens[1:Nlcens]]); } // priors including constants target += lprior; diff --git a/renv.lock b/renv.lock deleted file mode 100644 index 3ae7bdf..0000000 --- a/renv.lock +++ /dev/null @@ -1,2470 +0,0 @@ -{ - "R": { - "Version": "4.4.1", - "Repositories": [ - { - "Name": "carpentries", - "URL": "https://carpentries.r-universe.dev" - }, - { - "Name": "carpentries_archive", - "URL": "https://carpentries.github.io/drat" - }, - { - "Name": "CRAN", - "URL": "https://cran.rstudio.com" - } - ] - }, - "Packages": { - "BH": { - "Package": "BH", - "Version": "1.84.0-0", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "a8235afbcd6316e6e91433ea47661013" - }, - "Brobdingnag": { - "Package": "Brobdingnag", - "Version": 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"Repository": "CRAN", - "Requirements": [ - "grDevices", - "stats", - "tools" - ], - "Hash": "317a0538d32f4a009658bcedb7923f4b" - }, - "xml2": { - "Package": "xml2", - "Version": "1.3.6", - "Source": "Repository", - "Repository": "CRAN", - "Requirements": [ - "R", - "cli", - "methods", - "rlang" - ], - "Hash": "1d0336142f4cd25d8d23cd3ba7a8fb61" - }, - "yaml": { - "Package": "yaml", - "Version": "2.3.8", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "29240487a071f535f5e5d5a323b7afbd" - } - } -} diff --git a/stan.md b/stan.md index 7f5adaa..ea187ba 100644 --- a/stan.md +++ b/stan.md @@ -226,7 +226,7 @@ post-warmup draws per chain=1000, total post-warmup draws=4000. theta 0.16 0.00 0.05 0.07 0.12 0.15 0.18 0.26 1545 1 lp__ -22.80 0.02 0.69 -24.75 -22.93 -22.53 -22.37 -22.33 1987 1 -Samples were drawn using NUTS(diag_e) at Tue Sep 24 11:44:53 2024. +Samples were drawn using NUTS(diag_e) at Tue Nov 5 01:14:21 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). @@ -385,9 +385,9 @@ Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) Chain 1: -Chain 1: Elapsed Time: 0.008 seconds (Warm-up) -Chain 1: 0.007 seconds (Sampling) -Chain 1: 0.015 seconds (Total) +Chain 1: Elapsed Time: 0.009 seconds (Warm-up) +Chain 1: 0.008 seconds (Sampling) +Chain 1: 0.017 seconds (Total) Chain 1: SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2). @@ -410,9 +410,9 @@ Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 2: Iteration: 2000 / 2000 [100%] (Sampling) Chain 2: -Chain 2: Elapsed Time: 0.009 seconds (Warm-up) +Chain 2: Elapsed Time: 0.011 seconds (Warm-up) Chain 2: 0.008 seconds (Sampling) -Chain 2: 0.017 seconds (Total) +Chain 2: 0.019 seconds (Total) Chain 2: SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3). @@ -436,8 +436,8 @@ Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 3: Iteration: 2000 / 2000 [100%] (Sampling) Chain 3: Chain 3: Elapsed Time: 0.009 seconds (Warm-up) -Chain 3: 0.007 seconds (Sampling) -Chain 3: 0.016 seconds (Total) +Chain 3: 0.008 seconds (Sampling) +Chain 3: 0.017 seconds (Total) Chain 3: SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4). @@ -460,9 +460,9 @@ Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 4: Iteration: 2000 / 2000 [100%] (Sampling) Chain 4: -Chain 4: Elapsed Time: 0.01 seconds (Warm-up) +Chain 4: Elapsed Time: 0.009 seconds (Warm-up) Chain 4: 0.008 seconds (Sampling) -Chain 4: 0.018 seconds (Total) +Chain 4: 0.017 seconds (Total) Chain 4: ```