The goal of chkptstanr is to fit Bayesian models in Stan with checkpointing, that is, the ability to stop the MCMC sampler at will, and then pick right back up where the MCMC sampler left off. Custom Stan model can be fitted, or the popular package brms can be used to generate the Stan code. This package is fully compatible with the R packages brms, posterior, cmdstanr, and bayesplot.
You can install the development version of chkptstanr like so:
These packages are needed.
The illustrative data are bundled with R package lme4.
data("VerbAgg")
# 20 subjects
dat_sub <- subset(VerbAgg, id %in% 1:20)
# numeric outcome
dat_sub$y <- ifelse(dat_sub$r2 == "Y", 1, 0)To demonstrate how to use chkptstanr, we fit a Rasch model with I fixed item effects and random person effects. This can be done with familiar lme4 style syntax, i.e.,
The additional overhead is to create a folder that will store the checkpoints, i.e.,
The primary use of chkptstanr is to sample from the posterior distribution, while having the option of starting and stopping the sampler at will.
To make this clear, we stopped the following after 2 checkpoints.
fit_m1 <- chkpt_brms(formula = m1,
data = dat_sub,
path = path,
iter_warmup = 1000,
iter_sampling = 1000,
iter_per_chkpt = 250)
#> Compiling Stan program...
#> Initial Warmup (Typical Set)
#> Chkpt: 1 / 8; Iteration: 250 / 2000 (warmup)
#> Chkpt: 2 / 8; Iteration: 500 / 2000 (warmup)To start at the next checkpoint, rerun the same code.
fit_m1 <- chkpt_brms(formula = m1,
data = dat_sub,
path = path,
iter_warmup = 1000,
iter_sampling = 1000,
iter_per_chkpt = 250)
#> Sampling next checkpoint
#> Chkpt: 3 / 8; Iteration: 750 / 2000 (warmup)
#> Chkpt: 4 / 8; Iteration: 1000 / 2000 (warmup)
#> Chkpt: 5 / 8; Iteration: 1250 / 2000 (sample)
#> Chkpt: 6 / 8; Iteration: 1500 / 2000 (sample)
#> Chkpt: 7 / 8; Iteration: 1750 / 2000 (sample)
#> Chkpt: 8 / 8; Iteration: 2000 / 2000 (sample)
#> Checkpointing completeA key advantage of chkpt_brms is that it returns a brmsfit object, as seen when printing the summary output.
fit_m1
#> Family: binomial
#> Links: mu = logit
#> Formula: y ~ 0 + item + (1 | id)
#> Data: data (Number of observations: 480)
#> Draws: 2 chains, each with iter = 1000; warmup = 0; thin = 1;
#> total post-warmup draws = 2000
#> Group-Level Effects:
#> ~id (Number of levels: 20)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 1.68 0.36 1.07 2.46 1.00 683 742
#> Population-Level Effects:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> itemS1WantCurse 1.53 0.78 0.07 3.09 1.00 883 1034
#> itemS1WantScold -0.38 0.64 -1.61 0.86 1.00 743 888
#> itemS1WantShout 0.49 0.64 -0.69 1.77 1.00 742 1084
#> itemS2WantCurse 2.02 0.80 0.51 3.61 1.00 837 939
#> itemS2WantScold -0.65 0.64 -1.97 0.58 1.00 854 1001
#> itemS2WantShout 0.51 0.67 -0.80 1.81 1.00 696 931
#> itemS3WantCurse 1.16 0.72 -0.24 2.61 1.00 764 1169
#> itemS3WantScold -1.98 0.76 -3.54 -0.59 1.00 799 1122
#> itemS3WantShout -0.08 0.64 -1.33 1.21 1.00 725 1109
#> itemS4wantCurse -0.09 0.65 -1.41 1.12 1.00 736 1007
#> itemS4WantScold -3.07 0.93 -5.07 -1.44 1.00 1065 1131
#> itemS4WantShout -3.07 0.93 -5.01 -1.41 1.00 1095 1250
#> itemS1DoCurse 2.57 0.95 0.78 4.55 1.00 1125 988
#> itemS1DoScold 0.51 0.67 -0.80 1.85 1.00 738 1085
#> itemS1DoShout 0.21 0.64 -0.97 1.47 1.00 753 1241
#> itemS2DoCurse 1.97 0.80 0.49 3.61 1.00 828 1088
#> itemS2DoScold -0.09 0.64 -1.35 1.18 1.00 759 991
#> itemS2DoShout -1.24 0.67 -2.58 0.08 1.00 778 903
#> itemS3DoCurse 0.20 0.66 -1.16 1.50 1.00 657 1091
#> itemS3DoScold -2.44 0.82 -4.22 -0.98 1.00 976 1012
#> itemS3DoShout -3.07 0.97 -5.13 -1.36 1.00 999 1186
#> itemS4DoCurse 0.20 0.65 -1.03 1.48 1.00 730 1307
#> itemS4DoScold -0.37 0.63 -1.66 0.88 1.00 709 1064
#> itemS4DoShout -2.44 0.81 -4.14 -0.97 1.00 799 1077
#> Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).