chkptstanr (version 0.1.1)

chkpt_stan: Checkpoint Sampling: Stan

Description

Fit Bayesian models using Stan with checkpointing.

Usage

chkpt_stan(
  model_code,
  data,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 100,
  iter_typical = 150,
  parallel_chains = 2,
  threads_per = 1,
  chkpt_progress = TRUE,
  control = NULL,
  seed = 1,
  path,
  ...
)

Arguments

model_code

Character string corresponding to the Stan model.

data

A named list of R objects (like for RStan). Further details can be found in sample.

iter_warmup

(positive integer) The number of warmup iterations to run per chain (defaults to 1000).

iter_sampling

(positive integer) The number of post-warmup iterations to run per chain (defaults to 1000).

iter_per_chkpt

(positive integer). The number of iterations per checkpoint. Note that iter_sampling is divided by iter_per_chkpt to determine the number of checkpoints. This must result in an integer (if not, there will be an error).

iter_typical

(positive integer) The number of iterations in the initial warmup, which finds the so-called typical set. This is an initial phase, and not included in iter_warmup. Note that a large enough value is required to ensure converge (defaults to 150).

parallel_chains

(positive integer) The maximum number of MCMC chains to run in parallel. If parallel_chains is not specified then the default is to look for the option mc.cores, which can be set for an entire R session by options(mc.cores=value). If the mc.cores option has not been set then the default is 1.

threads_per

(positive integer) Number of threads to use in within-chain parallelization (defaults to 1).

chkpt_progress

logical. Should the chkptstanr progress be printed (defaults to TRUE) ? If set to FALSE, the standard cmdstanr progress bar is printed for each checkpoint (which does not actually keep track of checkpointing progress)

control

A named list of parameters to control the sampler's behavior. It defaults to NULL so all the default values are used. For a comprehensive overview see stan.

seed

(positive integer). The seed for random number generation to make results reproducible.

path

Character string. The path to the folder, that is used for saving the checkpoints.

...

Currently ignored.

Value

An objet of class chkpt_stan

Examples

# NOT RUN {
# path for storing checkpoint info
path <- create_folder(folder_name = "chkpt_folder_fit1")

stan_code <- make_stancode(bf(formula = count ~ zAge + zBase * Trt + (1|patient),
                              family = poisson()),
                           data = epilepsy)
stan_data <- make_standata(bf(formula = count ~ zAge + zBase * Trt + (1|patient),
                              family = poisson()),
                           data = epilepsy)

# "random" intercept
fit1 <- chkpt_stan(model_code = stan_code, 
                   data = stan_data,
                   iter_warmup = 1000,
                   iter_sampling = 1000,
                   iter_per_chkpt = 250,
                   path = path)

draws <- combine_chkpt_draws(object = fit1)

posterior::summarise_draws(draws)


# eight schools example
 
# path for storing checkpoint info
path <- create_folder(parent_folder = "chkpt_folder_fit2")

stan_code <- "
data {
 int<lower=0> n;
  real y[n]; 
  real<lower=0> sigma[n]; 
}
parameters {
  real mu;
  real<lower=0> tau; 
  vector[n] eta; 
}
transformed parameters {
  vector[n] theta; 
  theta = mu + tau * eta; 
}
model {
  target += normal_lpdf(eta | 0, 1); 
  target += normal_lpdf(y | theta, sigma);  
}
"
stan_data <- schools.data <- list(
  n = 8,
  y = c(28,  8, -3,  7, -1,  1, 18, 12),
  sigma = c(15, 10, 16, 11,  9, 11, 10, 18)
)

fit2 <- chkpt_stan(model_code = stan_code, 
                   data = stan_data,
                   iter_warmup = 1000,
                   iter_sampling = 1000,
                   iter_per_chkpt = 250,
                   path = path)

draws <- combine_chkpt_draws(object = fit2)

posterior::summarise_draws(draws)
# }