Package 'chkptstanr' reference manual

Title:	Checkpoint MCMC Sampling with 'Stan'
Description:	Fit Bayesian models in Stan <doi: 10.18637/jss.v076.i01> 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' models can be fitted, or the popular package 'brms' <doi: 10.18637/jss.v080.i01> can be used to generate the 'Stan' code. This package is fully compatible with the R packages 'brms', 'posterior', 'cmdstanr', and 'bayesplot'.
Authors:	Vencislav Popov [aut, cre, cph], Donald Williams [aut], Tyler Matta [aut], NWEA [cph]
Maintainer:	Vencislav Popov <[email protected]>
License:	Apache License 2.0 \| file LICENSE
Version:	0.2.0
Built:	2024-10-25 05:18:48 UTC
Source:	https://github.com/venpopov/chkptstanr

Checkpoint Sampling: brms

Description

Fit Bayesian generalized (non-)linear multivariate multilevel models using brms with checkpointing.

Usage

chkpt_brms(
  formula,
  data,
  iter_adaptation = 150,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 100,
  parallel_chains = 4,
  threads_per = 1,
  chkpt_progress = TRUE,
  control = NULL,
  seed = 1,
  stop_after = NULL,
  reset = FALSE,
  path,
  ...
)
chkpt_brms(
  formula,
  data,
  iter_adaptation = 150,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 100,
  parallel_chains = 4,
  threads_per = 1,
  chkpt_progress = TRUE,
  control = NULL,
  seed = 1,
  stop_after = NULL,
  reset = FALSE,
  path,
  ...
)

Arguments

`formula`	An object of class `formula`, `brmsformula`, or `brms{mvbrmsformula}`. Further information can be found in `brmsformula`.
`data`	An object of class `data.frame` (or one that can be coerced to that class) containing data of all variables used in the model.
`iter_adaptation`	(positive integer) The number of iterations in the initial warmup, which are used for the adaptation of the step size and inverse mass matrix. This is equivalent to the traditional warmup stage. Checkpointing will begin only after this stage is complete.
`iter_warmup`	(positive integer) The number of warmup iterations to run per chain after the adaptation stage (defaults to 1000). During this stage the step size and inverse mass matrix are fixed to the values found during the adaptation stage. There is no further adaptation performed.
`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).
`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.
`stop_after`	(positive integer). The number of iterations to sample before stopping. If `NULL`, then all iterations are sampled (defaults to `NULL`). Note that sampling will stop at the end of the first checkpoint which has an iteration number greater than or equal to `stop_after`.
`reset`	logical. Should the checkpointing be reset? If `TRUE`, then the model will begin sampling from the beginning (defaults to `FALSE`). WARNING: This will remove all previous checkpointing information (see `reset_checkpoints()`). If the model is unchanged and previously compiled, sampling will begin without recompiling the model.
`path`	Character string. The path to the folder, that is used for saving the checkpoints (see Details). You can provide either a relative path to the current working directory or a full path. You no longer need to create the folder, as this is done automatically.
`...`	Any additional arguments passed to `brm`, including, but not limited to, user-defined prior distributions, the `brmsfamily` (e.g., `family = poisson())`, data2, custom_families, etc.

Value

An object of class brmsfit

Note

A folder specified by path is created with four subfolders:

cmd_output: The cmdstanr output_files (one for each checkpoint and chain).
cp_info: Mass matrix, step size, and initial values for next checkpoint (last iteration from previous checkpoint).
cp_samples: Samples from the posterior distribution (post warmup)
stan_model: Complied Stan model

Examples

## Not run: 
library(brms)
library(cmdstanr)


# "random" intercept
fit1 <- chkpt_brms(
  bf(
    formula = count ~ zAge + zBase * Trt + (1 | patient),
    family = poisson()
  ),
  data = epilepsy, ,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 250,
  path = "chkpt_folder_fit1"
)

# brmsfit output
fit1


# remove "random" intercept (for model comparison)
fit2 <- chkpt_brms(
  bf(
    formula = count ~ zAge + zBase * Trt,
    family = poisson()
  ),
  data = epilepsy, ,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 250,
  path = "chkpt_folder_fit2"
)

# brmsfit output
fit2

# compare models
loo(fit1, fit2)


# priors
bprior <- prior(constant(1), class = "b") +
  prior(constant(2), class = "b", coef = "zBase") +
  prior(constant(0.5), class = "sd")

# fit model
fit3 <-
  chkpt_brms(
    bf(
      formula = count ~ zAge + zBase + (1 | patient),
      family = poisson()
    ),
    data = epilepsy,
    path = "chkpt_folder_fit3",
    prior = bprior,
    iter_warmup = 1000,
    iter_sampling = 1000,
    iter_per_chkpt = 250,
  )

# check priors
prior_summary(fit3)

## End(Not run)
## Not run: 
library(brms)
library(cmdstanr)


# "random" intercept
fit1 <- chkpt_brms(
  bf(
    formula = count ~ zAge + zBase * Trt + (1 | patient),
    family = poisson()
  ),
  data = epilepsy, ,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 250,
  path = "chkpt_folder_fit1"
)

# brmsfit output
fit1


# remove "random" intercept (for model comparison)
fit2 <- chkpt_brms(
  bf(
    formula = count ~ zAge + zBase * Trt,
    family = poisson()
  ),
  data = epilepsy, ,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 250,
  path = "chkpt_folder_fit2"
)

# brmsfit output
fit2

# compare models
loo(fit1, fit2)


# priors
bprior <- prior(constant(1), class = "b") +
  prior(constant(2), class = "b", coef = "zBase") +
  prior(constant(0.5), class = "sd")

# fit model
fit3 <-
  chkpt_brms(
    bf(
      formula = count ~ zAge + zBase + (1 | patient),
      family = poisson()
    ),
    data = epilepsy,
    path = "chkpt_folder_fit3",
    prior = bprior,
    iter_warmup = 1000,
    iter_sampling = 1000,
    iter_per_chkpt = 250,
  )

# check priors
prior_summary(fit3)

## End(Not run)

Checkpoint Setup

Description

Deterimine the number of checkpoints for the warmup and sampling, given the desired number of iterations for each and the iterations per checkpoint.

Usage

chkpt_setup(iter_sampling, iter_warmup, iter_per_chkpt)
chkpt_setup(iter_sampling, iter_warmup, iter_per_chkpt)

Arguments

`iter_sampling`	(positive integer) The number of post-warmup iterations to run per chain. Note: in the CmdStan User's Guide this is referred to as num_samples.
`iter_warmup`	(positive integer) The number of warmup iterations to run per chain. Note: in the CmdStan User's Guide this is referred to as num_warmup.
`iter_per_chkpt`	(positive integer) The number of iterations per check point.

Value

A list with the following:

warmup_chkpts: Number of warmup checkpoints
sample_chkpts: Number of sampling checkpoints
total_chkpts: Total number of checkpoints (warmup_chkpts + sample_chkpts)
iter_per_chkpt: Iterations per checkpoint

Examples

chkpt_setup <- chkpt_setup(
  iter_sampling = 5000,
  iter_warmup = 2000,
  iter_per_chkpt = 10
)

chkpt_setup
chkpt_setup <- chkpt_setup(
  iter_sampling = 5000,
  iter_warmup = 2000,
  iter_per_chkpt = 10
)

chkpt_setup

Checkpoint Sampling: Stan

Description

Fit Bayesian models using Stan with checkpointing.

Usage

chkpt_stan(
  model_code,
  data,
  iter_adaptation = 150,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 100,
  parallel_chains = 4,
  threads_per = 1,
  chkpt_progress = TRUE,
  control = NULL,
  seed = 1,
  stop_after = NULL,
  reset = FALSE,
  path,
  ...
)
chkpt_stan(
  model_code,
  data,
  iter_adaptation = 150,
  iter_warmup = 1000,
  iter_sampling = 1000,
  iter_per_chkpt = 100,
  parallel_chains = 4,
  threads_per = 1,
  chkpt_progress = TRUE,
  control = NULL,
  seed = 1,
  stop_after = NULL,
  reset = FALSE,
  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_adaptation`	(positive integer) The number of iterations in the initial warmup, which are used for the adaptation of the step size and inverse mass matrix. This is equivalent to the traditional warmup stage. Checkpointing will begin only after this stage is complete.
`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).
`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.
`stop_after`	(positive integer). The number of iterations to sample before stopping. If `NULL`, then all iterations are sampled (defaults to `NULL`). Note that sampling will stop at the end of the first checkpoint which has an iteration number greater than or equal to `stop_after`.
`reset`	logical. Should the checkpointing be reset? If `TRUE`, then the model will begin sampling from the beginning (defaults to `FALSE`). WARNING: This will remove all previous checkpointing information (see `reset_checkpoints()`). If the model is unchanged and previously compiled, sampling will begin without recompiling the model.
`path`	Character string. The path to the folder, that is used for saving the checkpoints (see Details). You can provide either a relative path to the current working directory or a full path. You no longer need to create the folder, as this is done automatically.
`...`	Currently ignored.

Value

An objet of class chkpt_stan

Examples

## Not run: 

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 = "chkpt_folder_fit1"
)

draws <- combine_chkpt_draws(object = fit1)

posterior::summarise_draws(draws)


# eight schools example

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 = "chkpt_folder_fit2"
)

draws <- combine_chkpt_draws(object = fit2)

posterior::summarise_draws(draws)

## End(Not run)
## Not run: 

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 = "chkpt_folder_fit1"
)

draws <- combine_chkpt_draws(object = fit1)

posterior::summarise_draws(draws)


# eight schools example

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 = "chkpt_folder_fit2"
)

draws <- combine_chkpt_draws(object = fit2)

posterior::summarise_draws(draws)

## End(Not run)

Combine Checkpoint Draws

Description

Combine Checkpoint Draws

Usage

combine_chkpt_draws(object, ...)
combine_chkpt_draws(object, ...)

Arguments

`object`	An object of class `brmsfit` or `chkpt_stan`.
`...`	Currently ignored.

Value

An object of class draws_array.

Examples

 
## Not run:   
path <- create_folder(folder_name = "chkpt_folder_fit1")

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)

draws

## End(Not run)
## Not run:   
path <- create_folder(folder_name = "chkpt_folder_fit1")

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)

draws

## End(Not run)

Create Folder for Checkpointing (Deprecated)

Description

create_folder() is deprected. Provide a path directly in chkpt_brms()', or chkpt_stan() instead and a folder will be created automatically.'

Usage

create_folder(folder_name = "cp_folder", path = NULL)
create_folder(folder_name = "cp_folder", path = NULL)

Arguments

`folder_name`	Character string. Desired name for the 'parent' folder (defaults to `cp_folder`).
`path`	Character string, when specified. Defaults to `NULL`, which then makes the folder in the working directory.

Value

the path to the main parent folder containing the four subfolders. This path should be used as the path argument in chkpt_brms. If return_relative = TRUE, the relative path to the current working directory is returned. If path is specified or return_relative = FALSE, the full path is returned.

——————-

Create the folder for checkingpointing, which will 'house' additional folders for the .stan model, checkpointing information, and draws from the posterior distribution.

Note

This creates a directory with four folders:

cmd_output: The cmdstanr output_files (one for each checkpoint and chain).
cp_info: Mass matrix, step size, and initial values for next checkpoint (last iteration from previous checkpoint).
cp_samples: Samples from the posterior distribution (post warmup)
stan_model: Complied Stan model

Examples

# create initial folder
path <- create_folder(folder_name = 'cp_folder')
path
unlink('cp_folder', recursive = TRUE) # remove folder

# remove folder
unlink('cp_folder', recursive = TRUE)
identical(dir(path), character(0))

# repeat - no warning
path <- create_folder(folder_name = 'cp_folder')

# repeat - warning, but folders are kept
path <- create_folder(folder_name = 'cp_folder')
identical(dir(path), c('cmd_output', 'cp_info', 'cp_samples', 'stan_model'))

unlink('cp_folder', recursive = TRUE)

# specify nested folder
path <- create_folder(folder_name = 'nested_folder/cp_folder')
path
unlink('nested_folder', recursive = TRUE) # remove folder
# create initial folder
path <- create_folder(folder_name = 'cp_folder')
path
unlink('cp_folder', recursive = TRUE) # remove folder

# remove folder
unlink('cp_folder', recursive = TRUE)
identical(dir(path), character(0))

# repeat - no warning
path <- create_folder(folder_name = 'cp_folder')

# repeat - warning, but folders are kept
path <- create_folder(folder_name = 'cp_folder')
identical(dir(path), c('cmd_output', 'cp_info', 'cp_samples', 'stan_model'))

unlink('cp_folder', recursive = TRUE)

# specify nested folder
path <- create_folder(folder_name = 'nested_folder/cp_folder')
path
unlink('nested_folder', recursive = TRUE) # remove folder

Extract Draws from `CmdStanMCMC` Objects

Description

A convenience function for extracting the draws from a CmdStanMCMC object.

Usage

extract_chkpt_draws(object, phase)
extract_chkpt_draws(object, phase)

Arguments

`object`	An object of class `CmdStanMCMC`.
`phase`	Character string. Which phase during checkpointing? The options included `warmup` and `sample`. The latter extracts the draws with `inc_warmup = FALSE`, which is the default in `draws`

Value

A 3-D draws_array object (iteration x chain x variable).

Note

This can be used to extract the draws in general by setting phase = "sample" which then only includes the post-warmup draws.

Examples

## Not run: 
 library(cmdstanr)

# eight schools example
fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")

drws <- extract_chkpt_draws(object = fit_schools_ncp_mcmc,
                            phase = "sample")

# compare to cmdstanr
all.equal(drws, fit_schools_ncp_mcmc$draws())

## End(Not run)
## Not run: 
 library(cmdstanr)

# eight schools example
fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")

drws <- extract_chkpt_draws(object = fit_schools_ncp_mcmc,
                            phase = "sample")

# compare to cmdstanr
all.equal(drws, fit_schools_ncp_mcmc$draws())

## End(Not run)

Extract HMC Sampler Information

Description

Extract the inverse metric and step size adaption from CmdStanMCMC objects.

Usage

extract_hmc_info(object)
extract_hmc_info(object)

Arguments

object

An object of class CmdStanMCMC

Value

A list including

inv_metric: Inverse metric for each chain (with matrix = FALSE).
step_size_adapt: Step size adaptation for each chain.

Note

This is primarily used internally.

Examples


## Not run: 

library(cmdstanr)

fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")

extract_hmc_info(fit_schools_ncp_mcmc)


## End(Not run)
## Not run: 

library(cmdstanr)

fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")

extract_hmc_info(fit_schools_ncp_mcmc)


## End(Not run)

Extract Stan State

Description

Extract Stan State

Usage

extract_stan_state(object, phase)
extract_stan_state(object, phase)

Arguments

`object`	An object of class `cmdstanr`
`phase`	Character string indicating the current phase. Options include `wormup` and `sample`/

Value

A list containing the inverse metric, step size, and last MCMC draw (to be used as the initial value for the next checkpoint)

Examples

## Not run: 
library(cmdstanr)

# eight schools example
fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")

extract_stan_state(fit_schools_ncp_mcmc, "sample")

## End(Not run)
## Not run: 
library(cmdstanr)

# eight schools example
fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")

extract_stan_state(fit_schools_ncp_mcmc, "sample")

## End(Not run)

Make `brmsfit` Object

Description

This is primarily used internally, wherein the output files of multiple cmdstanr fits are combined into a single brmsfit object. object is converted into a brmsfit object.

Usage

make_brmsfit(formula, data, path, ...)
make_brmsfit(formula, data, path, ...)

Arguments

`formula`	A `brms` formula used to generate the checkpoints
`data`	A data frame used to generate the checkpoints
`path`	Character string. The path to the folder, that is used for saving the checkpoints.
`...`	Additional arguments to be passed to `brm`.

Value

An object of class brmsfit

Note

This is primarily an internal function that constructs a brmsfit object.

Print `chkpt_brms` Objects

Description

Print chkpt_brms Objects

Usage

## S3 method for class 'chkpt_brms'
print(x, ...)
## S3 method for class 'chkpt_brms'
print(x, ...)

Arguments

`x`	Object of class `chkpt_brms`
`...`	Currently ignored

Value

No return value, and used to print the chkpt_brms object.

Note

This function mainly avoids printing out a list, and it is only used when brmsfit = "FALSE" in chkpt_brms.

Typically, after fitting, the posterior draws should be summarized with combine_chkpt_draws (assuming brmsfit = "FALSE").

Print `chkpt_setup` Object

Description

Print chkpt_setup Object

Usage

## S3 method for class 'chkpt_setup'
print(x, ...)
## S3 method for class 'chkpt_setup'
print(x, ...)

Arguments

`x`	An object of class `chkpt_setup`.
`...`	Currently ignored.

Value

No return value, and used to print the chkpt_setup object.

Examples

chkpt_setup <- chkpt_setup(
  iter_sampling = 5000,
  iter_warmup = 2000,
  iter_per_chkpt = 10
)


chkpt_setup
chkpt_setup <- chkpt_setup(
  iter_sampling = 5000,
  iter_warmup = 2000,
  iter_per_chkpt = 10
)


chkpt_setup

Delete Checkpoint Folders containing samples, keep the model

Description

Deletes all checkpoint files and folders under path except for stan_model/model.stan and stan_model/model.exe. This allows you to restart the sampling from 0 without recompiling the model.

Usage

reset_checkpoints(path, reset = TRUE, recompile = FALSE)
reset_checkpoints(path, reset = TRUE, recompile = FALSE)

Arguments

`path`	(character) The path to the checkpoint folder.
`reset`	(logical) If TRUE, only the checkpoint folders are deleted
`recompile`	(logical) If TRUE, the entire folder is deleted allowing for a fresh start. If both `reset` and `recompile` are `FALSE`, nothing is done.

Package 'chkptstanr'

Help Index

Checkpoint Sampling: brms

Description

Usage

Arguments

Value

Note

Examples

Checkpoint Setup

Description

Usage

Arguments

Value

Examples

Checkpoint Sampling: Stan

Description

Usage

Arguments

Value

Examples

Combine Checkpoint Draws

Description

Usage

Arguments

Value

Examples

Create Folder for Checkpointing (Deprecated)

Description

Usage

Arguments

Value

——————-

Note

Examples

Extract Draws from CmdStanMCMC Objects

Description

Usage

Arguments

Value

Note

Examples

Extract HMC Sampler Information

Description

Usage

Arguments

Value

Note

Examples

Extract Stan State

Description

Usage

Arguments

Value

Examples

Make brmsfit Object

Description

Usage

Arguments

Value

Note

Print chkpt_brms Objects

Description

Usage

Arguments

Value

Note

Print chkpt_setup Object

Description

Usage

Arguments

Value

Examples

Delete Checkpoint Folders containing samples, keep the model

Description

Usage

Arguments

Extract Draws from `CmdStanMCMC` Objects

Make `brmsfit` Object

Print `chkpt_brms` Objects

Print `chkpt_setup` Object