Help for package jagstargets

Title:

Targets for JAGS Pipelines

Description:

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'jagstargets' R package is leverages 'targets' and 'R2jags' to ease this burden. 'jagstargets' makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. For the underlying methodology, please refer to the documentation of 'targets' <doi:10.21105/joss.02959> and 'JAGS' (Plummer 2003) https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf.

Version:

1.2.2

License:

MIT + file LICENSE

URL:

https://docs.ropensci.org/jagstargets/, https://github.com/ropensci/jagstargets

BugReports:

https://github.com/ropensci/jagstargets/issues

Depends:

R (≥ 3.5.0)

Imports:

coda (≥ 0.19.4), fst (≥ 0.9.2), posterior (≥ 1.0.1), purrr (≥ 0.3.4), qs2, R2jags (≥ 0.6.1), rjags (≥ 4.10), rlang (≥ 0.4.10), secretbase (≥ 0.4.0), stats, targets (≥ 1.6.0), tarchetypes (≥ 0.8.0), tibble (≥ 3.0.1), tidyselect, tools, utils, withr (≥ 2.1.2),

Suggests:

dplyr (≥ 1.0.2), fs (≥ 1.5.0), knitr (≥ 1.30), qs (≥ 0.23.2), R.utils (≥ 2.10.1), rmarkdown (≥ 2.3), testthat (≥ 3.0.0), tidyr (≥ 1.1.2), visNetwork (≥ 2.0.9)

SystemRequirements:

JAGS 4.x.y (https://mcmc-jags.sourceforge.net)

Encoding:

UTF-8

Language:

en-US

RoxygenNote:

7.3.2

VignetteBuilder:

knitr

Config/testthat/edition:

NeedsCompilation:

Packaged:

2024-11-18 14:11:44 UTC; C240390

Author:

William Michael Landau

[aut, cre], David Lawrence Miller [rev], Eli Lilly and Company [cph]

Maintainer:

William Michael Landau <will.landau.oss@gmail.com>

Repository:

CRAN

Date/Publication:

2024-11-18 14:50:02 UTC

jagstargets: Targets for JAGS Workflows

Description

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the jagstargets R package leverages targets and R2jags to ease this burden. jagstargets makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone.

One MCMC per model with multiple outputs

Description

Targets to run a JAGS model once with MCMC and save multiple outputs.

Usage

tar_jags(
  name,
  jags_files,
  parameters.to.save,
  data = list(),
  summaries = list(),
  summary_args = list(),
  n.cluster = 1,
  n.chains = 3,
  n.iter = 2000,
  n.burnin = as.integer(n.iter/2),
  n.thin = 1,
  jags.module = c("glm", "dic"),
  inits = NULL,
  RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister"),
  jags.seed = 1,
  stdout = NULL,
  stderr = NULL,
  progress.bar = "text",
  refresh = 0,
  draws = TRUE,
  summary = TRUE,
  dic = TRUE,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = "qs",
  format_df = "fst_tbl",
  repository = targets::tar_option_get("repository"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Symbol, base name for the collection of targets. Serves as a prefix for target names.

jags_files

Character vector of JAGS model files. If you supply multiple files, each model will run on the one shared dataset generated by the code in data. If you supply an unnamed vector, tools::file_path_sans_ext(basename(jags_files)) will be used as target name suffixes. If jags_files is a named vector, the suffixed will come from names(jags_files).

parameters.to.save

Model parameters to save, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

data

Code to generate the data list for the JAGS model. Optionally include a .join_data element to join parts of the data to correspondingly named parameters in the summary output. See the vignettes for details.

summaries

List of summary functions passed to ... in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

summary_args

List of summary function arguments passed to .args in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

n.cluster

Number of parallel processes, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.chains

Number of MCMC chains, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.iter

Number if iterations (including warmup), passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.burnin

Number of warmup iterations, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.thin

Thinning interval, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.module

Character vector of JAGS modules to load, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

inits

Initial values of model parameters, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

RNGname

Choice of random number generator, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.seed

Seeds to apply to JAGS, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

stdout

Character of length 1, file path to write the stdout stream of the model when it runs. Set to NULL to print to the console. Set to R.utils::nullfile() to suppress stdout. Does not apply to messages, warnings, or errors.

stderr

Character of length 1, file path to write the stderr stream of the model when it runs. Set to NULL to print to the console. Set to R.utils::nullfile() to suppress stderr. Does not apply to messages, warnings, or errors.

progress.bar

Type of progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

refresh

Frequency for refreshing the progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

draws

Logical, whether to create a target for posterior draws. Saves draws as a compressed posterior::as_draws_df() tibble. Convenient, but duplicates storage.

summary

Logical, whether to create a target to store a small data frame of posterior summary statistics and convergence diagnostics.

dic

Logical, whether to create a target with deviance information criterion (DIC) results.

tidy_eval

Logical, whether to enable tidy evaluation when interpreting command and pattern. If TRUE, you can use the "bang-bang" operator ⁠!!⁠ to programmatically insert the values of global objects.

packages

Character vector of packages to load right before the target runs or the output data is reloaded for downstream targets. Use tar_option_set() to set packages globally for all subsequent targets you define.

library

Character vector of library paths to try when loading packages.

format

Character of length 1, storage format of the non-data-frame targets such as the JAGS data and any JAGS fit objects. Please choose an all=purpose format such as "qs" or "aws_qs" rather than a file format like "file" or a data frame format like "parquet". For more on storage formats, see the help file of targets::tar_target().

format_df

Character of length 1, storage format of the data frame targets such as posterior draws. We recommend efficient data frame formats such as "feather" or "aws_parquet". For more on storage formats, see the help file of targets::tar_target().

repository

Character of length 1, remote repository for target storage. Choices:

"local": file system of the local machine.
"aws": Amazon Web Services (AWS) S3 bucket. Can be configured with a non-AWS S3 bucket using the endpoint argument of tar_resources_aws(), but versioning capabilities may be lost in doing so. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
"gcp": Google Cloud Platform storage bucket. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
A character string from tar_repository_cas() for content-addressable storage.

Note: if repository is not "local" and format is "file" then the target should create a single output file. That output file is uploaded to the cloud and tracked for changes where it exists in the cloud. The local file is deleted after the target runs.

error

Character of length 1, what to do if the target stops and throws an error. Options:

"stop": the whole pipeline stops and throws an error.
"continue": the whole pipeline keeps going.
"null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails.
"abridge": any currently running targets keep running, but no new targets launch after that.
"trim": all currently running targets stay running. A queued target is allowed to start if:
1. It is not downstream of the error, and
2. It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)

memory

Character of length 1, memory strategy. Possible values:

"auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.
"persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network).
"transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value.

For cloud-based dynamic files (e.g. format = "file" with repository = "aws"), the memory option applies to the temporary local copy of the file: "persistent" means it remains until the end of the pipeline and is then deleted, and "transient" means it gets deleted as soon as possible. The former conserves bandwidth, and the latter conserves local storage.

garbage_collection

Logical: TRUE to run base::gc() just before the target runs, FALSE to omit garbage collection. In the case of high-performance computing, gc() runs both locally and on the parallel worker. All this garbage collection is skipped if the actual target is skipped in the pipeline. Non-logical values of garbage_collection are converted to TRUE or FALSE using isTRUE(). In other words, non-logical values are converted FALSE. For example, garbage_collection = 2 is equivalent to garbage_collection = FALSE.

deployment

Character of length 1. If deployment is "main", then the target will run on the central controlling R process. Otherwise, if deployment is "worker" and you set up the pipeline with distributed/parallel computing, then the target runs on a parallel worker. For more on distributed/parallel computing in targets, please visit https://books.ropensci.org/targets/crew.html.

priority

Numeric of length 1 between 0 and 1. Controls which targets get deployed first when multiple competing targets are ready simultaneously. Targets with priorities closer to 1 get dispatched earlier (and polled earlier in tar_make_future()).

resources

Object returned by tar_resources() with optional settings for high-performance computing functionality, alternative data storage formats, and other optional capabilities of targets. See tar_resources() for details.

storage

Character string to control when the output of the target is saved to storage. Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values:

"main": the target's return value is sent back to the host machine and saved/uploaded locally.
"worker": the worker saves/uploads the value.
"none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.

retrieval

Character string to control when the current target loads its dependencies into memory before running. (Here, a "dependency" is another target upstream that the current one depends on.) Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values:

"main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs.
"worker": the worker loads the target's dependencies.
"none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

description

Character of length 1, a custom free-form human-readable text description of the target. Descriptions appear as target labels in functions like tar_manifest() and tar_visnetwork(), and they let you select subsets of targets for the names argument of functions like tar_make(). For example, tar_manifest(names = tar_described_as(starts_with("survival model"))) lists all the targets whose descriptions start with the character string "survival model".

Details

The MCMC targets use R2jags::jags() if n.cluster is 1 and R2jags::jags.parallel() otherwise. Most arguments to tar_jags() are forwarded to these functions.

Value

tar_jags() returns list of target objects. See the "Target objects" section for background. The target names use the name argument as a prefix, and the individual elements of jags_files appear in the suffixes where applicable. As an example, the specific target objects returned by tar_jags(name = x, jags_files = "y.jags", ...) returns a list of targets::tar_target() objects:

x_file_y: reproducibly track the JAGS model file. Returns a character vector of length 1 with the path to the JAGS model file.
x_lines_y: read the contents of the JAGS model file for safe transport to parallel workers. Returns a character vector of lines in the model file.
x_data: run the R expression in the data argument to produce a JAGS dataset for the model. Returns a JAGS data list.
x_mcmc_y: run MCMC on the model and dataset. Returns an rjags object from R2jags with all the MCMC results.
x_draws_y: extract posterior samples from x_mcmc_y. Returns a tidy data frame of MCMC draws. Omitted if draws = FALSE.
x_summary_y: extract posterior summaries from x_mcmc_y. Returns a tidy data frame of MCMC draws. Omitted if summary = FALSE.
x_dic: extract deviance information criterion (DIC) info from x_mcmc_y. Returns a tidy data frame of DIC info. Omitted if dic = FALSE.

Target objects

Most stantargets functions are target factories, which means they return target objects or lists of target objects. Target objects represent skippable steps of the analysis pipeline as described at https://books.ropensci.org/targets/. Please read the walkthrough at https://books.ropensci.org/targets/walkthrough.html to understand the role of target objects in analysis pipelines.

For developers, https://wlandau.github.io/targetopia/contributing.html#target-factories explains target factories (functions like this one which generate targets) and the design specification at https://books.ropensci.org/targets-design/ details the structure and composition of target objects.

Examples

if (identical(Sys.getenv("TAR_JAGS_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
library(jagstargets)
# Do not use a temp file for a real project
# or else your targets will always rerun.
tmp <- tempfile(pattern = "", fileext = ".jags")
tar_jags_example_file(tmp)
list(
  tar_jags(
    your_model,
    jags_files = tmp,
    data = tar_jags_example_data(),
    parameters.to.save = "beta",
    stdout = R.utils::nullfile(),
    stderr = R.utils::nullfile()
  )
)
}, ask = FALSE)
targets::tar_make()
})
}

Select a strategic piece of `R2jags` output.

Description

Not a user-side function. Do not call directly. Exported for infrastructure reasons only.

Usage

tar_jags_df(
  fit,
  data,
  output = c("draws", "summary", "dic"),
  variables = NULL,
  summaries = NULL,
  summary_args = NULL,
  transform = NULL
)

Arguments

fit

R2jags object.

data

A list, the original JAGS dataset.

output

Character of length 1 denoting the type of output tibble to return: "draws" for MCMC samples (which could take up a lot of space) "summary" for lightweight posterior summary statistics, and "dic" for the overall deviance information criterion and effective number of parameters.

variables

Character vector of model parameter names. The output posterior summaries are restricted to these variables.

summaries

List of summary functions passed to ... in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

summary_args

List of summary function arguments passed to .args in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

transform

Symbol or NULL, name of a function that accepts arguments data and draws and returns a data frame. Here, data is the JAGS data list supplied to the model, and draws is a data frame with one column per model parameter and one row per posterior sample. Any arguments other than data and draws must have valid default values because jagstargets will not populate them. See the simulation-based calibration discussion thread at https://github.com/ropensci/jagstargets/discussions/31 for an example.

Value

A data frame of R2jags output. Depends on the output argument.

Simulate example JAGS data.

Description

An example dataset compatible with the model file from tar_jags_example_file(). The output has a .join_data element so the true value of beta from the simulation is automatically appended to the beta rows of the summary output.

Usage

tar_jags_example_data(n = 10L)

Arguments

n

Integer of length 1, number of data points.

Format

A list with the following elements:

n: integer, number of data points.
x: numeric, covariate vector.
y: numeric, response variable.
true_beta: numeric of length 1, value of the regression coefficient beta used in simulation.
.join_data: a list of simulated values to be appended to as a .join_data column in the output of targets generated by functions such as tar_jags_rep_summary(). Contains the regression coefficient beta (numeric of length 1) and prior predictive data y (numeric vector).

Details

The tar_jags_example_data() function draws a JAGS dataset from the prior predictive distribution of the model from tar_jags_example_file(). First, the regression coefficient beta is drawn from its standard normal prior, and the covariate x is computed. Then, conditional on the beta draws and the covariate, the response vector y is drawn from its Normal(x * beta, 1) likelihood.

Value

List, dataset compatible with the model file from tar_jags_example_file(). The output has a .join_data element so the true value of beta from the simulation is automatically appended to the beta rows of the summary output.

Examples

tar_jags_example_data()

Write an example JAGS model file.

Description

Overwrites the file at path with a built-in example JAGS model file.

Usage

tar_jags_example_file(path = tempfile(pattern = "", fileext = ".jags"))

Arguments

path

Character of length 1, file path to write the model file.

Value

NULL (invisibly).

Examples

path <- tempfile(pattern = "", fileext = ".jags")
tar_jags_example_file(path = path)
writeLines(readLines(path))

Tidy output from multiple MCMCs per model.

Description

Internal function. Users should not invoke directly.

Usage

tar_jags_rep(
  name,
  jags_files,
  parameters.to.save,
  data = quote(list()),
  batches = 1L,
  reps = 1L,
  output = c("summary", "draws", "dic"),
  variables = NULL,
  summaries = NULL,
  summary_args = NULL,
  transform = NULL,
  combine = TRUE,
  n.cluster = 1,
  n.chains = 3,
  n.iter = 2000,
  n.burnin = as.integer(n.iter/2),
  n.thin = 1,
  jags.module = c("glm", "dic"),
  inits = NULL,
  RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister"),
  jags.seed = NULL,
  stdout = NULL,
  stderr = NULL,
  progress.bar = "text",
  refresh = 0,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = "qs",
  format_df = "fst_tbl",
  repository = targets::tar_option_get("repository"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Symbol, base name for the collection of targets. Serves as a prefix for target names.

jags_files

parameters.to.save

Model parameters to save, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

data

batches

Number of batches. Each batch runs a model reps times.

reps

Number of replications per batch. Ideally, each rep should produce its own random dataset using the code supplied to data.

output

variables

Character vector of model parameter names. The output posterior summaries are restricted to these variables.

summaries

List of summary functions passed to ... in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

summary_args

List of summary function arguments passed to .args in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

transform

combine

Logical, whether to create a target to combine all the model results into a single data frame downstream. Convenient, but duplicates data.

n.cluster

Number of parallel processes, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.chains

Number of MCMC chains, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.iter

n.burnin

Number of warmup iterations, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.thin

Thinning interval, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.module

inits

RNGname

jags.seed

The jags.seed argument of the ⁠tar_jags_rep*()⁠ functions is deprecated. See the "Seeds" section for details.

stdout

stderr

progress.bar

Type of progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

refresh

tidy_eval

packages

library

Character vector of library paths to try when loading packages.

format

Character of length 1, storage format of the data frames of posterior summaries and other data frames returned by targets. We recommend efficient data frame formats such as "feather" or "aws_parquet". For more on storage formats, see the help file of targets::tar_target().

format_df

repository

Character of length 1, remote repository for target storage. Choices:

"local": file system of the local machine.
"aws": Amazon Web Services (AWS) S3 bucket. Can be configured with a non-AWS S3 bucket using the endpoint argument of tar_resources_aws(), but versioning capabilities may be lost in doing so. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
"gcp": Google Cloud Platform storage bucket. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
A character string from tar_repository_cas() for content-addressable storage.

error

Character of length 1, what to do if the target stops and throws an error. Options:

"stop": the whole pipeline stops and throws an error.
"continue": the whole pipeline keeps going.
"null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails.
"abridge": any currently running targets keep running, but no new targets launch after that.
"trim": all currently running targets stay running. A queued target is allowed to start if:
1. It is not downstream of the error, and
2. It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)

memory

Character of length 1, memory strategy. Possible values:

"auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.
"persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network).
"transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value.

garbage_collection

deployment

priority

resources

storage

"main": the target's return value is sent back to the host machine and saved/uploaded locally.
"worker": the worker saves/uploads the value.
"none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.

retrieval

"main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs.
"worker": the worker loads the target's dependencies.
"none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

description

Value

tar_jags_rep(name = x, jags_files = "y.jags") returns a list of targets::tar_target() objects:

x_file_y: reproducibly track the jags model file.
x_lines_y: contents of the jags model file.
x_data: dynamic branching target with simulated datasets.
x_y: dynamic branching target with tidy data frames of MCMC summaries.
x: combine all the model-specific summary targets into a single data frame with columns to distinguish among the models. Suppressed if combine is FALSE.

Seeds

Rep-specific random number generator seeds for the data and models are automatically set based on the batch, rep, parent target name, and tar_option_get("seed"). This ensures the rep-specific seeds do not change when you change the batching configuration (e.g. 40 batches of 10 reps each vs 20 batches of 20 reps each). Each data seed is in the .seed list element of the output, and each JAGS seed is in the .seed column of each JAGS model output.

Generate a batch of data

Description

Not a user-side function. Do not invoke directly.

Usage

tar_jags_rep_data_batch(reps, batch, command)

Arguments

reps

Positive integer of length 1, number of reps to run.

batch

Positive integer of length 1, index of the current batch.

command

R code to run to generate one dataset.

Value

A list of JAGS datasets containing data and dataset IDs.

Examples

if (identical(Sys.getenv("TAR_JAGS_EXAMPLES"), "true")) {
tar_jags_rep_data_batch(2, 1, tar_jags_example_data())
}

Tidy DIC output from multiple MCMCs per model

Description

Run multiple MCMCs on simulated datasets and return DIC and the effective number of parameters for each run.

Usage

tar_jags_rep_dic(
  name,
  jags_files,
  parameters.to.save,
  data = list(),
  batches = 1L,
  reps = 1L,
  combine = TRUE,
  n.cluster = 1,
  n.chains = 3,
  n.iter = 2000,
  n.burnin = as.integer(n.iter/2),
  n.thin = 1,
  jags.module = c("glm", "dic"),
  inits = NULL,
  RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister"),
  jags.seed = NULL,
  stdout = NULL,
  stderr = NULL,
  progress.bar = "text",
  refresh = 0,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = "qs",
  format_df = "fst_tbl",
  repository = targets::tar_option_get("repository"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Symbol, base name for the collection of targets. Serves as a prefix for target names.

jags_files

parameters.to.save

Model parameters to save, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

data

batches

Number of batches. Each batch runs a model reps times.

reps

Number of replications per batch. Ideally, each rep should produce its own random dataset using the code supplied to data.

combine

Logical, whether to create a target to combine all the model results into a single data frame downstream. Convenient, but duplicates data.

n.cluster

Number of parallel processes, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.chains

Number of MCMC chains, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.iter

n.burnin

Number of warmup iterations, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.thin

Thinning interval, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.module

inits

RNGname

jags.seed

The jags.seed argument of the ⁠tar_jags_rep*()⁠ functions is deprecated. See the "Seeds" section for details.

stdout

stderr

progress.bar

Type of progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

refresh

tidy_eval

packages

library

Character vector of library paths to try when loading packages.

format

format_df

repository

Character of length 1, remote repository for target storage. Choices:

"local": file system of the local machine.
"aws": Amazon Web Services (AWS) S3 bucket. Can be configured with a non-AWS S3 bucket using the endpoint argument of tar_resources_aws(), but versioning capabilities may be lost in doing so. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
"gcp": Google Cloud Platform storage bucket. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
A character string from tar_repository_cas() for content-addressable storage.

error

Character of length 1, what to do if the target stops and throws an error. Options:

"stop": the whole pipeline stops and throws an error.
"continue": the whole pipeline keeps going.
"null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails.
"abridge": any currently running targets keep running, but no new targets launch after that.
"trim": all currently running targets stay running. A queued target is allowed to start if:
1. It is not downstream of the error, and
2. It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)

memory

Character of length 1, memory strategy. Possible values:

"auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.
"persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network).
"transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value.

garbage_collection

deployment

priority

resources

storage

"main": the target's return value is sent back to the host machine and saved/uploaded locally.
"worker": the worker saves/uploads the value.
"none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.

retrieval

"main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs.
"worker": the worker loads the target's dependencies.
"none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

description

Details

The MCMC targets use R2jags::jags() if n.cluster is 1 and R2jags::jags.parallel() otherwise. Most arguments to tar_jags() are forwarded to these functions.

Value

tar_jags_rep_dic() returns list of target objects. See the "Target objects" section for background. The target names use the name argument as a prefix, and the individual elements of jags_files appear in the suffixes where applicable. As an example, the specific target objects returned by tar_jags_rep_dic(name = x, jags_files = "y.jags") are as follows.

x_file_y: reproducibly track the JAGS model file. Returns a character vector of length 1 with the path to the JAGS model file.
x_lines_y: read the contents of the JAGS model file for safe transport to parallel workers. Returns a character vector of lines in the model file.
x_data: use dynamic branching to generate multiple JAGS datasets from the R expression in the data argument. Each dynamic branch returns a batch of JAGS data lists.
x_y: run JAGS on each dataset from x_data. Each dynamic branch returns a tidy data frame of DIC results for each batch of data.
x: combine all the batches from x_y into a non-dynamic target. Suppressed if combine is FALSE. Returns a long tidy data frame with all DIC info from all the branches of x_y.

Seeds

Target objects

Examples

if (identical(Sys.getenv("TAR_JAGS_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
library(jagstargets)
# Do not use a temp file for a real project
# or else your targets will always rerun.
tmp <- tempfile(pattern = "", fileext = ".jags")
tar_jags_example_file(tmp)
list(
  tar_jags_rep_dic(
    your_model,
    jags_files = tmp,
    data = tar_jags_example_data(),
    parameters.to.save = "beta",
    batches = 2,
    reps = 2,
    stdout = R.utils::nullfile(),
    stderr = R.utils::nullfile()
  )
)
}, ask = FALSE)
targets::tar_make()
})
}

Tidy posterior draws from multiple MCMCs per model

Description

Run multiple MCMCs on simulated datasets and return posterior samples and the effective number of parameters for each run.

Usage

tar_jags_rep_draws(
  name,
  jags_files,
  parameters.to.save,
  data = list(),
  batches = 1L,
  reps = 1L,
  transform = NULL,
  combine = FALSE,
  n.cluster = 1,
  n.chains = 3,
  n.iter = 2000,
  n.burnin = as.integer(n.iter/2),
  n.thin = 1,
  jags.module = c("glm", "dic"),
  inits = NULL,
  RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister"),
  jags.seed = NULL,
  stdout = NULL,
  stderr = NULL,
  progress.bar = "text",
  refresh = 0,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = "qs",
  format_df = "fst_tbl",
  repository = targets::tar_option_get("repository"),
  error = targets::tar_option_get("error"),
  memory = "transient",
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Symbol, base name for the collection of targets. Serves as a prefix for target names.

jags_files

parameters.to.save

Model parameters to save, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

data

batches

Number of batches. Each batch runs a model reps times.

reps

Number of replications per batch. Ideally, each rep should produce its own random dataset using the code supplied to data.

transform

combine

Logical, whether to create a target to combine all the model results into a single data frame downstream. Convenient, but duplicates data.

n.cluster

Number of parallel processes, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.chains

Number of MCMC chains, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.iter

n.burnin

Number of warmup iterations, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.thin

Thinning interval, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.module

inits

RNGname

jags.seed

The jags.seed argument of the ⁠tar_jags_rep*()⁠ functions is deprecated. See the "Seeds" section for details.

stdout

stderr

progress.bar

Type of progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

refresh

tidy_eval

packages

library

Character vector of library paths to try when loading packages.

format

format_df

repository

Character of length 1, remote repository for target storage. Choices:

"local": file system of the local machine.
"aws": Amazon Web Services (AWS) S3 bucket. Can be configured with a non-AWS S3 bucket using the endpoint argument of tar_resources_aws(), but versioning capabilities may be lost in doing so. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
"gcp": Google Cloud Platform storage bucket. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
A character string from tar_repository_cas() for content-addressable storage.

error

Character of length 1, what to do if the target stops and throws an error. Options:

"stop": the whole pipeline stops and throws an error.
"continue": the whole pipeline keeps going.
"null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails.
"abridge": any currently running targets keep running, but no new targets launch after that.
"trim": all currently running targets stay running. A queued target is allowed to start if:
1. It is not downstream of the error, and
2. It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)

memory

Character of length 1, memory strategy. Possible values:

"auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.
"persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network).
"transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value.

garbage_collection

deployment

priority

resources

storage

"main": the target's return value is sent back to the host machine and saved/uploaded locally.
"worker": the worker saves/uploads the value.
"none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.

retrieval

"main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs.
"worker": the worker loads the target's dependencies.
"none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

description

Details

The MCMC targets use R2jags::jags() if n.cluster is 1 and R2jags::jags.parallel() otherwise. Most arguments to tar_jags() are forwarded to these functions.

Value

tar_jags_rep_draws() returns list of target objects. See the "Target objects" section for background. The target names use the name argument as a prefix, and the individual elements of jags_files appear in the suffixes where applicable. As an example, the specific target objects returned by tar_jags_rep_dic(name = x, jags_files = "y.jags") are as follows.

x_file_y: reproducibly track the JAGS model file. Returns a character vector of length 1 with the path to the JAGS model file.
x_lines_y: read the contents of the JAGS model file for safe transport to parallel workers. Returns a character vector of lines in the model file.
x_data: use dynamic branching to generate multiple JAGS datasets from the R expression in the data argument. Each dynamic branch returns a batch of JAGS data lists.
x_y: run JAGS on each dataset from x_data. Each dynamic branch returns a tidy data frame of draws for each batch of data.
x: combine all the batches from x_y into a non-dynamic target. Suppressed if combine is FALSE. Returns a long tidy data frame with all draws from all the branches of x_y.

Seeds

Target objects

Examples

if (identical(Sys.getenv("TAR_JAGS_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
library(jagstargets)
# Do not use a temp file for a real project
# or else your targets will always rerun.
tmp <- tempfile(pattern = "", fileext = ".jags")
tar_jags_example_file(tmp)
list(
  tar_jags_rep_draws(
    your_model,
    jags_files = tmp,
    data = tar_jags_example_data(),
    parameters.to.save = "beta",
    batches = 2,
    reps = 2,
    stdout = R.utils::nullfile(),
    stderr = R.utils::nullfile()
  )
)
}, ask = FALSE)
targets::tar_make()
})
}

Run a batch of iterations for a jags model and return only strategic output.

Description

Not a user-side function. Do not invoke directly.

Usage

tar_jags_rep_run(
  jags_lines,
  jags_name,
  jags_file,
  parameters.to.save,
  data,
  variables = NULL,
  summaries = NULL,
  summary_args = NULL,
  transform = NULL,
  reps,
  output,
  n.cluster = n.cluster,
  n.chains = n.chains,
  n.iter = n.iter,
  n.burnin = n.burnin,
  n.thin = n.thin,
  jags.module = jags.module,
  inits = inits,
  RNGname = RNGname,
  stdout = stdout,
  stderr = stderr,
  progress.bar = progress.bar,
  refresh = refresh
)

Arguments

jags_lines

Character vector of lines from a JAGS file.

jags_name

Friendly suffix of the jags model target.

jags_file

Original path to the input jags file.

parameters.to.save

Model parameters to save, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

data

A list, the original JAGS dataset.

variables

Character vector of model parameter names. The output posterior summaries are restricted to these variables.

summaries

List of summary functions passed to ... in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

summary_args

List of summary function arguments passed to .args in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

transform

output

n.cluster

Number of parallel processes, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.chains

Number of MCMC chains, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.iter

n.burnin

Number of warmup iterations, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.thin

Thinning interval, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.module

inits

RNGname

stdout

stderr

progress.bar

Type of progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

refresh

Value

A data frame of posterior summaries.

Tidy posterior summaries from multiple MCMCs per model

Description

Run multiple MCMCs on simulated datasets and return posterior summaries and the effective number of parameters for each run.

Usage

tar_jags_rep_summary(
  name,
  jags_files,
  parameters.to.save,
  data = list(),
  variables = NULL,
  summaries = NULL,
  summary_args = NULL,
  batches = 1L,
  reps = 1L,
  combine = TRUE,
  n.cluster = 1,
  n.chains = 3,
  n.iter = 2000,
  n.burnin = as.integer(n.iter/2),
  n.thin = 1,
  jags.module = c("glm", "dic"),
  inits = NULL,
  RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister"),
  jags.seed = NULL,
  stdout = NULL,
  stderr = NULL,
  progress.bar = "text",
  refresh = 0,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = "qs",
  format_df = "fst_tbl",
  repository = targets::tar_option_get("repository"),
  error = targets::tar_option_get("error"),
  memory = "transient",
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Symbol, base name for the collection of targets. Serves as a prefix for target names.

jags_files

parameters.to.save

Model parameters to save, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

data

variables

Character vector of model parameter names. The output posterior summaries are restricted to these variables.

summaries

List of summary functions passed to ... in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

summary_args

List of summary function arguments passed to .args in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.

batches

Number of batches. Each batch runs a model reps times.

reps

Number of replications per batch. Ideally, each rep should produce its own random dataset using the code supplied to data.

combine

Logical, whether to create a target to combine all the model results into a single data frame downstream. Convenient, but duplicates data.

n.cluster

Number of parallel processes, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.chains

Number of MCMC chains, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.iter

n.burnin

Number of warmup iterations, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.thin

Thinning interval, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.module

inits

RNGname

jags.seed

The jags.seed argument of the ⁠tar_jags_rep*()⁠ functions is deprecated. See the "Seeds" section for details.

stdout

stderr

progress.bar

Type of progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

refresh

tidy_eval

packages

library

Character vector of library paths to try when loading packages.

format

format_df

repository

Character of length 1, remote repository for target storage. Choices:

"local": file system of the local machine.
"aws": Amazon Web Services (AWS) S3 bucket. Can be configured with a non-AWS S3 bucket using the endpoint argument of tar_resources_aws(), but versioning capabilities may be lost in doing so. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
"gcp": Google Cloud Platform storage bucket. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions.
A character string from tar_repository_cas() for content-addressable storage.

error

Character of length 1, what to do if the target stops and throws an error. Options:

"stop": the whole pipeline stops and throws an error.
"continue": the whole pipeline keeps going.
"null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails.
"abridge": any currently running targets keep running, but no new targets launch after that.
"trim": all currently running targets stay running. A queued target is allowed to start if:
1. It is not downstream of the error, and
2. It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)

memory

Character of length 1, memory strategy. Possible values:

"auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.
"persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network).
"transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value.

garbage_collection

deployment

priority

resources

storage

"main": the target's return value is sent back to the host machine and saved/uploaded locally.
"worker": the worker saves/uploads the value.
"none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.

retrieval

"main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs.
"worker": the worker loads the target's dependencies.
"none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

description

Details

The MCMC targets use R2jags::jags() if n.cluster is 1 and R2jags::jags.parallel() otherwise. Most arguments to tar_jags() are forwarded to these functions.

Value

tar_jags_rep_summary() returns list of target objects. See the "Target objects" section for background. The target names use the name argument as a prefix, and the individual elements of jags_files appear in the suffixes where applicable. As an example, the specific target objects returned by tar_jags_rep_dic(name = x, jags_files = "y.jags") are as follows.

x_file_y: reproducibly track the JAGS model file. Returns a character vector of length 1 with the path to the JAGS model file.
x_lines_y: read the contents of the JAGS model file for safe transport to parallel workers. Returns a character vector of lines in the model file.
x_data: use dynamic branching to generate multiple JAGS datasets from the R expression in the data argument. Each dynamic branch returns a batch of JAGS data lists.
x_y: run JAGS on each dataset from x_data. Each dynamic branch returns a tidy data frame of summaries for each batch of data.
x: combine all the batches from x_y into a non-dynamic target. Suppressed if combine is FALSE. Returns a long tidy data frame with all summaries from all the branches of x_y.

Seeds

Target objects

Examples

if (identical(Sys.getenv("TAR_JAGS_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
library(jagstargets)
# Do not use a temp file for a real project
# or else your targets will always rerun.
tmp <- tempfile(pattern = "", fileext = ".jags")
tar_jags_example_file(tmp)
list(
  tar_jags_rep_summary(
    your_model,
    jags_files = tmp,
    data = tar_jags_example_data(),
    parameters.to.save = "beta",
    batches = 2,
    reps = 2,
    stdout = R.utils::nullfile(),
    stderr = R.utils::nullfile()
  )
)
}, ask = FALSE)
targets::tar_make()
})
}

Run a JAGS model and return the whole output object.

Description

Not a user-side function. Do not invoke directly.

Usage

tar_jags_run(
  jags_lines,
  parameters.to.save,
  data,
  inits,
  n.cluster,
  n.chains,
  n.iter,
  n.burnin,
  n.thin,
  jags.module,
  RNGname,
  jags.seed,
  stdout,
  stderr,
  progress.bar,
  refresh
)

Arguments

jags_lines

Character vector of lines from a JAGS model file.

parameters.to.save

Model parameters to save, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

inits

n.cluster

Number of parallel processes, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.chains

Number of MCMC chains, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.iter

n.burnin

Number of warmup iterations, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

n.thin

Thinning interval, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

jags.module

RNGname

jags.seed

Seeds to apply to JAGS, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

stdout

stderr

progress.bar

Type of progress bar, passed to R2jags::jags() or R2jags::jags.parallel(). See the argument documentation of the R2jags::jags() and R2jags::jags.parallel() help files for details.

refresh

Value

An R2jags output object.

jagstargets: Targets for JAGS Workflows

Description

See Also

One MCMC per model with multiple outputs

Description

Usage

Arguments

Details

Value

Target objects

Examples

Select a strategic piece of R2jags output.

Description

Usage

Arguments

Value

Simulate example JAGS data.

Description

Usage

Arguments

Format

Details

Value

Examples

Write an example JAGS model file.

Description

Usage

Arguments

Value

Examples

Tidy output from multiple MCMCs per model.

Description

Usage

Arguments

Value

Seeds

Generate a batch of data

Description

Usage

Arguments

Value

Examples

Tidy DIC output from multiple MCMCs per model

Description

Usage

Arguments

Details

Value

Seeds

Target objects

Examples

Tidy posterior draws from multiple MCMCs per model

Description

Usage

Arguments

Details

Value

Seeds

Target objects

Examples

Run a batch of iterations for a jags model and return only strategic output.

Description

Usage

Arguments

Value

Tidy posterior summaries from multiple MCMCs per model

Description

Usage

Arguments

Details

Value

Seeds

Target objects

Examples

Run a JAGS model and return the whole output object.

Description

Usage

Arguments

Value

Select a strategic piece of `R2jags` output.