Package 'jagstargets' reference manual

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>.
Authors:	William Michael Landau [aut, cre] , David Lawrence Miller [rev], Eli Lilly and Company [cph]
Maintainer:	William Michael Landau <[email protected]>
License:	MIT + file LICENSE
Version:	1.2.1.9000
Built:	2024-09-06 15:17:11 UTC
Source:	https://github.com/ropensci/jagstargets

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")
)
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. 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. `"abridge"`: any currently running targets keep running, but no new targets launch after that. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.) `"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.
`memory`	Character of length 1, memory strategy. If `"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). If `"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"`), this memory strategy 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, whether to run `base::gc()` just before the target runs.
`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 of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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"`: almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language. If you do use it, then the return value of the target is totally ignored when the target ends, but each downstream target still attempts to load the data file (except when `retrieval = "none"`). If you select `storage = "none"`, then the return value of the target's command is ignored, and the data is not saved automatically. As with dynamic files (`format = "file"`) it is the responsibility of the user to write to the data store from inside the target. The distinguishing feature of `storage = "none"` (as opposed to `format = "file"`) is that in the general case, downstream targets will automatically try to load the data from the data store as a dependency. As a corollary, `storage = "none"` is completely unnecessary if `format` is `"file"`.
`retrieval`	Character of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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 targets dependencies. `"none"`: the dependencies are not loaded at all. This choice is almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language.
`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 (requireNamespace("R2jags", quietly = 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()
})
}
if (requireNamespace("R2jags", quietly = 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()
})
}

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

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")
)
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`	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.
`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`	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`	The `jags.seed` argument of the `⁠tar_jags_rep*()⁠` functions is deprecated. See the "Seeds" section 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.
`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 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`	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. 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. `"abridge"`: any currently running targets keep running, but no new targets launch after that. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.) `"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.
`memory`	Character of length 1, memory strategy. If `"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). If `"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"`), this memory strategy 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, whether to run `base::gc()` just before the target runs.
`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 of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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"`: almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language. If you do use it, then the return value of the target is totally ignored when the target ends, but each downstream target still attempts to load the data file (except when `retrieval = "none"`). If you select `storage = "none"`, then the return value of the target's command is ignored, and the data is not saved automatically. As with dynamic files (`format = "file"`) it is the responsibility of the user to write to the data store from inside the target. The distinguishing feature of `storage = "none"` (as opposed to `format = "file"`) is that in the general case, downstream targets will automatically try to load the data from the data store as a dependency. As a corollary, `storage = "none"` is completely unnecessary if `format` is `"file"`.
`retrieval`	Character of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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 targets dependencies. `"none"`: the dependencies are not loaded at all. This choice is almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language.
`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_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

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.

Target objects

Examples

if (requireNamespace("R2jags", quietly = 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()
})
}
if (requireNamespace("R2jags", quietly = 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")
)
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`	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.
`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`	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.
`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`	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`	The `jags.seed` argument of the `⁠tar_jags_rep*()⁠` functions is deprecated. See the "Seeds" section 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.
`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 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`	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. 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. `"abridge"`: any currently running targets keep running, but no new targets launch after that. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.) `"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.
`memory`	Character of length 1, memory strategy. If `"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). If `"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"`), this memory strategy 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, whether to run `base::gc()` just before the target runs.
`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 of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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"`: almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language. If you do use it, then the return value of the target is totally ignored when the target ends, but each downstream target still attempts to load the data file (except when `retrieval = "none"`). If you select `storage = "none"`, then the return value of the target's command is ignored, and the data is not saved automatically. As with dynamic files (`format = "file"`) it is the responsibility of the user to write to the data store from inside the target. The distinguishing feature of `storage = "none"` (as opposed to `format = "file"`) is that in the general case, downstream targets will automatically try to load the data from the data store as a dependency. As a corollary, `storage = "none"` is completely unnecessary if `format` is `"file"`.
`retrieval`	Character of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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 targets dependencies. `"none"`: the dependencies are not loaded at all. This choice is almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language.
`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_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 (requireNamespace("R2jags", quietly = 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()
})
}
if (requireNamespace("R2jags", quietly = 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()
})
}

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")
)
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`	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.
`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`	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`	The `jags.seed` argument of the `⁠tar_jags_rep*()⁠` functions is deprecated. See the "Seeds" section 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.
`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 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`	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. 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. `"abridge"`: any currently running targets keep running, but no new targets launch after that. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.) `"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.
`memory`	Character of length 1, memory strategy. If `"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). If `"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"`), this memory strategy 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, whether to run `base::gc()` just before the target runs.
`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 of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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"`: almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language. If you do use it, then the return value of the target is totally ignored when the target ends, but each downstream target still attempts to load the data file (except when `retrieval = "none"`). If you select `storage = "none"`, then the return value of the target's command is ignored, and the data is not saved automatically. As with dynamic files (`format = "file"`) it is the responsibility of the user to write to the data store from inside the target. The distinguishing feature of `storage = "none"` (as opposed to `format = "file"`) is that in the general case, downstream targets will automatically try to load the data from the data store as a dependency. As a corollary, `storage = "none"` is completely unnecessary if `format` is `"file"`.
`retrieval`	Character of length 1, only relevant to `tar_make_clustermq()` and `tar_make_future()`. 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 targets dependencies. `"none"`: the dependencies are not loaded at all. This choice is almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language.
`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_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 (requireNamespace("R2jags", quietly = 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()
})
}
if (requireNamespace("R2jags", quietly = 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()
})
}

Package 'jagstargets'

Help Index

jagstargets: Targets for JAGS Workflows

Description

See Also

One MCMC per model with multiple outputs

Description

Usage

Arguments

Details

Value

Target objects

Examples

Simulate example JAGS data.

Description

Usage

Arguments

Format

Details

Value

Examples

Write an example JAGS model file.

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

Tidy posterior summaries from multiple MCMCs per model

Description

Usage

Arguments

Details

Value

Seeds

Target objects

Examples