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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 = 123,
  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")
)

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

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.

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 builds 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:

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 such as format = "aws_file", this memory strategy applies to temporary local copies of the file in _targets/scratch/": "persistent" means they remain until the end of the pipeline, and "transient" means they get deleted from the file system 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, only relevant to tar_make_clustermq() and tar_make_future(). If "worker", the target builds on a parallel worker. If "main", the target builds on the host machine / process managing the pipeline.

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 built 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" or "aws_file") it is the responsibility of the user to write to tar_path() from inside the target. An example target could look something like tar_target(x, saveRDS("value", tar_path(create_dir = TRUE)); "ignored", storage = "none")`.

    The distinguishing feature of storage = "none" (as opposed to format = "file" or "aws_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" or "aws_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 builds.

  • "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.

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.

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.

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_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()
})
}
#> • start target your_model_batch
#> • built target your_model_batch [0.001 seconds]
#> • start target your_model_file_X11e81f19fd26
#> • built target your_model_file_X11e81f19fd26 [0.001 seconds]
#> • start branch your_model_data_7b1b1358
#> • built branch your_model_data_7b1b1358 [0.001 seconds]
#> • start branch your_model_data_7b6d503e
#> • built branch your_model_data_7b6d503e [0.001 seconds]
#> • built pattern your_model_data
#> • start target your_model_lines_X11e81f19fd26
#> • built target your_model_lines_X11e81f19fd26 [0 seconds]
#> • start branch your_model_X11e81f19fd26_24769822
#> • built branch your_model_X11e81f19fd26_24769822 [0.089 seconds]
#> • start branch your_model_X11e81f19fd26_4e151279
#> • built branch your_model_X11e81f19fd26_4e151279 [0.055 seconds]
#> • built pattern your_model_X11e81f19fd26
#> • start target your_model
#> • built target your_model [0.001 seconds]
#> • end pipeline [0.369 seconds]
#>