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
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. Ifjags_files
is a named vector, the suffixed will come fromnames(jags_files)
.- parameters.to.save
Model parameters to save, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::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()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- n.chains
Number of MCMC chains, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- n.iter
Number if iterations (including warmup), passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- n.burnin
Number of warmup iterations, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- n.thin
Thinning interval, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- jags.module
Character vector of JAGS modules to load, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- inits
Initial values of model parameters, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- RNGname
Choice of random number generator, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- jags.seed
The
jags.seed
argument of thetar_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 toR.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 toR.utils::nullfile()
to suppress stderr. Does not apply to messages, warnings, or errors.- progress.bar
Type of progress bar, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- refresh
Frequency for refreshing the progress bar, passed to
R2jags::jags()
orR2jags::jags.parallel()
. See the argument documentation of theR2jags::jags()
andR2jags::jags.parallel()
help files for details.- tidy_eval
Logical, whether to enable tidy evaluation when interpreting
command
andpattern
. IfTRUE
, 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 oftargets::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 oftargets::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 theendpoint
argument oftar_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"
andformat
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 returnsNULL
. 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 (unlessstorage
is"worker"
, in which casetargets
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"
withrepository = "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, ifdeployment
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 intargets
, 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 oftargets
. Seetar_resources()
for details.- storage
Character of length 1, only relevant to
tar_make_clustermq()
andtar_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 whenretrieval = "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 toformat = "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 ifformat
is"file"
.
- retrieval
Character of length 1, only relevant to
tar_make_clustermq()
andtar_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()
andtar_visnetwork()
, and they let you select subsets of targets for thenames
argument of functions liketar_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"
.
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 thedata
argument. Each dynamic branch returns a batch of JAGS data lists.x_y
: run JAGS on each dataset fromx_data
. Each dynamic branch returns a tidy data frame of DIC results for each batch of data.x
: combine all the batches fromx_y
into a non-dynamic target. Suppressed ifcombine
isFALSE
. Returns a long tidy data frame with all DIC info from all the branches ofx_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.
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
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()
})
}
#> ▶ dispatched target your_model_file_X9d42641b7cb
#> ● completed target your_model_file_X9d42641b7cb [0 seconds]
#> ▶ dispatched target your_model_batch
#> ● completed target your_model_batch [0 seconds]
#> ▶ dispatched target your_model_lines_X9d42641b7cb
#> ● completed target your_model_lines_X9d42641b7cb [0 seconds]
#> ▶ dispatched branch your_model_data_d89cc2d257e1e88a
#> ● completed branch your_model_data_d89cc2d257e1e88a [0.001 seconds]
#> ▶ dispatched branch your_model_data_2b884456858ba77b
#> ● completed branch your_model_data_2b884456858ba77b [0.001 seconds]
#> ● completed pattern your_model_data
#> ▶ dispatched branch your_model_X9d42641b7cb_b12defd2030554f0
#> ● completed branch your_model_X9d42641b7cb_b12defd2030554f0 [0.059 seconds]
#> ▶ dispatched branch your_model_X9d42641b7cb_a99f2934ec83fb61
#> ● completed branch your_model_X9d42641b7cb_a99f2934ec83fb61 [0.056 seconds]
#> ● completed pattern your_model_X9d42641b7cb
#> ▶ dispatched target your_model
#> ● completed target your_model [0 seconds]
#> ▶ ended pipeline [0.219 seconds]
#>