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One summary of a CmdStanFit object

Source: R/tar_stan_summary.R
tar_stan_summary.Rd

Create a target to run the $summary() method of a CmdStanFit object.

Usage

tar_stan_summary(
  name,
  fit,
  data = NULL,
  variables = NULL,
  summaries = NULL,
  summary_args = NULL,
  format = "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.

fit

Symbol, name of a CmdStanFit object or an upstream target that returns a CmdStanFit object.

data

Code to generate the data for the Stan model.

variables

(character vector) The variables to include.

summaries

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

summary_args

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

format

Character of length 1, storage format of the data frame of posterior summaries. 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 (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".

Value

tar_stan_summary() returns target object to summarize a CmdStanFit object. The return value of the target is a tidy data frame of summaries returned by the $summary() method of the CmdStanFit object. See the "Target objects" section for background.

Details

tar_stan_mcmc() etc. with summary = TRUE already gives you a target with output from the $summary() method. Use tar_stan_summary() to create additional specialized summaries.

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

# First, write your Stan model file, e.g. model.stan.
# Then in _targets.R, write a pipeline like this:
if (Sys.getenv("TAR_LONG_EXAMPLES") == "true") {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
# Running inside a temporary directory to avoid
# modifying the user's file space. The file "model.stan"
# created below lives in a temporary directory.
# This satisfies CRAN policies.
tar_stan_example_file("model.stan")
targets::tar_script({
library(stantargets)
list(
  # Run a model and produce default summaries.
  tar_stan_mcmc(
    your_model,
    stan_files = "model.stan",
    data = tar_stan_example_data()
  ),
  # Produce a more specialized summary
  tar_stan_summary(
    your_summary,
    fit = your_model_mcmc_model,
    data = your_model_data_model,
    variables = "beta",
    summaries = list(~quantile(.x, probs = c(0.25, 0.75)))
  )
)}, ask = FALSE)
targets::tar_make()
})
}