tar_stan_mle_rep_run: Run a Stan model and return only the summaries.
In ropensci/stantargets: Targets for Stan Workflows

tar_stan_mle_rep_run

R Documentation

Run a Stan model and return only the summaries.

Description

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

Usage

tar_stan_mle_rep_run(
  stan_file,
  stan_name,
  stan_path,
  data,
  output_type,
  compile,
  quiet,
  stdout,
  stderr,
  dir,
  pedantic,
  include_paths,
  cpp_options,
  stanc_options,
  force_recompile,
  seed,
  refresh,
  init,
  save_latent_dynamics,
  output_dir,
  algorithm,
  init_alpha,
  iter,
  sig_figs,
  tol_obj,
  tol_rel_obj,
  tol_grad,
  tol_rel_grad,
  tol_param,
  history_size,
  data_copy,
  variables,
  summaries,
  summary_args
)

Arguments

`stan_file`	(string) The path to a `.stan` file containing a Stan program. The helper function `write_stan_file()` is provided for cases when it is more convenient to specify the Stan program as a string. If `stan_file` is not specified then `exe_file` must be specified.
`data`	(multiple options) The data to use for the variables specified in the data block of the Stan program. One of the following: A named list of R objects with the names corresponding to variables declared in the data block of the Stan program. Internally this list is then written to JSON for CmdStan using `write_stan_json()`. See `write_stan_json()` for details on the conversions performed on R objects before they are passed to Stan. A path to a data file compatible with CmdStan (JSON or R dump). See the appendices in the CmdStan guide for details on using these formats. `NULL` or an empty list if the Stan program has no data block.
`compile`	(logical) Do compilation? The default is `TRUE`. If `FALSE` compilation can be done later via the `$compile()` method.
`quiet`	(logical) Should the verbose output from CmdStan during compilation be suppressed? The default is `TRUE`, but if you encounter an error we recommend trying again with `quiet=FALSE` to see more of the output.
`dir`	(string) The path to the directory in which to store the CmdStan executable (or `.hpp` file if using `⁠$save_hpp_file()⁠`). The default is the same location as the Stan program.
`pedantic`	(logical) Should pedantic mode be turned on? The default is `FALSE`. Pedantic mode attempts to warn you about potential issues in your Stan program beyond syntax errors. For details see the Pedantic mode chapter in the Stan Reference Manual. Note: to do a pedantic check for a model without compiling it or for a model that is already compiled the `$check_syntax()` method can be used instead.
`include_paths`	(character vector) Paths to directories where Stan should look for files specified in `⁠#include⁠` directives in the Stan program.
`cpp_options`	(list) Any makefile options to be used when compiling the model (`STAN_THREADS`, `STAN_MPI`, `STAN_OPENCL`, etc.). Anything you would otherwise write in the `make/local` file. For an example of using threading see the Stan case study Reduce Sum: A Minimal Example.
`stanc_options`	(list) Any Stan-to-C++ transpiler options to be used when compiling the model. See the Examples section below as well as the `stanc` chapter of the CmdStan Guide for more details on available options: https://mc-stan.org/docs/cmdstan-guide/stanc.html.
`force_recompile`	(logical) Should the model be recompiled even if was not modified since last compiled. The default is `FALSE`. Can also be set via a global `cmdstanr_force_recompile` option.
`seed`	(positive integer(s)) A seed for the (P)RNG to pass to CmdStan. In the case of multi-chain sampling the single `seed` will automatically be augmented by the the run (chain) ID so that each chain uses a different seed. The exception is the transformed data block, which defaults to using same seed for all chains so that the same data is generated for all chains if RNG functions are used. The only time `seed` should be specified as a vector (one element per chain) is if RNG functions are used in transformed data and the goal is to generate different data for each chain.
`refresh`	(non-negative integer) The number of iterations between printed screen updates. If `refresh = 0`, only error messages will be printed.
`init`	(multiple options) The initialization method to use for the variables declared in the parameters block of the Stan program. One of the following: A real number `x>0`. This initializes all parameters randomly between `⁠[-x,x]⁠` on the unconstrained parameter space.; The number `0`. This initializes all parameters to `0`; A character vector of paths (one per chain) to JSON or Rdump files containing initial values for all or some parameters. See `write_stan_json()` to write R objects to JSON files compatible with CmdStan. A list of lists containing initial values for all or some parameters. For MCMC the list should contain a sublist for each chain. For other model fitting methods there should be just one sublist. The sublists should have named elements corresponding to the parameters for which you are specifying initial values. See Examples. A function that returns a single list with names corresponding to the parameters for which you are specifying initial values. The function can take no arguments or a single argument `chain_id`. For MCMC, if the function has argument `chain_id` it will be supplied with the chain id (from 1 to number of chains) when called to generate the initial values. See Examples. A `CmdStanMCMC`, `CmdStanMLE`, `CmdStanVB`, `CmdStanPathfinder`, or `CmdStanLaplace` fit object. If the fit object's parameters are only a subset of the model parameters then the other parameters will be drawn by Stan's default initialization. The fit object must have at least some parameters that are the same name and dimensions as the current Stan model. For the `sample` and `pathfinder` method, if the fit object has fewer draws than the requested number of chains/paths then the inits will be drawn using sampling with replacement. Otherwise sampling without replacement will be used. When a `CmdStanPathfinder` fit object is used as the init, if . `psis_resample` was set to `FALSE` and `calculate_lp` was set to `TRUE` (default), then resampling without replacement with Pareto smoothed weights will be used. If `psis_resample` was set to `TRUE` or `calculate_lp` was set to `FALSE` then sampling without replacement with uniform weights will be used to select the draws. PSIS resampling is used to select the draws for `CmdStanVB`, and `CmdStanLaplace` fit objects. A type inheriting from `posterior::draws`. If the draws object has less samples than the number of requested chains/paths then the inits will be drawn using sampling with replacement. Otherwise sampling without replacement will be used. If the draws object's parameters are only a subset of the model parameters then the other parameters will be drawn by Stan's default initialization. The fit object must have at least some parameters that are the same name and dimensions as the current Stan model.
`save_latent_dynamics`	(logical) Should auxiliary diagnostic information about the latent dynamics be written to temporary diagnostic CSV files? This argument replaces CmdStan's `diagnostic_file` argument and the content written to CSV is controlled by the user's CmdStan installation and not CmdStanR (for some algorithms no content may be written). The default is `FALSE`, which is appropriate for almost every use case. To save the temporary files created when `save_latent_dynamics=TRUE` see the `$save_latent_dynamics_files()` method.
`output_dir`	(string) A path to a directory where CmdStan should write its output CSV files. For MCMC there will be one file per chain; for other methods there will be a single file. For interactive use this can typically be left at `NULL` (temporary directory) since CmdStanR makes the CmdStan output (posterior draws and diagnostics) available in R via methods of the fitted model objects. This can be set for an entire R session using `options(cmdstanr_output_dir)`. The behavior of `output_dir` is as follows: If `NULL` (the default), then the CSV files are written to a temporary directory and only saved permanently if the user calls one of the `⁠$save_*⁠` methods of the fitted model object (e.g., `$save_output_files()`). These temporary files are removed when the fitted model object is garbage collected (manually or automatically). If a path, then the files are created in `output_dir` with names corresponding to the defaults used by `⁠$save_output_files()⁠`.
`algorithm`	(string) The optimization algorithm. One of `"lbfgs"`, `"bfgs"`, or `"newton"`. The control parameters below are only available for `"lbfgs"` and `⁠"bfgs⁠`. For their default values and more details see the CmdStan User's Guide. The default values can also be obtained by running `cmdstanr_example(method="optimize")$metadata()`.
`init_alpha`	(positive real) The initial step size parameter.
`iter`	(positive integer) The maximum number of iterations.
`sig_figs`	(positive integer) The number of significant figures used when storing the output values. By default, CmdStan represent the output values with 6 significant figures. The upper limit for `sig_figs` is 18. Increasing this value will result in larger output CSV files and thus an increased usage of disk space.
`tol_obj`	(positive real) Convergence tolerance on changes in objective function value.
`tol_rel_obj`	(positive real) Convergence tolerance on relative changes in objective function value.
`tol_grad`	(positive real) Convergence tolerance on the norm of the gradient.
`tol_rel_grad`	(positive real) Convergence tolerance on the relative norm of the gradient.
`tol_param`	(positive real) Convergence tolerance on changes in parameter value.
`history_size`	(positive integer) The size of the history used when approximating the Hessian. Only available for L-BFGS.
`variables`	(character vector) Optionally, the names of the variables (parameters, transformed parameters, and generated quantities) to read in. If `NULL` (the default) then all variables are included. If an empty string (`variables=""`) then none are included. For non-scalar variables all elements or specific elements can be selected: `variables = "theta"` selects all elements of `theta`; `variables = c("theta[1]", "theta[3]")` selects only the 1st and 3rd elements.