The $diagnose() method of a CmdStanModel object
runs Stan's basic diagnostic feature that will calculate the gradients
of the initial state and compare them with gradients calculated by
finite differences. Discrepancies between the two indicate that there is
a problem with the model or initial states or else there is a bug in Stan.
diagnose(
data = NULL,
seed = NULL,
init = NULL,
output_dir = getOption("cmdstanr_output_dir"),
output_basename = NULL,
epsilon = NULL,
error = NULL
)(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.
(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.
(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.
(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 $save_output_files().
(string) A string to use as a prefix for the names of
the output CSV files of CmdStan. If NULL (the default), the basename of
the output CSV files will be comprised from the model name, timestamp, and
5 random characters.
(positive real) The finite difference step size. Default value is 1e-6.
(positive real) The error threshold. Default value is 1e-6.
A CmdStanDiagnose object.
The CmdStanR website (mc-stan.org/cmdstanr) for online documentation and tutorials.
The Stan and CmdStan documentation:
Stan documentation: mc-stan.org/users/documentation
CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide
Other CmdStanModel methods:
model-method-check_syntax,
model-method-compile,
model-method-expose_functions,
model-method-format,
model-method-generate-quantities,
model-method-laplace,
model-method-optimize,
model-method-pathfinder,
model-method-sample,
model-method-sample_mpi,
model-method-variables,
model-method-variational
# \dontrun{
test <- cmdstanr_example("logistic", method = "diagnose")
# retrieve the gradients
test$gradients()
#> param_idx value model finite_diff error
#> 1 0 0.591940 0.00146498 0.00146493 4.91040e-08
#> 2 1 1.390710 -30.34660000 -30.34660000 -4.69705e-09
#> 3 2 -0.987687 11.19930000 11.19930000 1.90498e-08
#> 4 3 -1.426340 28.46830000 28.46830000 1.54688e-08
# }