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Analyse Character Model

Source: R/analyse.R
analyse.character.Rd

Analyses a model defined as a character string containing Stan or JAGS code. The character string is first converted to an mb_model object, then passed on to analyse.mb_model()

Usage

# S3 method for class 'character'
analyse(
  x,
  data,
  select_data = list(),
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.nthin", 1L),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  seed = sample.int(.Machine$integer.max, 1),
  stan_engine = getOption("mb.stan_engine", character(0)),
  niters_warmup = niters,
  ...
)

Arguments

x

A character string containing Stan or JAGS model code.

data

The data frame to analyse.

select_data

A named list specifying the columns to select and their associated classes and values as well as transformations and scaling options.

nchains

A count of the number of chains (default: 3).

niters

A count of the number of iterations to save per chain (default: 1000).

nthin

A count of the thinning interval.

parallel

A flag indicating whether to perform the analysis in parallel if possible.

quiet

A flag indicating whether to disable messages and warnings, including sampling progress.

glance

A flag indicating whether to print a model summary.

beep

A flag indicating whether to beep on completion of the analysis.

seed

A positive whole number specifying the seed to use. The default is random. This is currently only implemented for Stan models.

stan_engine

A string specifying the Stan engine to use:

  • "rstan" for MCMC sampling via rstan::sampling() (default).

  • "cmdstan-mcmc" for MCMC sampling via cmdstanr::sample()

  • "cmdstan-pathfinder" for pathfinder estimation via cmdstanr::pathfinder()

  • "cmdstan-optimize" for optimization via cmdstanr::optimize()

  • "cmdstan-laplace" for Laplace approximation via cmdstanr::laplace()

niters_warmup

A count of the number of warmup iterations. The default is to use the same number of iterations as niters. This is currently only implemented for Stan models.

...

Additional arguments passed to the underlying estimation function. See analyse.mb_model() for details.

Value

An mb_analysis object containing the fitted model results.

See also