Analyse Single Model

Performs parameter estimation on a single mb_model object using Stan or JAGS.

The model fitting method dispatched depends on the class of the mb_model object and the stan_engine argument.

If the model is a JAGS model, rjags is used for MCMC sampling. If the model is a Stan model, rstan or cmdstanr is used, depending on the value provided to stan_engine:

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

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

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

• "cmdstan-variational" for variational ADVI estimation via cmdstanr::variational()

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

• Any other character value will default to MCMC sampling via rstan::sampling()

Usage

# S3 method for class 'mb_model'
analyse(
  x,
  data,
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.thin", NULL),
  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

An mb_model object to analyse.

data

The data frame to analyse, or a list of data frames for multiple datasets.

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 above for details).

Value

• If data is a data.frame: An mb_analysis object

• If data is a list of data.frames: An mb_meta_analysis object

Details

For CmdStan models, additional arguments can be passed to the engine-specific estimation functions via the ... argument.

For example, additional options in cmdstanr::sample() include:

• adapt_delta - Target acceptance rate (0 < adapt_delta < 1)

• max_treedepth - Maximum tree depth for NUTS sampler

• step_size - Initial step size for sampler

• refresh - How often to print sampling progress

• output_dir - Directory to save output files (default: NULL, uses temporary directory)

Some additional options in cmdstanr::pathfinder() include:

• num_paths - Number of single-path Pathfinders to run (default: 4)

• history_size - L-BFGS history size for approximating Hessian (default: 5)

• max_lbfgs_iters - Maximum L-BFGS iterations per path (default: 1000)

• psis_resample - Whether to use Pareto-smoothed importance sampling (default: TRUE)

Some additional options in cmdstanr::variational() include:

• algorithm - Variational algorithm: "meanfield" (default) or "fullrank"

Some aditional options in cmdstanr::optimize() include:

• algorithm - Optimization algorithm: "lbfgs" (default), "bfgs", or "newton"

Some additional options in cmdstanr::laplace() include:

• mode - CmdStanMLE object from previous optimization (if NULL, runs optimize)

• jacobian - Whether mode used Jacobian adjustment (default: TRUE)

Each analyse1 method checks for the presence of conflicting arguments and will ignore these (e.g., iter_sampling with stan_engine = 'cmdstan-mcmc' will be ignored in favour of iterations set via niters)

One exception is for init, which can be passed via '...' to override defaults or inits generated via gen_inits() in model(). Non-MCMC model fitting methods cannot use gen_inits(). Some arguments in analyse.mb_model() are unused in non-MCMC methods (e.g., nthin, niters_warmup).

For pathfinder, variational, and laplace methods, niters samples are drawn from the approximated posterior distributions.

See also

• analyse.character() for analysing a character model template

• analyse.mb_models() for analysing multiple models

Examples

if (FALSE) { # \dontrun{
# Stan model with RStan (default)
analysis <- analyse(stan_model, data, nchains = 4, niters = 1000)

# Stan model with CmdStanR MCMC
analysis <- analyse(stan_model, data,
  stan_engine = "cmdstan-mcmc",
  nchains = 4, niters = 1000
)

# Stan model with CmdStanR Pathfinder
analysis <- analyse(stan_model, data,
  stan_engine = "cmdstan-pathfinder",
  niters = 500
)

# JAGS model
analysis <- analyse(jags_model, data, nchains = 4, niters = 2000)

# Passing engine-specific arguments
analysis <- analyse(stan_model, data,
  stan_engine = "cmdstan-mcmc",
  nchains = 4, niters = 2000,
  adapt_delta = 0.99, # cmdstanr::sample argument
  iter_warmup = 500L
) # cmdstanr::sample argument

analysis <- analyse(stan_model, data,
  nchains = 4, niters = 2000,
  control = list(adapt_delta = 0.95)
) # rstan::sampling argument

# Multiple datasets
data_list <- list(dataset1 = data1, dataset2 = data2)
analyses <- analyse(model, data_list, nchains = 3)
} # }