update.BiBMA: Updates a fitted BiBMA object

In RoBMA: Robust Bayesian Meta-Analyses

Updates a fitted BiBMA object

Description

update.BiBMA can be used to

1. add an additional model to an existing "BiBMA" object by specifying either a null or alternative prior for each parameter and the prior odds of the model (prior_weights), see the vignette("CustomEnsembles") vignette,

2. change the prior odds of fitted models by specifying a vector prior_weights of the same length as the fitted models,

3. refitting models that failed to converge with updated settings of control parameters,

4. or changing the convergence criteria and recalculating the ensemble results by specifying new control argument and setting refit_failed == FALSE.

Usage

## S3 method for class 'BiBMA'
update(
  object,
  refit_failed = TRUE,
  extend_all = FALSE,
  prior_effect = NULL,
  prior_heterogeneity = NULL,
  prior_baseline = NULL,
  prior_weights = NULL,
  prior_effect_null = NULL,
  prior_heterogeneity_null = NULL,
  prior_baseline_null = NULL,
  study_names = NULL,
  chains = NULL,
  adapt = NULL,
  burnin = NULL,
  sample = NULL,
  thin = NULL,
  autofit = NULL,
  parallel = NULL,
  autofit_control = NULL,
  convergence_checks = NULL,
  save = "all",
  seed = NULL,
  silent = TRUE,
  ...
)

Arguments

object	a fitted BiBMA object
refit_failed	whether failed models should be refitted. Relevant only if new priors or prior_weights are not supplied. Defaults to TRUE.
extend_all	extend sampling in all fitted models based on "sample_extend" argument in set_autofit_control() function. Defaults to FALSE.
prior_effect	prior distribution for the effect size (mu) parameter that will be treated as belonging to the alternative hypothesis. Defaults to NULL.
prior_heterogeneity	prior distribution for the heterogeneity tau parameter that will be treated as belonging to the alternative hypothesis. Defaults to NULL.
prior_baseline	prior distribution for the intercepts (pi) of each study that will be treated as belonging to the alternative hypothesis. Defaults to NULL.
prior_weights	either a single value specifying prior model weight of a newly specified model using priors argument, or a vector of the same length as already fitted models to update their prior weights.
prior_effect_null	prior distribution for the effect size (mu) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.
prior_heterogeneity_null	prior distribution for the heterogeneity tau parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.
prior_baseline_null	prior distribution for the intercepts (pi) of each study that will be treated as belonging to the null hypothesis. Defaults to NULL.
study_names	an optional argument with the names of the studies
chains	a number of chains of the MCMC algorithm.
adapt	a number of adaptation iterations of the MCMC algorithm. Defaults to 500.
burnin	a number of burnin iterations of the MCMC algorithm. Defaults to 2000.
sample	a number of sampling iterations of the MCMC algorithm. Defaults to 5000.
thin	a thinning of the chains of the MCMC algorithm. Defaults to 1.
autofit	whether the model should be fitted until the convergence criteria (specified in autofit_control) are satisfied. Defaults to TRUE.
parallel	whether the individual models should be fitted in parallel. Defaults to FALSE. The implementation is not completely stable and might cause a connection error.
autofit_control	allows to pass autofit control settings with the set_autofit_control() function. See ?set_autofit_control for options and default settings.
convergence_checks	automatic convergence checks to assess the fitted models, passed with set_convergence_checks() function. See ?set_convergence_checks for options and default settings.
save	whether all models posterior distributions should be kept after obtaining a model-averaged result. Defaults to "all" which does not remove anything. Set to "min" to significantly reduce the size of final object, however, some model diagnostics and further manipulation with the object will not be possible.
seed	a seed to be set before model fitting, marginal likelihood computation, and posterior mixing for reproducibility of results. Defaults to NULL - no seed is set.
silent	whether all print messages regarding the fitting process should be suppressed. Defaults to TRUE. Note that parallel = TRUE also suppresses all messages.
...	additional arguments.

Details

See BiBMA() for more details.

Value

BiBMA returns an object of class 'BiBMA'.

See Also

BiBMA(), summary.RoBMA(), prior(), check_setup()

RoBMA documentation built on July 26, 2023, 5:13 p.m.