update.RoBMA can be used to

1. add an additional model to an existing "RoBMA" 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.

# S3 method for RoBMA
update(
object,
refit_failed = TRUE,
prior_effect = NULL,
prior_heterogeneity = NULL,
prior_bias = NULL,
prior_weights = NULL,
prior_effect_null = NULL,
prior_heterogeneity_null = NULL,
prior_bias_null = NULL,
study_names = NULL,
chains = 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 RoBMA object whether failed models should be refitted. Relevant only if new priors or prior_weights are not supplied. Defaults to TRUE. prior distribution for the effect size (mu) parameter that will be treated as belonging to the alternative hypothesis. Defaults to NULL. prior distribution for the heterogeneity tau parameter that will be treated as belonging to the alternative hypothesis. Defaults to NULL. prior distribution for the publication bias adjustment component that will be treated as belonging to the alternative hypothesis. Defaults to NULL. 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 distribution for the effect size (mu) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL. prior distribution for the heterogeneity tau parameter that will be treated as belonging to the null hypothesis. Defaults to NULL. prior distribution for the publication bias adjustment component that will be treated as belonging to the null hypothesis. Defaults to NULL. an optional argument with the names of the studies a number of chains of the MCMC algorithm a number of adaptation iterations of the MCMC algorithm. Defaults to 500. a number of burnin iterations of the MCMC algorithm. Defaults to 2000. a number of sampling iterations of the MCMC algorithm. Defaults to 5000. a thinning of the chains of the MCMC algorithm. Defaults to 1. whether the model should be fitted until the convergence criteria (specified in autofit_control) are satisfied. Defaults to TRUE. whether the individual models should be fitted in parallel. Defaults to FALSE. The implementation is not completely stable and might cause a connection error. allows to pass autofit control settings with the set_autofit_control() function. See ?set_autofit_control for options and default settings. automatic convergence checks to assess the fitted models, passed with set_convergence_checks() function. See ?set_convergence_checks for options and default settings. 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. 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. whether all print messages regarding the fitting process should be suppressed. Defaults to TRUE. Note that parallel = TRUE also suppresses all messages. additional arguments.

## Value

RoBMA returns an object of class 'RoBMA'.

## Details

See RoBMA() for more details.

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

## Examples

if (FALSE) {
# using the example data from Bem 2011 and fitting the default (RoBMA-PSMA) model
fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011\$study)

# the update function allows us to change the prior model weights of each model
fit1 <- update(fit, prior_weights = c(0, rep(1, 35)))

fit2 <- update(fit,
priors_effect_null = prior("point", parameters = list(location = 0)),
priors_heterogeneity = prior("normal",
parameters = list(mean = 0, sd = 1),
truncation = list(lower = 0, upper = Inf)),
priors_bias = prior_weightfunction("one-sided",
parameters = list(cuts = c(.05, .10, .20),
alpha = c(1, 1, 1, 1))))

# refit the models with an increased number of sample iterations
fit3 <- update(fit, sample = 10000)
}