RoBMA (version 3.5.0)

update.RoBMA: Updates a fitted RoBMA object

Description

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.

Usage

# S3 method for RoBMA
update(
  object,
  refit_failed = TRUE,
  extend_all = FALSE,
  prior_effect = NULL,
  prior_heterogeneity = NULL,
  prior_bias = NULL,
  prior_hierarchical = NULL,
  prior_weights = NULL,
  prior_effect_null = NULL,
  prior_heterogeneity_null = NULL,
  prior_bias_null = NULL,
  prior_hierarchical_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,
  ...
)

Value

RoBMA returns an object of class 'RoBMA'.

Arguments

object

a fitted RoBMA 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_bias

prior distribution for the publication bias adjustment component that will be treated as belonging to the alternative hypothesis. Defaults to NULL.

prior_hierarchical

prior distribution for the correlation of random effects (rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. Note that this is an experimental feature and see News for more details. Defaults to a beta distribution prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

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_bias_null

prior distribution for the publication bias adjustment component that will be treated as belonging to the null hypothesis. Defaults to NULL.

prior_hierarchical_null

prior distribution for the correlation of random effects (rho) parameter 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 RoBMA() for more details.

See Also

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)))

# add an additional model with different priors specification
# (see '?prior' for more information)
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))))

# update the models with an increased number of sample iterations
fit3 <- update(fit, autofit_control = set_autofit_control(sample_extend = 1000), extend_all = TRUE)
}