update.RoBMA can be used to
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,
change the prior odds of fitted models by specifying a vector
prior_weights of the same length as the fitted models,
refitting models that failed to converge with updated settings of control parameters,
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,
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,
...
)a fitted RoBMA object
whether failed models should be refitted. Relevant only
if new priors or prior_weights are not supplied. Defaults to TRUE.
extend sampling in all fitted models based on "sample_extend"
argument in set_autofit_control() function. Defaults to FALSE.
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.
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)).
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.
prior distribution for the correlation of random effects
(rho) parameter 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.
RoBMA returns an object of class 'RoBMA'.
See RoBMA() for more details.
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)
}