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, 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, adapt = NULL, burnin = NULL, sample = NULL, thin = NULL, autofit = NULL, parallel = NULL, autofit_control = NULL, convergence_checks = NULL, save = "all", seed = NULL, silent = TRUE, ... )
object  a fitted RoBMA object 

refit_failed  whether failed models should be refitted. Relevant only
if new priors or 
prior_effect  prior distribution for the effect size ( 
prior_heterogeneity  prior distribution for the heterogeneity 
prior_bias  prior distribution for the publication bias adjustment
component that will be treated as belonging to the alternative hypothesis.
Defaults to 
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 ( 
prior_heterogeneity_null  prior distribution for the heterogeneity 
prior_bias_null  prior distribution for the publication bias adjustment
component that will be treated as belonging to the null hypothesis.
Defaults to 
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 
burnin  a number of burnin iterations of the MCMC algorithm.
Defaults to 
sample  a number of sampling iterations of the MCMC algorithm.
Defaults to 
thin  a thinning of the chains of the MCMC algorithm. Defaults to

autofit  whether the model should be fitted until the convergence
criteria (specified in 
parallel  whether the individual models should be fitted in parallel.
Defaults to 
autofit_control  allows to pass autofit control settings with the

convergence_checks  automatic convergence checks to assess the fitted
models, passed with 
save  whether all models posterior distributions should be kept
after obtaining a modelaveraged result. Defaults to 
seed  a seed to be set before model fitting, marginal likelihood
computation, and posterior mixing for reproducibility of results. Defaults
to 
silent  whether all print messages regarding the fitting process
should be suppressed. Defaults to 
...  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 (RoBMAPSMA) 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("onesided", 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) }