update.RoBMA | R Documentation |
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 class '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,
...
)
object |
a fitted RoBMA object |
refit_failed |
whether failed models should be refitted. Relevant only
if new priors or |
extend_all |
extend sampling in all fitted models based on |
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_hierarchical |
prior distribution for the correlation of random effects
( |
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 |
prior_hierarchical_null |
prior distribution for the correlation of random effects
( |
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 model-averaged 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. |
See RoBMA()
for more details.
RoBMA
returns an object of class 'RoBMA'.
RoBMA()
, summary.RoBMA()
, prior()
, check_setup()
## Not run:
# 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)
## End(Not run)
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