RoBMA
is used to estimate a robust Bayesian
meta-regression. The interface allows a complete customization of
the ensemble with different prior (or list of prior) distributions
for each component.
Usage
RoBMA.reg(
formula,
data,
test_predictors = TRUE,
study_names = NULL,
study_ids = NULL,
transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
standardize_predictors = TRUE,
effect_direction = "positive",
priors = NULL,
model_type = NULL,
rescale_priors = 1,
priors_effect = set_default_priors("effect", rescale = rescale_priors),
priors_heterogeneity = set_default_priors("heterogeneity", rescale = rescale_priors),
priors_bias = set_default_priors("bias", rescale = rescale_priors),
priors_effect_null = set_default_priors("effect", null = TRUE),
priors_heterogeneity_null = set_default_priors("heterogeneity", null = TRUE),
priors_bias_null = set_default_priors("bias", null = TRUE),
priors_hierarchical = set_default_priors("hierarchical"),
priors_hierarchical_null = set_default_priors("hierarchical", null = TRUE),
prior_covariates = set_default_priors("covariates", rescale = rescale_priors),
prior_covariates_null = set_default_priors("covariates", null = TRUE),
prior_factors = set_default_priors("factors", rescale = rescale_priors),
prior_factors_null = set_default_priors("factors", null = TRUE),
chains = 3,
sample = 5000,
burnin = 2000,
adapt = 500,
thin = 1,
parallel = FALSE,
autofit = TRUE,
autofit_control = set_autofit_control(),
convergence_checks = set_convergence_checks(),
save = "all",
seed = NULL,
silent = TRUE,
...
)
Arguments
- formula
a formula for the meta-regression model
- data
a data.frame containing the data for the meta-regression. Note that the column names have to correspond to the effect sizes (
d
,logOR
,OR
,r
,z
), a measure of sampling variability (se
,v
,n
,lCI
,uCI
,t
), and the predictors. Seecombine_data()
for a complete list of reserved names and additional information about specifying input data.- test_predictors
vector of predictor names to test for the presence of moderation (i.e., assigned both the null and alternative prior distributions). Defaults to
TRUE
, all predictors are tested using the default prior distributions (i.e.,prior_covariates
,prior_covariates_null
,prior_factors
, andprior_factors_null
). To only estimate and adjust for the effect of predictors useFALSE
. Ifpriors
is specified, any settings intest_predictors
is overridden.- study_names
an optional argument with the names of the studies
- study_ids
an optional argument specifying dependency between the studies (for using a multilevel model). Defaults to
NULL
for studies being independent.- transformation
transformation to be applied to the supplied effect sizes before fitting the individual models. Defaults to
"fishers_z"
. We highly recommend using"fishers_z"
transformation since it is the only variance stabilizing measure and does not bias PET and PEESE style models. The other options are"cohens_d"
, correlation coefficient"r"
and"logOR"
. Supplying"none"
will treat the effect sizes as unstandardized and refrain from any transformations.- prior_scale
an effect size scale used to define priors. Defaults to
"cohens_d"
. Other options are"fishers_z"
, correlation coefficient"r"
, and"logOR"
. The prior scale does not need to match the effect sizes measure - the samples from prior distributions are internally transformed to match thetransformation
of the data. Theprior_scale
corresponds to the effect size scale of default output, but can be changed within the summary function.- standardize_predictors
whether continuous predictors should be standardized prior to estimating the model. Defaults to
TRUE
.- effect_direction
the expected direction of the effect. Correctly specifying the expected direction of the effect is crucial for one-sided selection models, as they specify cut-offs using one-sided p-values. Defaults to
"positive"
(another option is"negative"
).- priors
named list of prior distributions for each predictor (with names corresponding to the predictors). It allows users to specify both the null and alternative hypothesis prior distributions for each predictor by assigning the corresponding element of the named list with another named list (with
"null"
and"alt"
). If only one prior is specified for a given parameter, it is assumed to correspond to the alternative hypotheses and the default null hypothesis is specified (i.e.,prior_covariates_null
orprior_factors_null
). If a named list with only one named prior distribution is provided (either"null"
or"alt"
), only this prior distribution is used and no default distribution is filled in. Parameters without specified prior distributions are assumed to be only adjusted for using the default alternative hypothesis prior distributions (i.e.,prior_covariates
orprior_factors
). Ifpriors
is specified,test_predictors
is ignored.- model_type
string specifying the RoBMA ensemble. Defaults to
NULL
. The other options are"PSMA"
,"PP"
, and"2w"
which override settings passed to thepriors_effect
,priors_heterogeneity
,priors_effect
,priors_effect_null
,priors_heterogeneity_null
,priors_bias_null
, andpriors_effect
. See details for more information about the different model types.- rescale_priors
a re-scaling factor for the prior distributions. The re-scaling factor allows to adjust the width of all default priors simultaneously. Defaults to
1
.- priors_effect
list of prior distributions for the effect size (
mu
) parameter that will be treated as belonging to the alternative hypothesis. Defaults to a standard normal distributionprior(distribution = "normal", parameters = list(mean = 0, sd = 1))
.- priors_heterogeneity
list of prior distributions for the heterogeneity
tau
parameter that will be treated as belonging to the alternative hypothesis. Defaults toprior(distribution = "invgamma", parameters = list(shape = 1, scale = .15))
that is based on heterogeneities estimates from psychology erp2017estimatesRoBMA.- priors_bias
list of prior distributions for the publication bias adjustment component that will be treated as belonging to the alternative hypothesis. Defaults to
list( prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) )
, corresponding to the RoBMA-PSMA model introduce by bartos2021no;textualRoBMA.- priors_effect_null
list of prior distributions for the effect size (
mu
) parameter that will be treated as belonging to the null hypothesis. Defaults to a point null hypotheses at zero,prior(distribution = "point", parameters = list(location = 0))
.- priors_heterogeneity_null
list of prior distributions for the heterogeneity
tau
parameter that will be treated as belonging to the null hypothesis. Defaults to a point null hypotheses at zero (a fixed effect meta-analytic models),prior(distribution = "point", parameters = list(location = 0))
.- priors_bias_null
list of prior weight functions for the
omega
parameter that will be treated as belonging to the null hypothesis. Defaults no publication bias adjustment,prior_none()
.- priors_hierarchical
list of prior distributions 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 whenstudy_ids
are supplied. Note that this is an experimental feature and see News for more details. Defaults to a beta distributionprior(distribution = "beta", parameters = list(alpha = 1, beta = 1))
.- priors_hierarchical_null
list of prior distributions for the correlation of random effects (
rho
) parameter that will be treated as belonging to the null hypothesis. Defaults toNULL
.- prior_covariates
a prior distributions for the regression parameter of continuous covariates on the effect size under the alternative hypothesis (unless set explicitly in
priors
). Defaults to a relatively wide normal distributionprior(distribution = "normal", parameters = list(mean = 0, sd = 0.25))
.- prior_covariates_null
a prior distributions for the regression parameter of continuous covariates on the effect size under the null hypothesis (unless set explicitly in
priors
). Defaults to a no effectprior("spike", parameters = list(location = 0))
.- prior_factors
a prior distributions for the regression parameter of categorical covariates on the effect size under the alternative hypothesis (unless set explicitly in
priors
). Defaults to a relatively wide multivariate normal distribution specifying differences from the mean contrastsprior_factor("mnormal", parameters = list(mean = 0, sd = 0.25), contrast = "meandif")
.- prior_factors_null
a prior distributions for the regression parameter of categorical covariates on the effect size under the null hypothesis (unless set explicitly in
priors
). Defaults to a no effectprior("spike", parameters = list(location = 0))
.- chains
a number of chains of the MCMC algorithm.
- sample
a number of sampling iterations of the MCMC algorithm. Defaults to
5000
.- burnin
a number of burnin iterations of the MCMC algorithm. Defaults to
2000
.- adapt
a number of adaptation iterations of the MCMC algorithm. Defaults to
500
.- thin
a thinning of the chains of the MCMC algorithm. Defaults to
1
.- 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
whether the model should be fitted until the convergence criteria (specified in
autofit_control
) are satisfied. Defaults toTRUE
.- 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 thatparallel = TRUE
also suppresses all messages.- ...
additional arguments.
Details
The vignette("/MetaRegression", package = "RoBMA")
vignette describes how to use RoBMA.reg()
function to fit Bayesian meta-regression ensembles. See
bartos2023robust;textualRoBMA for more details about the methodology and
RoBMA()
for more details about the function options.
The RoBMA.reg function first generates models from a combination of the provided priors for each of the model parameters. Then, the individual models are fitted using autorun.jags function. A marginal likelihood is computed using bridge_sampler function. The individual models are then combined into an ensemble using the posterior model probabilities using BayesTools package.
Generic summary.RoBMA()
, print.RoBMA()
, and plot.RoBMA()
functions are
provided to facilitate manipulation with the ensemble. A visual check of the
individual model diagnostics can be obtained using the diagnostics()
function.
The fitted model can be further updated or modified by update.RoBMA()
function.
Estimated marginal means can be computed by marginal_summary()
function and
visualized by the marginal_plot()
function.
Examples
if (FALSE) { # \dontrun{
# using the example data from Andrews et al. (2021) and reproducing the example from
# Bartos et al. (2024) with measure and age covariate.
# note the the Andrews2021 data.frame columns identify the effect size "r" and
# the standard error "se" of the effect size that are used to estimate the model
fit_RoBMA <- RoBMA.reg(~ measure + age, data = Andrews2021, parallel = TRUE, seed = 1)
# summarize the results
summary(fit_RoBMA, output_scale = "r")
# compute effect size estimates for each group
marginal_summary(fit_RoBMA, output_scale = "r")
# visualize the effect size estimates for each group
marginal_plot(fit_RoBMA, parameter = "measure", output_scale = "r", lwd = 2)
} # }