NoBMA is a wrapper around RoBMA() that can
be used to estimate a (Normal - publication bias unadjusted) Bayesian
model-averaged meta-analysis. The interface allows a complete customization of
the ensemble with different prior (or list of prior) distributions
for each component.
NoBMA(
d = NULL,
r = NULL,
logOR = NULL,
OR = NULL,
z = NULL,
y = NULL,
se = NULL,
v = NULL,
n = NULL,
lCI = NULL,
uCI = NULL,
t = NULL,
study_names = NULL,
study_ids = NULL,
data = NULL,
weight = NULL,
transformation = if (is.null(y)) "fishers_z" else "none",
prior_scale = if (is.null(y)) "cohens_d" else "none",
model_type = NULL,
priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
scale = 0.15)),
priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
0)),
priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
priors_hierarchical_null = NULL,
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,
...
)a vector of effect sizes measured as Cohen's d
a vector of effect sizes measured as correlations
a vector of effect sizes measured as log odds ratios
a vector of effect sizes measured as odds ratios
a vector of effect sizes measured as Fisher's z
a vector of unspecified effect sizes (note that effect size transformations are unavailable with this type of input)
a vector of standard errors of the effect sizes
a vector of variances of the effect sizes
a vector of overall sample sizes
a vector of lower bounds of confidence intervals
a vector of upper bounds of confidence intervals
a vector of t/z-statistics
an optional argument with the names of the studies
an optional argument specifying dependency between the
studies (for using a multilevel model). Defaults to NULL for
studies being independent.
a data object created by the combine_data function. This is
an alternative input entry to specifying the d, r, y, etc...
directly. I.e., you cannot pass the a data.frame and reference to the columns.
specifies likelihood weights of the individual estimates. Notes that this is an untested experimental feature.
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.
a 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 the
transformation of the data. The prior_scale corresponds to
the scale of default output, but can be changed within the summary function.
string specifying the RoBMA ensemble. Defaults to NULL.
The other options are "PSMA", "PP", and "2w" which override
settings passed to the priors_effect, priors_heterogeneity,
priors_effect, priors_effect_null, priors_heterogeneity_null,
priors_bias_null, and priors_effect. See details for more information
about the different model types.
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 distribution
prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).
list of prior distributions for the heterogeneity tau
parameter that will be treated as belonging to the alternative hypothesis. Defaults to
prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15)) that
is based on heterogeneities estimates from psychology (van Erp et al. 2017)
.
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)).
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)).
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 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)).
list of prior distributions for the correlation of random effects
(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.
a number of chains of the MCMC algorithm.
a number of sampling iterations of the MCMC algorithm.
Defaults to 5000.
a number of burnin iterations of the MCMC algorithm.
Defaults to 2000.
a number of adaptation iterations of the MCMC algorithm.
Defaults to 500.
a thinning of the chains of the MCMC algorithm. Defaults to
1.
whether the individual models should be fitted in parallel.
Defaults to FALSE. The implementation is not completely stable
and might cause a connection error.
whether the model should be fitted until the convergence
criteria (specified in autofit_control) are satisfied. Defaults to
TRUE.
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.
NoBMA returns an object of class 'RoBMA'.
See RoBMA() for more details.