Publication-bias prior distributions are specified separately from the
meta-analytic prior distributions documented in
prior_specification. They define selection-model weights,
PET/PEESE regression coefficients, or the publication-bias model space in
RoBMA.
prior distribution for publication-bias adjustment. For
bselmodel, this is usually a weightfunction prior created by
prior_weightfunction. For bPET, use
prior_PET. For bPEESE, use
prior_PEESE. For RoBMA, this can be a single
publication-bias prior distribution or a list of publication-bias prior
distributions. In the single-model bias-adjustment constructors, omitted or
NULL uses the corresponding default prior distribution.
prior distribution(s) for null publication-bias
component(s) in RoBMA, usually prior_none().
A single prior distribution object creates one null component, a list creates
multiple null components, and NULL or FALSE omits the null
publication-bias component.
character string specifying predefined publication-bias
model ensembles for RoBMA. One of "PSMA",
"6w", "2w", or "PP".
numeric vector of one-sided p-value cut points for the default
bselmodel weightfunction prior. If prior_bias is supplied,
the prior distribution carries its own p-value cut points.
bselmodel, bPET, and bPEESE fit one
publication-bias adjustment at a time. The prior_bias argument must
match the fitted bias-adjustment type.
| Constructor | Prior constructor | Default prior distribution |
bselmodel() | prior_weightfunction | one-sided cumulative weightfunction with steps = 0.025 |
bPET() | prior_PET | positive Cauchy centered at 0 |
bPEESE() | prior_PEESE | positive Cauchy centered at 0, with UISD-adjusted scale |
The default PET prior distribution uses
RoBMA.get_option("default_bias_PET.scale"). The default PEESE prior
distribution uses RoBMA.get_option("default_bias_PEESE.scale") after
rescaling to the analyzed effect-size measure. The default weightfunction
prior distribution uses RoBMA.get_option("default_bias_weightfunction.alpha")
for each cumulative-Dirichlet alpha parameter.
RoBMA averages over publication-bias components. By default,
prior_bias_null is prior_none() and model_type
determines the alternative components:
"PSMA" | six weight functions, PET, and PEESE |
"6w" | six weight functions |
"2w" | two two-sided weight functions |
"PP" | PET and PEESE |
Custom prior_bias replaces the preset alternative components. If
prior_bias is omitted, model_type still supplies the default
alternative components even when prior_bias_null is customized or
omitted. Setting prior_bias = NULL or prior_bias = FALSE
omits all alternative bias-adjustment models. Setting
prior_bias_null = NULL or prior_bias_null = FALSE omits the
no-bias component.
Each publication-bias component has a prior model weight. User-specified
components receive equal weights unless their prior objects set
prior_weights. The "2w" and "6w" presets split the
alternative bias mass equally across their weight functions, "PP"
splits it equally across PET and PEESE, and "PSMA" assigns half of the
alternative bias mass to the six weight functions combined and one quarter
each to PET and PEESE.
Publication-bias prior distributions are not rescaled by rescale_priors.
The exception is the default PEESE prior distribution, whose scale is adjusted
to the effect-size measure via the unit-information standard deviation.
prior_weightfunction, prior_PET,
prior_PEESE, prior_none,
RoBMA_prior_specification,
prior_specification