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Set default prior distributions for binomial meta-analytic models

Source: R/priors.R
set_default_binomial_priors.Rd

Set default prior distributions for BiBMA models.

Usage

set_default_binomial_priors(parameter, null = FALSE, rescale = 1)

Arguments

parameter

a character string specifying the parameter for which the prior distribution should be set. Available options are "effect", "heterogeneity", "baseline", "covariates", "factors".

null

a logical indicating whether the prior distribution should be set for the null hypothesis. Defaults to FALSE.

rescale

a numeric value specifying the re-scaling factor for the default prior distributions. Defaults to 1. Allows convenient re-scaling of prior distributions simultaneously.

Value

A prior distribution object or a list of prior distribution objects.

Details

The default prior are based on the binary outcome meta-analyses in the Cochrane Database of Systematic Reviews outlined in bartos2023empirical;textualRoBMA.

Specifically, the prior distributions are:

For the alternative hypothesis:

  • Effect: T distribution with mean 0, scale 0.58, and 4 degrees of freedom.

  • Heterogeneity: Inverse gamma distribution with shape 1.77 and scale 0.55.

  • Baseline: No prior distribution.

  • Standardized continuous covariates: Normal distribution with mean 0 and standard deviation 0.29.

  • Factors (via by-level differences from the grand mean): Normal distribution with mean 0 and standard deviation 0.29.

For the null hypothesis:

  • Effect: Point distribution at 0.

  • Heterogeneity: Point distribution at 0.

  • Baseline: Independent uniform distributions.

  • Standardized continuous covariates: Point distribution at 0.

  • Factors (via by-level differences from the grand mean): Point distribution at 0.

The rescaling factor adjusts the width of the effect, heterogeneity, covariates, factor, and PEESE-style model prior distributions. PET-style and weight function prior distributions are scale-invariant.

Examples


set_default_binomial_priors("effect")
#> Student-t(0, 0.58, 4)
set_default_binomial_priors("heterogeneity")
#> InvGamma(1.77, 0.55)
set_default_binomial_priors("baseline")
#> NULL