meta_mixed_by: Function to run Bayesian random-effects meta-regression on...

In UNCDEPENdLab/fmri.pipeline: Analysis of fMRI Data on HPC clusters

Function to run Bayesian random-effects meta-regression on coefficients from mixed_by

Description

Function to run Bayesian random-effects meta-regression on coefficients from mixed_by

Usage

meta_mixed_by(
  coef_df,
  terms = "all",
  fit_subsets = "all",
  max_order = 3,
  outcome = NULL,
  fixef = NULL,
  rhs = NULL,
  brms_args = list(chains = 4, cores = 4, iter = 12000)
)

Arguments

coef_df	A tidy data.frame of coefficients produced by mixed_by.
terms	Which model terms to fit in meta-regression. If 'all', then all unique values of the term column of coef_df will be fit in separate meta-regression models. Alternatively, a character vector of terms can be specified to run meta-regressions on a smaller subset
fit_subsets	If TRUE or "all", then all subsets of the split variables will be tested in separate meta-regression models. This can be useful for model comparison tests to examine whether a given factor has an overall effect. If FALSE or "none", no split variable subsets will be fit. If "individual", then the split variables are added in subsets individually, but their interactions are not.

UNCDEPENdLab/fmri.pipeline documentation built on April 3, 2025, 3:21 p.m.