buzzMed v0.1.3

Bayesian Mediation Analysis with Variable Selection

Overview

buzzMed is an R package for Bayesian mediation analysis. It provides tools for exploratory Bayesian mediation models with Bayesian variable selection, supporting continuous and binary mediators and outcomes. The package also includes a longitudinal Bayesian mediation model for repeated-measures data.

Features

Exploratory Bayesian mediation analysis with variable selection
Continuous or binary (0/1) mediators
Continuous or binary (0/1) outcomes
Multiple predictors and candidate mediators
Formula-based model specification
Longitudinal Bayesian mediation for repeated-measures data

Requirements

This package requires JAGS (Just Another Gibbs Sampler) to be installed on your system.

Download JAGS from:

https://mcmc-jags.sourceforge.io/

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("olfactorybulb/buzzMed")

library(buzzMed)

Main Functions

Generalized Two-stage(GT) exploratory Bayesian mediation model

The package provides four functions for exploratory Bayesian mediation analysis based on the mediator and outcome variable types.

Function	Mediator	Outcome
`buzzEBMcontMcontY()`	Continuous	Continuous
`buzzEBMcontMcatY()`	Continuous	Binary
`buzzEBMcatMcontY()`	Binary	Continuous
`buzzEBMcatMcatY()`	Binary	Binary

Longitudinal Bayesian Mediation

longBMed() fits a Bayesian mediation model for repeated-measures data with one or more predictors, multiple candidate mediators, and a continuous outcome.

Example Usage

GT-Exploratory Bayesian Mediation

library(buzzMed)

# Create toy data
my_data <- data.frame(
  MyPredictor = rnorm(30),
  MyMediator1 = rnorm(30),
  MyMediator2 = rnorm(30),
  MyOutcome = rnorm(30)
)

# Fit the model
fit <- buzzEBMcontMcontY(
  model = "MyOutcome ~ MyPredictor | MyMediator1 + MyMediator2",
  dataset = my_data
)

Longitudinal Bayesian Mediation

library(buzzMed)

# Load the example longitudinal dataset
data(sublongspikes)

# Fit the longitudinal Bayesian mediation model
# For model specification, slice 1 is the predictor, slices 2--20 are candidate mediators, and slice 21 is the outcome.
# n.burnin and n.iter are optional arguments
results <- longBMed(
  model = "21 ~ 1 | 2:20",
  data = sublongspikes,
  n.burnin = 100,
  n.iter = 500
)

summary(results)

Included Example Datasets

singlespikes: Cross-sectional mediation dataset containing one predictor, nineteen candidate mediators, and one outcome.
sublongspikes: Longitudinal mediation dataset stored as a three-dimensional array for repeated-measures analysis with longBMed().
framing2: A modified version of the framing dataset from the mediation package containing dichotomized candidate mediators for demonstrating the GT-exploratory Bayesian mediation functions.

Citation

If you use buzzMed in your research, please cite:

Shi, D., Shi, D., & Fairchild, A. J. (2023). Variable Selection for Mediators under a Bayesian Mediation Model. Structural Equation Modeling: A Multidisciplinary Journal, 30(6), 887–900. https://doi.org/10.1080/10705511.2022.2164285

License

This project is licensed under the GNU General Public License v3.0.

See the LICENSE file for details.