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README.Rmd
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---
output: md_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!-- badges: start -->
[](https://cran.r-project.org/package=bama)
# Bayesian Mediation Analysis
Perform mediation analysis in the presence of high-dimensional
mediators based on the potential outcome framework. Bayesian Mediation
Analysis (BAMA), developed by Song et al (2019) and
Song et al (2020),
relies on two Bayesian sparse linear mixed models to simultaneously analyze
a relatively large number of mediators for a continuous exposure and outcome
assuming a small number of mediators are truly active. This sparsity
assumption also allows the extension of univariate mediator analysis by
casting the identification of active mediators as a variable selection
problem and applying Bayesian methods with continuous shrinkage priors on
the effects.
## Installation
You can install `bama` via CRAN
```{r, eval = F}
install.packages("bama")
```
Or devtools
```{r, eval = F}
devtools::install_github("umich-cphds/bama", build_opts = c())
```
The Github version may contain new features or bug fixes not yet present on
CRAN, so if you are experiencing issues, you may want to try the Github
version of the package.
If you wish to install the package via `devtools`, you will need a C++ compiler
installed. This can be accomplished by installing Rtools on Windows and Xcode
on MacOS.
## Example
This example is taken from the `bama` help file to help you get started using
the method. Please check the documentation of the function by typing
`?bama::bama`, and the vignette by typing `vingette("bama")` in R.
`bama` includes an example dataset, `bama.data`. It is a `data.frame` with a
numeric response `y`, numeric exposure `a` and 100 numeric mediators named
`m1, m2, ..., m100`.
We recommend using much larger numbers for `burnin` and `ndraws`, for
example (30000, 35000).
```{r, eval = FALSE}
library(bama)
Y <- bama.data$y
A <- bama.data$a
# grab the mediators from the example data.frame
M <- as.matrix(bama.data[, paste0("m", 1:100)], nrow(bama.data))
# We just include the intercept term in this example as we have no covariates
C1 <- matrix(1, 1000, 1)
C2 <- matrix(1, 1000, 1)
beta.m <- rep(0, 100)
alpha.a <- rep(0, 100)
out <- bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "BSLMM", seed = 1234,
burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL,
control = list(k = 2, lm0 = 1e-04, lm1 = 1, l = 1))
# The package includes a function to summarise output from 'bama'
summary <- summary(out)
head(summary)
# Product Threshold Gaussian
ptgmod = bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "PTG", seed = 1234,
burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL,
control = list(lambda0 = 0.04, lambda1 = 0.2, lambda2 = 0.2))
mean(ptgmod$beta.a)
apply(ptgmod$beta.m, 2, mean)
apply(ptgmod$alpha.a, 2, mean)
apply(ptgmod$betam_member, 2, mean)
apply(ptgmod$alphaa_member, 2, mean)
# Gaussian Mixture Model
gmmmod = bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "GMM", seed = 1234,
burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL,
control = list(phi0 = 0.01, phi1 = 0.01))
mean(gmmmod$beta.a)
apply(gmmmod$beta.m, 2, mean)
apply(gmmmod$alpha.a, 2, mean)
mean(gmmmod$sigma.sq.a)
mean(gmmmod$sigma.sq.e)
mean(gmmmod$sigma.sq.g)
```
# References
Song, Y, Zhou, X, Zhang, M, et al. Bayesian shrinkage estimation of high
dimensional causal mediation effects in omics studies. Biometrics. 2019;
1-11.
Song, Yanyi, Xiang Zhou, Jian Kang, Max T. Aung, Min Zhang, Wei Zhao, Belinda L. Needham et al. "Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects." arXiv preprint arXiv:2008.06366 (2020).