hdbm is a Bayesian inference method that uses continuous shrinkage priors for
high-dimensional mediation analysis, developed by Song et al (2018).
hdbm provides estimates for the regression coefficients as well as
the posterior inclusion probability for ranking mediators.
You can install
hdbm via CRAN
devtools::install_github("umich-cphds/hdbm", build_opts = c())
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.
Taken from the
hdbm help file
library(hdbm) Y <- hdbm.data$y A <- hdbm.data$a # grab the mediators from the example data.frame M <- as.matrix(hdbm.data[, paste0("m", 1:100)], nrow(hdbm.data)) # We just include the intercept term in this example. C <- matrix(1, nrow(M), 1) beta.m <- rep(0, 100) alpha.a <- rep(0, 100) set.seed(1245) output <- hdbm(Y, A, M, C, C, beta.m, alpha.a, burnin = 3000, ndraws = 100) # Which mediators are active? active <- which(colSums(output$r1 * output$r3) > 50) colnames(M)[active]
Yanyi Song, Xiang Zhou et al. Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies. bioRxiv 467399
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