`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`

from CRAN

`install.packages("hdbm")`

or from github via `devtools`

```
# install.packages(devtools)
devtools::install_github("umich-cphds/hdbm", built_opts = c())
```

`hdbm`

requires the R packages `Rcpp`

and `RcppArmadillo`

, so you may want to install / update them before downloading. If you decide to install `hdbm`

from source (eg github), you will need a C++ compiler that supports C++11. On Windows this can accomplished by installing Rtools, and Xcode on MacOS.

`hdbm`

contains a semi-synthetic example data set, `hdbm.data`

that is used in this example. `hdbm.data`

contains a continuous response `y`

and a continuous exposure `a`

that is mediated by 100 mediators, `m[1:100]`

.

```
library(hdbm)
# print just the first 10 columns
head(hdbm.data[,1:10])
#> y a m1 m2 m3 m4
#> 1 -0.5077701 1.3979467 -0.75395346 -0.2787043 -0.04471833 0.3422936
#> 2 -0.3239898 -0.2311032 -1.20208195 0.4210638 0.93175992 -0.3699733
#> 3 -1.8553536 -2.4647028 -0.65712133 0.3285993 0.59144748 -0.1307554
#> 4 0.1685455 0.1119932 0.04982723 0.6816996 -0.12956715 -0.8348541
#> 5 0.9070900 0.4994626 -0.99964057 -0.7660710 1.53962908 -0.9308951
#> 6 -1.0357105 0.6359685 1.06954128 -0.2441489 -1.52176072 0.4657214
#> m5 m6 m7 m8
#> 1 -0.66227113 -0.30925865 1.58001664 0.008326522
#> 2 1.09811497 -0.09969085 1.02369272 0.045104531
#> 3 -0.30196963 0.38853526 -0.05841533 -0.436429826
#> 4 0.08936191 -0.69699157 0.41615473 0.973411472
#> 5 1.12107670 1.07603088 0.37449777 -0.289794580
#> 6 -1.55992443 -0.42705075 -0.98761802 -0.639473238
```

The mediators have an internal correlation structure that is based off the covariance matrix from the Multi-Ethnic Study of Atherosclerosis (MESA) data. However, `hdbm`

does not model internal correlation between mediators. Instead, `hdbm`

employs continuous Bayesian shrinkage priors to select mediators and assumes that all the potential mediators contribute small effects in mediating the exposure-outcome relationship, but only a small proportion of mediators exhibit large effects.

We use no adjustment covariates in this example, so we just include the intercept. Also, in a real world situation, it may be beneficial to normalize the input data.

```
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)
# Initial guesses for coefficients
beta.m <- rep(0, ncol(M))
alpha.a <- rep(0, ncol(M))
set.seed(12345)
# It is recommended to pick a larger number for burnin.
hdbm.out <- hdbm(Y, A, M, C, C, beta.m, alpha.a,
burnin = 1000, ndraws = 100)
# Which mediators are active?
active <- which(colSums(hdbm.out$r1 * hdbm.out$r3) > 100 / 2)
colnames(M)[active]
#> [1] "m12" "m65" "m89"
```

Here, we calculate the posterior inclusion probability `r1 = r3 = 1 | Data`

, and classify a mediator as active if its posterior probability is greater than 0.5.

Yanyi Song, Xiang Zhou et al. Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies. bioRxiv 10.1101/467399

If you would like to report a bug, ask questions, or suggest something, please e-mail Alexander Rix at `alexrix@umich.edu`

.