knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of sparsemediation is to conduct sparse mediation analysis.
You can install the released version of sparsemediation from CRAN with:
install.packages("sparsemediation")
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("muschellij2/sparsemediation")
The sparse.mediation
function conducts sparse mediation for the specified tuning parameters for elastic net. Cross-validation is possible using cv.sparse.mediation
.
library(sparsemediation) N = 100 V = 50 set.seed(1234) a = rbinom(V, 1, 0.1) * 5 b <- a X = scale(rnorm(N)) M = X %*% t(a) + matrix(rnorm(N * V), N, V) Y = 10 * X + M %*% b + rnorm(N) cvfit <- cv.sparse.mediation( X, M, Y, tol = 10 ^ (-10), K = 4, max.iter = 100, lambda = log(1 + (1:10) / 25), tau = c(0.5, 1, 2), multicore = 4, seednum = 1e+06 ) # fit <- sparse.mediation( # X, # M, # Y, # tol = 10 ^ (-10), # max.iter = 100, # lambda = cvfit$cv.lambda, # tau = cvfit$cv.tau # )
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