Hierarchical Integrative Grouped LASSO

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relation- ship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Hierarchical integrative group LASSO (HiGLASSO) is a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction. HiGLASSO is broadly applicable for studying potential nonlinear main and interaction effects in the presence of group structures among a set of exposures.


higlasso can be installed via Github using devtools

# install.packages("devtools")

You'll need a working C++11 compiler, which can obtained by installing Xcode on MacOS, and RTools on Windows. ## Example

higlasso can be slow, so it may may be beneficial to tweak some its settings (for example, nlambda1 and nlambda2) to get a handle on how long the method will take before running the full model.


X <- as.matrix(higlasso.df[, paste0("V", 1:10)])
Y <- higlasso.df$Y
Z <- matrix(1, nrow(X))

# This can take a bit of time <- cv.higlasso(Y, X, Z)


If you encounter a bug, please open an issue on the Issues tab on Github or send us an email.


For questions or feedback, please email Jonathan Boss at [email protected] or Alexander Rix [email protected].


umich-cphds/higlasso documentation built on Feb. 14, 2020, 8:23 a.m.