vennLasso
Variable Selection for Heterogeneous Populations
Provides variable selection and estimation routines for models
with main effects stratified on multiple binary factors. The 'vennLasso' package
is an implementation of the method introduced in Huling, et al. (2017)
The vennLasso package provides methods for hierarchical variable selection for models with covariate effects stratified by multiple binary factors.
Installation and Help Files
The vennLasso package can be installed from CRAN using:
install.packages("vennLasso")The development version can be installed using the devtools package:
devtools::install_github("jaredhuling/vennLasso")or by cloning and building.
Load the vennLasso package:
library(vennLasso)Access help file for the main fitting function vennLasso() by running:
?vennLassoHelp file for cross validation function cv.vennLasso() can be accessed by running:
?cv.vennLassoA Quick Example
Simulate heterogeneous data:
set.seed(100)dat.sim <- genHierSparseData(ncats = 3, # number of stratifying factors nvars = 25, # number of variables nobs = 150, # number of observations per strata nobs.test = 10000, hier.sparsity.param = 0.5, prop.zero.vars = 0.75, # proportion of variables # zero for all strata snr = 0.5, # signal-to-noise ratio family = "gaussian") # design matricesx <- dat.sim$xx.test <- dat.sim$x.test # response vectorsy <- dat.sim$yy.test <- dat.sim$y.test # binary stratifying factorsgrp <- dat.sim$group.indgrp.test <- dat.sim$group.ind.testInspect the populations for each strata:
plotVenn(grp)
Fit vennLasso model with tuning parameter selected with 5-fold cross validation:
fit.adapt <- cv.vennLasso(x, y, grp, adaptive.lasso = TRUE, nlambda = 50, family = "gaussian", standardize = FALSE, intercept = TRUE, nfolds = 5)Plot selected variables for each strata (not run):
library(igraph)##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
plotSelections(fit.adapt)Predict response for test data:
preds.vl <- predict(fit.adapt, x.test, grp.test, s = "lambda.min", type = 'response')Evaluate mean squared error:
mean((y.test - preds.vl) ^ 2)## [1] 0.6852124
mean((y.test - mean(y.test)) ^ 2)## [1] 1.011026
Compare with naive model with all interactions between covariates and stratifying binary factors:
df.x <- data.frame(y = y, x = x, grp = grp)df.x.test <- data.frame(x = x.test, grp = grp.test) # create formula for interactions between factors and covariatesform <- paste("y ~ (", paste(paste0("x.", 1:ncol(x)), collapse = "+"), ")*(grp.1*grp.2*grp.3)" )Fit linear model and generate predictions for test set:
lmf <- lm(as.formula(form), data = df.x) preds.lm <- predict(lmf, df.x.test)Evaluate mean squared error:
mean((y.test - preds.lm) ^ 2)## [1] 0.8056107
mean((y.test - preds.vl) ^ 2)## [1] 0.6852124