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) .


version Build Status

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:

?vennLasso

Help file for cross validation function cv.vennLasso() can be accessed by running:

?cv.vennLasso

A 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 matrices
x        <- dat.sim$x
x.test   <- dat.sim$x.test
 
# response vectors
y        <- dat.sim$y
y.test   <- dat.sim$y.test
 
# binary stratifying factors
grp      <- dat.sim$group.ind
grp.test <- dat.sim$group.ind.test

Inspect 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 covariates
form <- 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

News

Reference manual

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install.packages("vennLasso")

0.1.5 by Jared Huling, 6 months ago


https://github.com/jaredhuling/vennLasso


Report a bug at https://github.com/jaredhuling/vennLasso/issues


Browse source code at https://github.com/cran/vennLasso


Authors: Jared Huling [aut, cre] , Muxuan Liang [ctb] , Yixuan Qiu [cph] , Gael Guennebaud [cph] , Ray Gardner [cph] , Jitse Niesen [cph]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp, foreach, survival, MASS, Matrix, VennDiagram, visNetwork, igraph, methods

Suggests knitr, rmarkdown

Linking to Rcpp, RcppEigen, RcppNumerical


See at CRAN