palasso: Paired lasso

In palasso: Sparse Regression with Paired Covariates

Paired lasso

Description

The function palasso fits the paired lasso. Use this function if you have paired covariates and want a sparse model.

Usage

palasso(y = y, X = X, max = 10, ...)

Arguments

y	response: vector of length n
X	covariates: list of matrices, each with n rows (samples) and p columns (variables)
max	maximum number of non-zero coefficients: positive numeric, or NULL (no sparsity constraint)
...	further arguments for cv.glmnet or glmnet

Details

Let x denote one entry of the list X. See glmnet for alternative specifications of y and x. Among the further arguments, family must equal "gaussian", "binomial", "poisson", or "cox", and penalty.factor must not be used.

Hidden arguments: Deactivate adaptive lasso by setting adaptive to FALSE, activate standard lasso by setting standard to TRUE, and activate shrinkage by setting shrink to TRUE.

Value

This function returns an object of class palasso. Available methods include predict, coef, weights, fitted, residuals, deviance, logLik, and summary.

References

Armin Rauschenberger, Iiuliana Ciocanea-Teodorescu, Marianne A. Jonker, Renee X. Menezes, and Mark A. van de Wiel (2020). "Sparse classification with paired covariates." Advances in Data Analysis and Classification 14:571-588. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11634-019-00375-6")}. (Click here to access PDF. Contact: [e-mail].)

Examples

set.seed(1)
n <- 50; p <- 20
y <- rbinom(n=n,size=1,prob=0.5)
X <- lapply(1:2,function(x) matrix(rnorm(n*p),nrow=n,ncol=p))
object <- palasso(y=y,X=X,family="binomial") # adaptive=TRUE,standard=FALSE
names(object)

palasso documentation built on Sept. 30, 2024, 9:30 a.m.