Paired lasso — palasso • palasso

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

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

Value

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

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.

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. doi:10.1007/s11634-019-00375-6 . (Click here to access PDF. Contact: armin.rauschenberger@uni.lu.)

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)
#> [1] "adaptive_x"  "adaptive_z"  "adaptive_xz" "within_xz"