screening: Screening

In jbecu/ridgeAdap: Adaptive Ridge for variable selection

Description

Screening

Usage

screening(X,y,lambda1 = NULL,lambda2=0,nfolds=10,scale=FALSE,center=TRUE,se1=FALSE)

Arguments

X	a matrix object with n*p size representing the data
y	a vector of size p representing the output variable
lambda1	a vector representing the l1 penalty. Default is NULL and automatically defined by quadrupen.
lambda2	a vector representing the l2 penalty. If lambda2 = 0 that correspond to the lasso solution, otherwise to the elastic-net solution. Default is 0.
nfolds	An integer representing the number of folds for the k-folds cross-validation used to choose l1 and l2 penatly. Default is 10 and must be greater than 2.
scale	a boolean to indicate if X and y will be scaled. Default is FALSE.
center	a boolean to indicate if X and y will be centered. Default is TRUE.
se1	a boolean to indicate if using the se1 rule for the cross validation with nfolds upper than 2.

Value

A object of class 'screening' with 5 elements

beta.min	a vector of size p with the elastic.net estimates
S	a vector contaning the index of variable which $B_j != 0$
lambda1.min	l1 penalty chosen by CV
lambda2.min	l2 penalty chosen by CV
ref	a list with two component contains respectively the X and y variance and mean used to scaling and/or centering the data.

Note

screening requires quadrupen package.

Author(s)

JM BECU

Examples

 # see ridgeAdap help

jbecu/ridgeAdap documentation built on May 18, 2019, 5:58 p.m.