ridge: Ridge

Description Usage Arguments Value Author(s) Examples

Description

Ridge

Usage

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ridge(X,y,lambda1=0,lambda2=0,alpha=1,beta=rep(1,ncol(X)),group=1:ncol(X),
penalty="quadrupen")

Arguments

X

an matrix object with n*p size

y

an vector of size p

lambda1

The l1 penalty to used.

lambda2

The l2 penalty to use in case of elatic-net for screening.

alpha

The α mixing parameter to use in case of sparse group lasso for screening or if glmnet is used for screening.

beta

a vector of size p representing the weight of variable. A variable with weight equal to zero signify will not appear in the ordinary least square regression. No constraint to the number of variables with a non-zero weight.

group

a vector of size p representing the group index for each variables. Default is 1:ncol(X) which represent the particular case whit no group. Warning : this vector must be in ascending group order and variables ordered in this sense.

penalty

a string of characters to determine the adaptive ridge specific penalty to be used. This should be one of "quadrupen" to elastic-net by quadrupen package, "glmnet" to elastic-net by glmnet package, "grplasso" to group lasso by grplasso package and "SGL" to sparse group lasso by SGL package at screening. By default is "quadrupen".

Value

An list with ridge estimates, y estimates.

Author(s)

JM BECU

Examples

1
 # see ridgeAdap help

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