ridge.permut: Ridge.permut

In jbecu/ridgeAdap: Adaptive Ridge for variable selection

Description

Ridge.permut

Usage

ridge.permut(X,y,lambda1=0,lambda2=0,alpha=1,beta=rep(1,ncol(X)),iter=1000,
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.
iter	Number of iterations used to simulate the null hypothesis law. Bigger is better. Default is 1000.
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".
lawWithOne	a boolean to indicate if one distribution under the null hypothesis for all groups of variables could be used instead of one distribution for each of them. Warning : It is an approximation and that work only if all groups have the same size. Default is FALSE.

Value

An list with ridge estimates, F statistic an F statistics under H0 and p-value for each variables or group significancy. Contains equally pval.gamma obtaines with the gamma law approximation.

Author(s)

JM BECU

Examples

 # see ridgeAdap help

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