lassoglmnet: The lassoglmnet() function

In superOmics/sparsenetgls: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression

Description

The lassoglmnet function is designed to learn the graph structure by using the lasso and elastics net methods.

Usage

lassoglmnet(Y0, nlambda = 10, alpha)

Arguments

Y0	The data matrix for the GGM model.
nlambda	The number of interval used in the penalized path in lasso and elastics. It results in the number of lambda values to be used in the penalization. The default value is 10.
alpha	The value to be used in enet, it has values between 0 and 1. The value of 0 is corresponding to l-1 penalization, and 1 is corresponding to the l-2 regularization (Ridge regression). The other values between 0 and 1 will result in a combination of l1-l2 norm regularization named as elastic net.

Value

Return the regression coefficients of glmnet 'coef_glmnet', residuals from the glmnet 'resid_glmnet' and lambda.

Examples

n=20
VARknown <- rWishart(1,df=4,Sigma=matrix(c(1,0,0,0,1,0,0,0,1),nrow=3,ncol=3))
Y0 <- mvrnorm(n=n,mu=rep(0.5,3),Sigma=VARknown[,,1])
fitlasso <- lassoglmnet(Y0=Y0,alpha=0.5)

superOmics/sparsenetgls documentation built on Sept. 11, 2020, 5:49 a.m.