glassonet2: The glassonet2() function In sparsenetgls: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression

Description

The glassonet2 function is designed to learn the graph structure, the corresponding precision matrix and covariance matrix by using the graph lasso method.

Usage

 `1` ```glassonet2(Y0, nlambda = nlambda, lambda.min.ratio = 0.001, method) ```

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 nlambda assigned in the parent function sparsenetgls(). `lambda.min.ratio` It is the default parameter set in function huge() in the package 'huge'. Quoted from huge(), it is the minimal value of lambda, being a fraction of the upper bound (MAX) of the regularization/ thresholding parameter that makes all the estimates equal to 0. The default value is 0.001. `method` There are two options for the method parameter which is provided in the huge() function. One is 'glasso' and the other one is 'mb'.

Value

Return the precision matrix 'OMEGAMATRIX', penalized path parameter lambda 'lambda' and covariance matrix 'COVMATRIX'.

Examples

 ```1 2 3 4 5``` ```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]) fitglasso <- glassonet2(Y0=Y0,nlambda=5,method='glasso') ```

sparsenetgls documentation built on Nov. 8, 2020, 7:37 p.m.