convertbeta: The convertbeta() function

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

Description

The covertbeta function is designed to convert the regression coefficients derived from the standardized data.

Usage

convertbeta(X, Y, q, beta0)

Arguments

X	It is a dataset of explanatory variables.
Y	It is the multivariate response variables.
q	It is an integer representing the number of explanatory variables and intercept.
beta0	The vector contains the regression coefficients result from sparsenetgls.

Value

Return the list of converted regression coefficients of the explanatory variables 'betaconv' and intercept value 'betaconv_int'.

Examples

X <- mvrnorm(n=20,mu=rep(0,5),Sigma=Diagonal(5,rep(1,5)))
Y <- mvrnorm(n=20,mu=rep(0.5,10),Sigma=Diagonal(10,rep(1,10)))
fitmodel <-  sparsenetgls(responsedata=Y,predictdata=X,nlambda=5,ndist=2,
method='elastic')
#Example of converting the regression coef of the first lamda
convertbeta(X=X,Y=Y,q=5+1,beta0=fitmodel$beta[,1])

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