plotsngls {sparsenetgls}R Documentation

The plotsngls() function

Description

The plotsngls function is designed to provide the line plots of penalized parameter lambda and variance of regression coefficients in gls regression. It also provides the graph structure of the solution to the precision matrix in the penalized path.

Usage

plotsngls(fitgls, lineplot = FALSE, nrow, ncol, structplot = TRUE,
ith_lambda = 1)

Arguments

fitgls

It is a returning object of the sparsnetgls() multivariate generalized least squared regression function.

lineplot

It is a logical indicator. When value=TRUE, it will provide line plot.

nrow

It is a graph parameter representing number of rows in the lineplot.

ncol

It is a graph parameter representing number of columns in the lineplot.

structplot

It is a logical indicator. When value=TRUE, it will provide the structure plot of the specified precision matrix from the series of the sparsenetgls results.

ith_lambda

It is the number for the specified precision matrix to be used in the structplot. It represents the ordering number in the precision matrix series from sparsenetgls.

Value

Return a plot subject for sparsenetgls including the plot of variance vs lambda and graph structure of the precision matrix estimates.

Examples

ndox=5;p=3;n=200
VARknown <- rWishart(1, df=4, Sigma=matrix(c(1,0,0,0,1,0,0,0,1),
nrow=3,ncol=3))
normc <- mvrnorm(n=n,mu=rep(0,p),Sigma=VARknown[,,1])
Y0=normc
##u-beta
u <- rep(1,ndox)
X <- mvrnorm(n=n,mu=rep(0,ndox),Sigma=Diagonal(ndox,rep(1,ndox)))        
X00 <- scale(X,center=TRUE,scale=TRUE)
X0 <- cbind(rep(1,n),X00)
#Add predictors of simulated CNA
abundance1 <- scale(Y0,center=TRUE,scale=TRUE)+as.vector(X00%*%as.matrix(u))

##sparsenetgls()
fitgls <- sparsenetgls(responsedata=abundance1,predictdata=X00,
nlambda=5,ndist=4,method='glasso')
plotsngls(fitgls, ith_lambda=5)
#plotsngls(fitgls,lineplot=TRUE,structplot=FALSE,nrow=2,ncol=3)

[Package sparsenetgls version 1.2.0 Index]