plotsngls: The plotsngls() function

Description Usage Arguments Value Examples

View source: R/plotsngls.R

Description

The plotsngls function is designed to provide the line plots of variance of regression coefficients vs. values of penalized parameter lambda in gls regression, when the tuning parameter d is the maximal value. It also provides the graph structure of the estimated precision matrix in the penalized path.

Usage

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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

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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='lasso')
plotsngls(fitgls, ith_lambda=5)
#plotsngls(fitgls,lineplot=TRUE,structplot=FALSE,nrow=2,ncol=3)

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