plotsngls {sparsenetgls} | R Documentation |
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.
plotsngls(fitgls, lineplot = FALSE, nrow, ncol, structplot = TRUE, ith_lambda = 1)
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. |
Return a plot subject for sparsenetgls including the plot of variance vs lambda and graph structure of the precision matrix estimates.
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)