pgraph (version 1.6)

greg: Regularized graphical model estimation

Description

greg calculate the regularized graphical model estimation using lasso, scad and adaptive lasso penalties. It report the results in the form of roc results for each method.

Usage

greg(z, A, eps = 1e-15, rholist = NULL, gamma = 0.5, trace = FALSE)

Arguments

z

n * p dimensional matrix

A

p * p true graph

eps

a tolerence level for thresholding

rholist

a sequence of penalty parameters

gamma

the adaptive lasso penalty parameter

trace

whether to trace to estimation process.

Value

a list.

roc.lasso

roc results for lasso

roc.scad

roc results for scad

roc.alasso

roc results for adaptive lasso

See Also

pgraph, roc, projcov

Examples

# NOT RUN {
set.seed(0)
p = 20;
n = 300;
tmp=runif(p-1,1,3)
s=c(0,cumsum(tmp));
s1=matrix(s,p,p)
cov.mat.true=exp(-abs(s1-t(s1)))
prec.mat.true=solve(cov.mat.true);
a=matrix(rnorm(p*n),n,p)
data.sa=a%*%chol(cov.mat.true);
true.graph = outer(1:p,1:p,f<-function(x,y){(abs(x-y)==1)})
greg.fit = greg(data.sa, true.graph)
auc.lasso = sum(diff(greg.fit$roc.lasso[,1])*greg.fit$roc.lasso[-1,2])
auc.alasso = sum(diff(greg.fit$roc.alasso[,1])*greg.fit$roc.alasso[-1,2])
auc.scad = sum(diff(greg.fit$roc.scad[,1])*greg.fit$roc.scad[-1,2])
auc.lasso
auc.alasso
auc.scad
# }