greg: Regularized graphical model estimation

In pgraph: Build Dependency Graphs using Projection

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

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

Example output

[1] 0.8479532
[1] 0.8464143
[1] 0.8790397

pgraph documentation built on Jan. 21, 2020, 5:07 p.m.