Description Usage Arguments Value See Also Examples
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.
1 |
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. |
a list.
roc.lasso |
roc results for lasso |
roc.scad |
roc results for scad |
roc.alasso |
roc results for adaptive lasso |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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
|
[1] 0.8479532
[1] 0.8464143
[1] 0.8790397
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