Description Usage Arguments Value Author(s) Examples
BIC-tuned glasso with additional thresholding
1 2 3 4 5 |
x |
The input data. Needs to be a num.samples by dim.samples matrix. |
include.mean |
Include mean in likelihood. TRUE / FALSE (default). |
length.lambda |
Length of lambda path to consider (default=20). |
lambdamin.ratio |
Ratio lambda.min/lambda.max. |
penalize.diagonal |
If TRUE apply penalization to diagonal of inverse covariance as well. (default=FALSE) |
plot.it |
TRUE / FALSE (default) |
trunc.method |
None / linear.growth (default) / sqrt.growth |
trunc.k |
truncation constant, number of samples per predictor (default=5) |
use.package |
'glasso' or 'huge' (default). |
verbose |
If TRUE, output la.min, la.max and la.opt (default=FALSE). |
Returns a list with named elements 'rho.opt', 'wi', 'wi.orig', Variable rho.opt is the optimal (scaled) penalization parameter (rho.opt=2*la.opt/n). The variables wi and wi.orig are matrices of size dim.samples by dim.samples containing the truncated and untruncated inverse covariance matrix.
n.stadler
1 2 3 4 | n=50
p=5
x=matrix(rnorm(n*p),n,p)
wihat=screen_bic.glasso(x,length.lambda=5)$wi
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