screen_mb {nethet} | R Documentation |
Node-wise Lasso-regressions for GGM estimation
screen_mb(x, include.mean = NULL, folds = 10, length.lambda = 20, lambdamin.ratio = ifelse(ncol(x) > nrow(x), 0.01, 0.001), penalize.diagonal = FALSE, trunc.method = "linear.growth", trunc.k = 5, plot.it = FALSE, se = FALSE, verbose = FALSE)
x |
The input data. Needs to be a num.samples by dim.samples matrix. |
include.mean |
Include mean in likelihood. TRUE / FALSE (default). |
folds |
Number of folds in the cross-validation (default=10). |
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) |
trunc.method |
None / linear.growth (default) / sqrt.growth |
trunc.k |
truncation constant, number of samples per predictor (default=5) |
plot.it |
TRUE / FALSE (default) |
se |
default=FALSE. |
verbose |
If TRUE, output la.min, la.max and la.opt (default=FALSE). |
(Meinshausen-Buehlmann approach)
Returns a list with named elements 'rho.opt', 'wi'. Variable rho.opt is the optimal (scaled) penalization parameter (rho.opt=2*la.opt/n). The variables wi is a matrix of size dim.samples by dim.samples containing the truncated inverse covariance matrix. Variable Mu mean of the input data.
n.stadler
n=50 p=5 x=matrix(rnorm(n*p),n,p) wihat=screen_mb(x)$wi