screen_bic.glasso {nethet} | R Documentation

## BIC-tuned glasso with additional thresholding

### Description

BIC-tuned glasso with additional thresholding

### Usage

    screen_bic.glasso(x, include.mean = TRUE, length.lambda = 20,
      lambdamin.ratio = ifelse(ncol(x) > nrow(x), 0.01, 0.001),
      penalize.diagonal = FALSE, plot.it = FALSE,
      trunc.method = "linear.growth", trunc.k = 5, use.package = "huge",
      verbose = FALSE)

### Arguments

`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).

### Value

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.

### Author(s)

n.stadler

### Examples

    n=50
    p=5
    x=matrix(rnorm(n*p),n,p)
    wihat=screen_bic.glasso(x,length.lambda=5)$wi

[Package nethet version 1.26.0 Index]