screen_cv.glasso {nethet} | R Documentation

## Cross-validated glasso with additional thresholding

### Description

Cross-validated glasso with additional thresholding

### Usage

    screen_cv.glasso(x, include.mean = FALSE, folds = min(10, dim(x)[1]),
      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, 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).
`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.
`use.package` | 'glasso' or 'huge' (default).
`verbose` | If TRUE, output la.min, la.max and la.opt (default=FALSE).

### Details

Run glasso on a single dataset, using cross-validation to estimate the penalty parameter lambda. Performs additional thresholding (optionally).

### Value

Returns a list with named elements 'rho.opt', 'w', 'wi', 'wi.orig', 'mu'. Variable rho.opt is the optimal (scaled) penalization parameter (rho.opt=2*la.opt/n). Variable w is the estimated covariance matrix. The variables wi and wi.orig are matrices of size dim.samples by dim.samples containing the truncated and untruncated inverse covariance matrix. Variable mu is the mean of the input data.

### Author(s)

n.stadler

### Examples

    n=50
    p=5
    x=matrix(rnorm(n*p),n,p)
    wihat=screen_cv.glasso(x,folds=2)$wi

[Package nethet version 1.26.0 Index]