robglasso: glasso with robust covariance estimations

In robustcov: Collection of Robust Covariance and (Sparse) Precision Matrix Estimators

Description

This routine fits glasso using a robust covariance matrix

Usage

robglasso(
  data,
  covest = cov,
  rho = 0.1,
  CV = FALSE,
  k = 10,
  grids = 15,
  evaluation = negLLrobOmega,
  ...
)

Arguments

data	raw data, should be a matrix or a data.frame, row as sample
covest	a *function* or name of a function (string) that takes a matrix to estimate covariance
rho	a scalar or vector of tuning parameters to be chosen, if CV=FALSE, should be a scalar, if CV=TRUE scalar input will be override and tuning parameter will be chosen based on CV
CV	bool, whether doing cross validation for tuning parameter, if set to TRUE and rho is a scalar, the candidate will be chosen automatically by log spacing between 0.01 max covariance and max covariance with number of grids
k	fold for cross validation if applicable
grids	number of candidate tuning parameters in cross validation
evaluation	a *function* or name of a function (string) that takes only two arguments, the estimated *covariance* and the test *covariace*, when NULL, we use negative log likelihood on test sets
...	extra argument sent to glasso::glasso

Value

a glasso return (see ?glasso::glasso), most important one is $X the estimated sparse precision,with an extra entry of tuning parameter lambda

Examples

robglasso(matrix(rnorm(100),20,5))

robustcov documentation built on Aug. 4, 2021, 5:07 p.m.