robglasso {robustcov}

glasso with robust covariance estimations

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))