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Covariance Selection with Graphical Lasso

Source: R/covarianceSelection.R
covarianceSelection.Rd

Estimates a sparse inverse covariance matrix using a lasso (L1) penalty.

Usage

covarianceSelection(S, rankedEdges)

Arguments

S

Required. A symetric p-by-p covariance matrix.

rankedEdges

Required. A list of ranked edges to be constrained by zero.

Value

A list with components.

  • `w` Estimated inverse covariance matrix.

  • `loglik` Value of maximized log-likelihodo+penalty.

  • `errflag` Memory allocation error flag: 0 means no error; !=0 means memory allocation error - no output returned.

  • `approx` Value of input argument approx.

  • `del` Change in parameter value at convergence.

  • `niter` Number of iterations of outer loop used by algorithm.