covarianceSelection: Covariance Selection with Graphical Lasso

In Sage-Bionetworks/metanetwork: Network meta analysis

Covariance Selection with Graphical Lasso

Description

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.

Sage-Bionetworks/metanetwork documentation built on April 27, 2022, 7:42 a.m.