concord: CONvex CORrelation selection methoD

In gconcord: Concord method for Graphical Model Selection

Description

Estimates a sparse inverse covariance matrix from a convex pseudo-likelihood function with lasso L1 penalty

Usage

concord(data, lambda, tol = 1e-05, maxit = 100, save.iterates = FALSE,
  ...)

Arguments

data	Data matrix with n observations (rows) and p variables (columns)
lambda	Penalty parameter
tol	Convergence threshold
maxit	Maximum number of iterations before termination
save.iterates	Returns iterates if TRUE
...	ignored

Details

Implements the CONCORD method by Khare, Oh and Rajaratnam (2013) http://arxiv.org/abs/1307.5381

Examples

library(mvtnorm)

## True omega
omega <- matrix(0,3,3)
omega[1,2] <- omega[2,3] <- 2.1
omega <- t(omega) + omega
diag(omega) <- 3

sigma <- solve(omega)

## Generate data
set.seed(60)
data <- rmvnorm(100, rep(0,3), sigma)

## Solve
concord(data,2)

gconcord documentation built on May 2, 2019, 7:30 a.m.