PDSCE (version 1.0)

pdsoft: A permutation invariant positive definite and sparse covariance matrix estimate

Description

Computes the sparse and positive definite covariance matrix estimator proposed by Rothman (2012).

Usage

pdsoft(s, lam, tau = 1e-04, init = c("soft", "diag", "dense", "user"), 
       s0 = NULL, i0 = NULL, standard = TRUE, tolin = 1e-08, 
       tolout = 1e-08, maxitin = 10000, maxitout = 1000, quiet = FALSE)

Value

A list withsigmacovariance estimateomegainverse covariance estimatethetacorrelation matrix estimate, will be NULL if standard=FALSEtheta.invinverse correlation matrix estimate, will be NULL if standard=FALSE

Details

See Rothman (2012) for the objective function and more information.

References

Rothman, A. J. (2012). Positive definite estimators of large covariance matrices. Biometrika 99(3): 733-740

See Also

pdsoft.cv

Examples

set.seed(1)
n=10
p=20
true.cov=diag(p)
true.cov[cbind(1:(p-1), 2:p)]=0.4
true.cov[cbind(2:p, 1:(p-1))]=0.4
eo=eigen(true.cov, symmetric=TRUE)
z=matrix(rnorm(n*p), nrow=n, ncol=p)
x=z%*% tcrossprod(eo$vec*rep(eo$val^(0.5), each=p),eo$vec)
s=cov(x)*(n-1)/n
output=pdsoft(s=s, lam=0.3)
output$sigma