scio (version 0.9.0)

scio: Sparse Column-wise Inverse Operator

Description

Estimates a sparse inverse covariance matrix using Sparse Column-wise Inverse Operator

Usage

scio(S, lambda,  thr=1e-4, maxit=1e4, pen.diag=F, sym=T)

Arguments

S

Input covariance matrix of size p by p (symmetric).

lambda

(Non-negative) regularization parameter for the lasso penalty. Can be a scalar or a matrix of size p by p.

thr

Threshold for convergence. Iterations stop when the maximum change in two successive updates is less than thr. Default value is 1e-4.

maxit

Maximum number of iterations for each column computation. Default 10,000.

pen.diag

Whether the diagonal should be penalized. Default False.

sym

Whether the return values should be symmetrized. Default True.

Value

A list with components:

w

Estimated inverse covariance matrix

Details

This is a fast, nonparametric approach to estimate sparse inverse covariance matrices, with possibly really large dimensions. Details of this procedure are described in the reference.

References

Weidong Liu and Xi Luo (2012). Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions. arXiv:1203.3896.

Examples

# NOT RUN {
set.seed(100)
x<-matrix(rnorm(50*20),ncol=4)
s<- var(x)
a<-scio(s, lambda=.01)
# }