sciopath: Compute the SCIO estimates for a grid of penalty values

In scio: Sparse Columnwise Inverse Operator

Description

Estimates a sparse inverse covariance matrix using a Sparse Column-wise Inverse Operator, path-following a grid of values for the regularization parameter

Usage

sciopath(S, lambdalist=NULL, thr = 1e-4, maxit = 1e4, pen.diag=F, sym=T)

Arguments

S	Input covariance matrix of size p by p (symmetric).
lambdalist	Vector of non-negative regularization parameters for the lasso penalty. The path is computed from the largest to the smallest value of this vector. If not given, 10 values are generated.
thr	Threshold for convergence. Iterations stop when the maximum change in two successive updates is less than thr. Default 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.

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.

Value

A list with components:

wlist	Estimated covariance matrices, an array of dimension (nrow(s),ncol(n), length(lambdalist))
lambdalist	Regularization parameters used

References

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

Examples

set.seed(100)
x <- matrix(rnorm(50*20),ncol=4)
s <- var(x)
a <- sciopath(s)

scio documentation built on March 3, 2020, 1:07 a.m.