# QUIC: QUadratic Inverse Covariance estimation In QUIC: Regularized sparse inverse covariance matrix estimation

## Description

Estimates a sparse inverse covariance matrix using a combination of Newton's method and coordinate descent.

## Usage

 ```1 2``` ```QUIC(S, rho, path = NULL, tol = 1e-04, msg = 1, maxIter = 1000, X.init = NULL, W.init = NULL) ```

## Arguments

 `S` Covariance matrix. A p by p symmetric matrix. `rho` Regularization parameter. It can be a p by p matrix, a vector or scalar. `path` If specified, then rho is scaled with the elements of path and the corresponding inverse covariance matrix estimation is carried out for each value. `tol` Specifes the convergence tolerance. `msg` Controls how verbose messages should be printed during execution. Valid value range: 0–4. `maxIter` Specifies the maximum number of Newton iterations. `X.init` The initial estimate for the regularized inverse covariance matrix. `W.init` The inverse of initial estimate for the regularized inverse covariance matrix.

## Value

 `X` Regularized inverse covariance matrix; an array of matrices when path is used. `W` Inverse of the matrix X. `regloglik` The optimal value for the regularized logarithmic likelihood, an array when path is used. `opt` The optimal value of the minimization problem, an array when path is used. `iter` The number of Newton iterations executed, an array when path is used.

## Author(s)

Matyas A. Sustik (package maintainer), Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar

## References

Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural Information Processing Systems, vol. 24, 2011, p. 2330–2338.

http://www.cs.utexas.edu/users/sustik/papers/invcov.pdf

QUIC documentation built on May 30, 2017, 12:07 a.m.