QUIC: QUIC function replicated from...

In ruiyangli1/XMInt: Model Selection for Exposure-Mediator Interaction

QUIC function replicated from https://cran.r-project.org/src/contrib/Archive/QUIC/QUIC_1.1.1.tar.gz

Description

QUadratic Inverse Covariance estimation

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

For details, please refer to https://cran.r-project.org/src/contrib/Archive/QUIC/QUIC_1.1.1.tar.gz

Usage

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.

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

ruiyangli1/XMInt documentation built on March 18, 2024, 12:13 p.m.