Estimates a sparse inverse covariance matrix using a combination of Newton's method and coordinate descent.
1 2 |
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. |
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. |
Matyas A. Sustik (package maintainer), Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar
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
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.