Description Usage Arguments Details Value References Examples
Refitted SCIO Estimators using Penalized Likelihood
1 | scio.refit(S, Omega,thr=1e-4, pkg = c("QUIC","glasso"),...)
|
S |
Input covariance matrix of size p by p (symmetric). |
Omega |
Estimated inverse covariance matrices. Can be a matrix of
size p by p from |
thr |
Tolerance. Small entries in magnitude (< |
pkg |
R packge to be used for refitting. Default |
... |
Additional options passed on to |
This implements the refitting procedure discussed in Cai, Liu, and Luo
(2011). The current version uses the QUIC
solver for the
penalized likelihood criterion. More solvers will be added.
A list with one component:
w |
Estimated inverse covariance matrix when a single
|
Weidong Liu and Xi Luo (2012). Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions. arXiv:1203.3896.
Tony Cai, Weidong Liu, and Xi Luo (2011). A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation. Journal of the American Statistical Association, 106(494), 594-607.
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