Description Usage Arguments Details Value References Examples
Estimates a sparse inverse covariance matrix using Sparse Column-wise Inverse Operator
1 | scio(S, lambda, thr=1e-4, maxit=1e4, pen.diag=F, sym=T)
|
S |
Input covariance matrix of size p by p (symmetric). |
lambda |
(Non-negative) regularization parameter for the lasso penalty. Can be a scalar or a matrix of size p by p. |
thr |
Threshold for convergence. Iterations stop when the maximum
change in two successive updates is less than |
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. |
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.
A list with components:
w |
Estimated inverse covariance matrix |
Weidong Liu and Xi Luo (2012). Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions. arXiv:1203.3896.
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