Description Usage Arguments Details Value References Examples
Estimates a sparse inverse covariance matrix using a Sparse Column-wise Inverse Operator, path-following a grid of values for the regularization parameter
1 |
S |
Input covariance matrix of size p by p (symmetric). |
lambdalist |
Vector of non-negative regularization parameters for the lasso penalty. The path is computed from the largest to the smallest value of this vector. If not given, 10 values are generated. |
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:
wlist |
Estimated covariance matrices, an array of dimension (nrow(s),ncol(n), length(lambdalist)) |
lambdalist |
Regularization parameters used |
Weidong Liu and Xi Luo (2012). Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions. arXiv:1203.3896.
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