Calculates sensitivity and specificity of estimating K precision matrices using LASICH. Returns precision matrix estimates, Frobenius norm errors, and operating characteristics.
1 2 |
Sigmas |
(p * p * K) arrays sample covariance matrices from each group |
Sigma0 |
Number of case subjects |
Omega0 |
(p * p * K) arrays true precision matrices |
ns |
vector of sample sizes for each group |
L |
graph Laplacian |
lambda1 |
value of first tuning parameter (lambda_1) |
lambda2 |
value of second tuning parameter (lambda_2) |
tol |
tolerance level for determining convergence of LASICH |
rho |
tuning parameter for ADMM algorithm |
truegraph |
a list containing true graphs (to return performance evaluation metrics) |
initest |
initial estimator for warm start |
LD |
a diagonal matrix added to L to improve computational efficiency |
wt |
weight for likelihood; NULL = nk/n, 1 = (1,...,1)??? |
thr |
whether to threshold the sample cov matrix to find connected components of the graph |
Sigma0 |
(p * p * K) arrays true covariance matrices |
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