Description Usage Arguments Details Value Author(s) References See Also
Inverse row and column covariance estimation for the L1 penalized matrix-variate normal model.
1 2 3 |
xc |
Centered data matrix. |
rhor |
Row regularization parameter. |
rhoc |
Column regularization parameter. |
row |
Logical. TRUE = Start with row covariance. |
sigi.init |
Initialization for the row precision matrix. (Optional). |
delti.init |
Initialization for the column precision matrix. (Optional). |
thr |
Convergence threshold. |
maxit |
Maximum number of iterations. |
trace |
Prints matrix-variate log-likelihood for each iteration. |
thr.glasso |
Convergence threshold for the graphical lasso. |
maxit.glasso |
Maximum number of iterations for the graphical lasso. |
pen.diag |
Logical. Indicates whether the diagonal should be penalized. |
Estimates row and column precision matrix via L1 penalized Transposable Regularized Covariance Models.
Sigmahat |
Estimated row covariance. |
Deltahat |
Estimated column covariance. |
Sigmaihat |
Estimated sparse row precision matrix. |
Deltaihat |
Estimated sparse column precision matrix. |
loglike |
Trace of the penalized log-likelihood at each iteration. |
Genevera I. Allen
G. I. Allen and R. Tibshirani, "Transposable regularized covariance models with an application to missing data imputation", Annals of Applied Statistics, 4:2, 764-790, 2010.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.