covTranspose11: Covariance Estimation.
In Tsphere: Transposable Sphering for Large-Scale Inference with Correlated Data.

Description Usage Arguments Details Value Author(s) References See Also

Inverse row and column covariance estimation for the L1 penalized matrix-variate normal model.

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covTranspose11(xc, rhor, rhoc, row = TRUE, sigi.init = NULL, 
delti.init = NULL, thr = 1e-04, maxit = 1000, trace = TRUE,  thr.glasso
= 1e-04,  maxit.glasso = 1000, pen.diag = TRUE)

`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.

TransSphere

Tsphere documentation built on May 2, 2019, 3:32 p.m.