# GeminiB: Estimate Row-Row Covariance Structure Using Gemini In jointMeanCov: Joint Mean and Covariance Estimation for Matrix-Variate Data

## Description

GeminiB estimates the row-row covariance, inverse covariance, correlation, and inverse correlation matrices using Gemini. For identifiability, the covariance factors A and B are scaled so that A has trace m, where m is the number of columns of X, A is the column-column covariance matrix, and B is the row-row covariance matrix.

## Usage

 `1` ```GeminiB(X, rowpen, penalize.diagonal = FALSE) ```

## Arguments

 `X` Data matrix, of dimensions n by m. `rowpen` Glasso penalty parameter. `penalize.diagonal` Logical value indicating whether to penalize the off-diagonal entries of the correlation matrix. Default is FALSE.

## Value

 `corr.B.hat` estimated correlation matrix. `corr.B.hat.inv` estimated inverse correlation matrix. `B.hat` estimated covariance matrix. `B.hat.inv` estimated inverse covariance matrix.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```n1 <- 5 n2 <- 5 n <- n1 + n2 m <- 20 X <- matrix(rnorm(n * m), nrow=n, ncol=m) rowpen <- sqrt(log(m) / n) out <- GeminiB(X, rowpen, penalize.diagonal=FALSE) # Display the estimated correlation matrix rounded to two # decimal places. print(round(out\$corr.B.hat, 2)) ```

jointMeanCov documentation built on May 6, 2019, 1:09 a.m.