# GeminiBPath: Estimate Row-Row Covariance Using Gemini for a Sequence of... In jointMeanCov: Joint Mean and Covariance Estimation for Matrix-Variate Data

## Description

GeminiBPath estimates the row-row covariance, inverse covariance, correlation, and inverse correlation matrices using Gemini with a sequence of penalty parameters. 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` ```GeminiBPath(X, rowpen.list, penalize.diagonal = FALSE) ```

## Arguments

 `X` Data matrix, of dimensions n by m. `rowpen.list` Vector of penalty parameters, should be increasing (analogous to the `glassopath` function of the `glasso` package). `penalize.diagonal` Logical indicating whether to penalize the off-diagonal entries of the correlation matrix. Default is FALSE.

## Value

 `corr.B.hat` array of estimated correlation matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)). `corr.B.hat.inv` array of estimated inverse correlation matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)). `B.hat` array of estimated covariance matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)). `B.hat.inv` array of estimated inverse covariance matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)).

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# Generate a data matrix. n1 <- 5 n2 <- 5 n <- n1 + n2 m <- 20 X <- matrix(rnorm(n * m), nrow=n, ncol=m) # Apply GeminiBPath for a sequence of penalty parameters. rowpen.list <- sqrt(log(m) / n) * c(1, 0.5, 0.1) out <- GeminiBPath(X, rowpen.list, penalize.diagonal=FALSE) # Display the estimated correlation matrix corresponding # to penalty 0.1, rounded to two decimal places. print(round(out\$corr.B.hat[, , 3], 2)) ```

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