# summary.jointMeanCov: Summary of Test Statistics In jointMeanCov: Joint Mean and Covariance Estimation for Matrix-Variate Data

## Description

`summary` method for class `jointMeanCov`. This function displays the minimum, maximum, mean, median, 25th percentile, and 75th percentile of the test statistics.

## Usage

 ```1 2``` ```## S3 method for class 'jointMeanCov' summary(object, ...) ```

## Arguments

 `object` output of `jointMeanCovGroupCen` or `jointMeanCovModSelCen`. `...` other arguments passed to `summary`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```# Define sample sizes n1 <- 5 n2 <- 5 n <- n1 + n2 m <- 200 # Generate data with row and column covariance # matrices each autorogressive of order 1 with # parameter 0.2. The mean is defined so the first # three columns have true differences in group means # equal to four. Z <- matrix(rnorm(m * n), nrow=n, ncol=m) A <- outer(1:m, 1:m, function(i, j) 0.2^abs(i - j)) B <- outer(1:n, 1:n, function(i, j) 0.2^abs(i - j)) M <- matrix(0, nrow=nrow(Z), ncol=ncol(Z)) group.one.indices <- 1:5 group.two.indices <- 6:10 M[group.one.indices, 1:3] <- 2 M[group.two.indices, 1:3] <- -2 X <- t(chol(B)) %*% Z %*% chol(A) + M # Apply Algorithm 2 (jointMeanCovModSelCen) and pass the # output to the summary function. rowpen <- sqrt(log(m) / n) out <- jointMeanCovModSelCen(X, group.one.indices, rowpen) summary(out) ```

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