plot.jointMeanCov: Quantile Plot of Test Statistics

In jointMeanCov: Joint Mean and Covariance Estimation for Matrix-Variate Data

Description

This function displays a quantile plot of test statistics, based on the output of the functions jointMeanCovGroupCen or jointMeanCovModSelCen.

Usage

## S3 method for class 'jointMeanCov'
plot(x, ...)

Arguments

x	output of jointMeanCovGroupCen or jointMeanCovModSelCen.
...	other plotting arguments passed to qqnorm.

Examples

# 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 plot the
# test statistics.
rowpen <- sqrt(log(m) / n)
out <- jointMeanCovModSelCen(X, group.one.indices, rowpen)
plot(out)

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