plot.jointMeanCov {jointMeanCov} | R Documentation |
Quantile Plot of Test Statistics
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 |
... |
other plotting arguments passed to
|
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)
[Package jointMeanCov version 0.1.0 Index]