MVComp {mvdalab}

Traditional Multivariate Mean Vector Comparison

Description

Performs a traditional multivariate comparison of mean vectors drawn from two populations.

Usage

MVComp(data1, data2, level = .95)

Arguments

data1

a multivariable dataset to compare to.

data2

a multivariable dataset to compare.

level

draw elliptical contours at these (normal) probability or confidence levels.

Details

This function provides a T2-statistic for testing the equality of two mean vectors. This test is appropriate for testing two populations, assuming independence.

Assumptions:

The sample for both populations is a random sample from a multivariate population.

-Both populations are independent

-Both populations are multivariate normal

-Covariance matrices are approximately equal

Value

This function returns the simultaneous confidence intervals for the p-variates and its corresponding confidence ellipse at the stated confidence level.

Author(s)

Nelson Lee Afanador ([e-mail])

References

Johnson, R.A., Wichern, D.W. (2002) Applied Multivariate Statistical Analysis. Prentice Hall.

Examples

data(College)
dat1 <- College
#Generate a 'fake' difference of 15 units
dat2 <- College + matrix(rnorm(nrow(dat1) * ncol(dat1), mean = 15), 
        nrow = nrow(dat1), ncol = ncol(dat1))

Comparison <- MVComp(dat1, dat2, level = .95)
Comparison
plot(Comparison, Diff2Plot = c(1, 2), include.zero = FALSE)
plot(Comparison, Diff2Plot = c(1, 2), include.zero = TRUE)

plot(Comparison, Diff2Plot = c(2, 3), include.zero = FALSE)
plot(Comparison, Diff2Plot = c(2, 3), include.zero = TRUE)


data(iris)
dat1b <- iris[, -5]
#Generate a 'fake' difference of .5 units
dat2b <- dat1b + matrix(rnorm(nrow(dat1b) * ncol(dat1b), mean = .5), 
          nrow = nrow(dat1b), ncol = ncol(dat1b))

Comparison2 <- MVComp(dat1b, dat2b, level = .90)
plot(Comparison2, Diff2Plot = c(1, 2), include.zero = FALSE)
plot(Comparison2, Diff2Plot = c(1, 2), include.zero = TRUE)

plot(Comparison2, Diff2Plot = c(3, 4), include.zero = FALSE)
plot(Comparison2, Diff2Plot = c(3, 4), include.zero = TRUE)