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Performs a traditional multivariate comparison of mean vectors drawn from two populations.
MVComp(data1, data2, level = .95)This function returns the simultaneous confidence intervals for the p-variates and its corresponding confidence ellipse at the stated confidence level.
a multivariable dataset to compare to.
a multivariable dataset to compare.
draw elliptical contours at these (normal) probability or confidence levels.
Nelson Lee Afanador (nelson.afanador@mvdalab.com)
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
Johnson, R.A., Wichern, D.W. (2002) Applied Multivariate Statistical Analysis. Prentice Hall.
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)
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