# MVComp: Traditional Multivariate Mean Vector Comparison In mvdalab: Multivariate Data Analysis Laboratory

## Description

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

## Usage

 `1` ```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)

 ``` 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 27``` ```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) ```