Compositional (version 5.4)

Hotelling's multivariate version of the 2 sample t-test for Euclidean data: Hotelling's multivariate version of the 2 sample t-test for Euclidean data

Description

Hotelling's test for testing the equality of two Euclidean population mean vectors.

Usage

hotel2T2(x1, x2, a = 0.05, R = 999, graph = FALSE)

Arguments

x1

A matrix containing the Euclidean data of the first group.

x2

A matrix containing the Euclidean data of the second group.

a

The significance level, set to 0.05 by default.

R

If R is 1 no bootstrap calibration is performed and the classical p-value via the F distribution is returned. If R is greater than 1, the bootstrap p-value is returned.

graph

A boolean variable which is taken into consideration only when bootstrap calibration is performed. IF TRUE the histogram of the bootstrap test statistic values is plotted.

Value

A list including:

mesoi

The two mean vectors.

info

The test statistic, the p-value, the critical value and the degrees of freedom of the F distribution (numerator and denominator). This is given if no bootstrap calibration is employed.

pvalue

The bootstrap p-value is bootstrap is employed.

note

A message informing the user that bootstrap calibration has been employed.

runtime

The runtime of the bootstrap calibration.

Details

Multivariate analysis of variance assuming equality of the covariance matrices. The p-value can be calculated either asymptotically or via bootstrap.

References

Everitt B. (2005). An R and S-Plus Companion to Multivariate Analysis p. 139-140. Springer.

See Also

james, maov, el.test2, eel.test2, comp.test

Examples

# NOT RUN {
hotel2T2( as.matrix(iris[1:25, 1:4]), as.matrix(iris[26:50, 1:4]) )
hotel2T2( as.matrix(iris[1:25, 1:4]), as.matrix(iris[26:50, 1:4]), R = 1 )
james( as.matrix(iris[1:25, 1:4]), as.matrix(iris[26:50, 1:4]) )
james( as.matrix(iris[1:25, 1:4]), as.matrix(iris[26:50, 1:4]), R = 1 )
# }