James multivariate version of the t-test {Rfast}

James multivariate version of the t-test

Description

James test for testing the equality of two population mean vectors without assuming equality of the covariance matrices.

Usage

james(y1, y2, a = 0.05, R = 1)

Arguments

y1

A matrix containing the Euclidean data of the first group.

y2

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 the classical James test is returned. If R is 2 the MNV modficiation is implemented.

Details

Multivariate analysis of variance without assuming equality of the covariance matrices. The p-value can be calculated either asymptotically or via bootstrap. The James test (1954) or a modification proposed by Krishnamoorthy and Yanping (2006) is implemented. The James test uses a corected chi-square distribution, whereas the modified version uses an F distribution.

Value

A list including:

note

A message informing the user about the test used.

mesoi

The two mean vectors.

info

The test statistic, the p-value, the correction factor and the corrected critical value of the chi-square distribution if the James test has been used or, the test statistic, the p-value, the critical value and the degrees of freedom (numerator and denominator) of the F distribution if the modified James test has been used.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[e-mail]>.

References

G.S. James (1954). Tests of Linear Hypothese in Univariate and Multivariate Analysis when the Ratios of the Population Variances are Unknown. Biometrika, 41(1/2): 19-43

Krishnamoorthy K. and Yanping Xia. On Selecting Tests for Equality of Two Normal Mean Vectors (2006). Multivariate Behavioral Research 41(4): 533-548

See Also

mv.eeltest2

Examples

james( 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]), R = 2 )