James multivariate version of the t-test
James test for testing the equality of two population mean vectors without assuming equality of the covariance matrices.
james(y1, y2, a = 0.05, R = 1)
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. |
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.
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. |
Michail Tsagris
R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr>.
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
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 )
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