Hotelling's multivariate version of the 2 sample t-test for Euclidean data
Hotelling's test for testing the equality of two Euclidean population mean vectors.
hotel2T2(x1, x2, a = 0.05, R = 999, graph = FALSE)
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. |
Multivariate analysis of variance assuming equality of the covariance matrices. The p-value can be calculated either asymptotically or via bootstrap.
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. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>
Everitt B. (2005). An R and S-Plus Companion to Multivariate Analysis p. 139-140. Springer.
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 )
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