Last chance! 50% off unlimited learning
Sale ends in
Hotelling's test for testing the equality of two Euclidean population mean vectors.
hotel2T2(x1, x2, a = 0.05, R = 999, graph = FALSE)
A matrix containing the Euclidean data of the first group.
A matrix containing the Euclidean data of the second group.
The significance level, set to 0.05 by default.
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.
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.
A list including:
The two mean vectors.
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.
The bootstrap p-value is bootstrap is employed.
A message informing the user that bootstrap calibration has been employed.
The runtime of the bootstrap calibration.
Multivariate analysis of variance assuming equality of the covariance matrices. The p-value can be calculated either asymptotically or via bootstrap.
Everitt B. (2005). An R and S-Plus Companion to Multivariate Analysis p. 139-140. Springer.
# 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 )
# }
Run the code above in your browser using DataLab