Hotelling's test for testing one Euclidean population mean vector.
hotel1T2(x, M, a = 0.05, R = 999, graph = FALSE)A matrix containing Euclidean data.
The significance level, set to 0.05 by default.
The hypothesized mean vector.
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 sample mean vector.
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.
The runtime of the bootstrap calibration.
Multivariate hypothesis test for a one sample mean vector. This is the multivariate analogue of the one sample t-test. The p-value can be calculated either asymptotically or via bootstrap.
K.V. Mardia, J.T. Kent and J.M. Bibby (1979). Multivariate analysis.
eel.test1, el.test1, james, hotel2T2, maov, el.test2, comp.test
# NOT RUN {
x <- Rfast::rmvnorm(100, numeric(10), diag( rexp(10,0.5) ) )
hotel1T2(x, numeric(10), R = 1)
hotel1T2(x, numeric(10), R = 999, graph = TRUE)
# }
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