james: James multivariate version of the t-test

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/james.R

Description

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

Usage

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james(y1, y2, a = 0.05, R = 999, graph = FALSE)

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 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.

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.

pvalue

The bootstrap p-value if bootstrap is employed.

runtime

The runtime of the bootstrap calibration.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr> and Giorgos Athineou <athineou@csd.uoc.gr>

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

hotel2T2, maovjames, el, comp.test

Examples

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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 )
james( 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]) )

Compositional documentation built on July 27, 2017, 1:02 a.m.