HT2test: Hotelling T Square Test
In MVET: Multivariate Estimates and Tests

View source: R/HT2test.R

HT2test

R Documentation

Hotelling T Square Test

Description

The mean vector test (Hotelling T square test) to compare one sample or two samples that satisfy the multivariate normality test and the homogeneity of covariance matrices test.

Usage

HT2test(data1,
        data2,
        mu0 = NULL,
        sample = "two",
        plot.scale = FALSE)

Arguments

`data1`	The data frame or matrix must consist of only numbers, and the data must consist of only a single group or class. It should not contain columns that separate groups or classes.
`data2`	The data frame or matrix must consist of only numbers, and the data must consist of only a single group or class. It should not contain columns that separate groups or classes. The `data2` is for comparison with `data1` and is not used to compare one sample to another.
`mu0`	The mu0 is used to test the mean vector hypothesis of `data1`. It is only used to compare one-sample.
`sample`	The options for specifying the number of groups for group comparisons are `one` and `two`, where `one` is used to compare one-sample and `two` is used to compare two-samples. (default sample = `two`)
`plot.scale`	If `TRUE`, the data will be scaled before calculating mean values and used in the plot. It has no direct effect on the data. It only applies to two samples. (default plot.scale = `FALSE`)

Value

`One.HT2`	The Hotelling T square test in one-sample, showing the degrees of freedom required for the F test, the Hotelling t square statistic, the F test statistic, and the probability of significance.
`Mean.val.plot`	Plot the mean value parallel coordinates, representing the two samples using the mean values for each variable.
`Two.HT2`	The Hotelling T square test in two-sample, showing the degrees of freedom required for the F test, the Hotelling t square statistic, the F test statistic, and the probability of significance.

References

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson Prentice Hall.

Examples

data(wine)
class1.wine <- subset(wine, class == 1)[, -1]
class2.wine <- subset(wine, class == 2)[, -1]
modified.class2.wine <- outlier(class2.wine, lim = 0, level = 0.05, option = "all")$modified.data

## one sample
value <- 0
p <- ncol(class1.wine)
mu0 <- matrix(rep(value, p), nrow = p, ncol = 1)
HT2test(data1 = class1.wine, mu0 = mu0, sample = "one")

## two sample
HT2test(data1 = class1.wine, data2 = modified.class2.wine, sample = "two", plot.scale = TRUE)

MVET documentation built on June 22, 2024, 11:25 a.m.