tamhaneT2Test {PMCMRplus} | R Documentation

## Tamhane's T2 Test

### Description

Performs Tamhane's T2 (or T2') all-pairs comparison test for normally distributed data with unequal variances.

### Usage

    tamhaneT2Test(x, ...)

    ## Default S3 method:
    tamhaneT2Test(x, g, welch = TRUE, ...)

    ## S3 method for class 'formula'
    tamhaneT2Test(formula, data, subset, na.action, welch = TRUE, ...)

    ## S3 method for class 'aov'
    tamhaneT2Test(x, welch = TRUE, ...)

### Arguments

`x` | a numeric vector of data values, a list of numeric data vectors or a fitted model object, usually an aov fit.
`...` | further arguments to be passed to or from methods.
`g` | a vector or factor object giving the group for the corresponding elements of `"x"`. Ignored with a warning if `"x"` is a list.
`welch` | indicates, whether Welch's approximate solution for calculating the degree of freedom shall be used or, as usually, df = N - 2. Defaults to `TRUE`.
`formula` | a formula of the form `response ~ group` where `response` gives the data values and `group` a vector or factor of the corresponding groups.
`data` | an optional matrix or data frame (or similar: see `model.frame`) containing the variables in the formula `formula`. By default the variables are taken from `environment(formula)`.
`subset` | an optional vector specifying a subset of observations to be used.
`na.action` | a function which indicates what should happen when the data contain `NA`s. Defaults to `getOption("na.action")`.

### Details

For all-pairs comparisons in an one-factorial layout with normally distributed residuals but unequal groups variances the T2 test (or T2' test) of Tamhane can be performed. A total of m = k(k-1)/2 hypotheses can be tested. The null hypothesis H_{ij}: μ_i(x) = μ_j(x) is tested in the two-tailed test against the alternative A_{ij}: μ_i(x) \ne μ_j(x), ~~ i \ne j.

T2 test uses Welch's approximate solution for calculating the degree of freedom. T2' test uses the usual df = N - 2 approximation. A warning message appears in the modified T2' test, if none of in Tamhane (1979) given conditions for nearly balanced sample sizes and nearly balanced standard errors is true.

The p-values are computed from the t-distribution and adjusted according to Dunn-Sidak.

### Value

A list with class `"PMCMR"` containing the following components:

method

  a character string indicating what type of test was performed.

data.name

  a character string giving the name(s) of the data.

statistic

  lower-triangle matrix of the estimated quantiles of the pairwise test statistics.

p.value

  lower-triangle matrix of the p-values for the pairwise tests.

alternative

  a character string describing the alternative hypothesis.

p.adjust.method

  a character string describing the method for p-value adjustment.

model

  a data frame of the input data.

dist

  a string that denotes the test distribution.

### Note

T2 test is basically an all-pairs pairwise-t-test. Similar results can be obtained with `pairwise.t.test(..., var.equal=FALSE, p.adjust.mehod = FALSE)`.

Thanks to Sirio Bolaños for his kind suggestion for adding T2' test into this function.

### References

Tamhane, A. C. (1979) A Comparison of Procedures for Multiple Comparisons of Means with Unequal Variances, Journal of the American Statistical Association 74, 471–480.

### See Also

`dunnettT3Test`

### Examples

    fit <- aov(weight ~ feed, chickwts)
    shapiro.test(residuals(fit))
    bartlett.test(weight ~ feed, chickwts) # var1 = varN
    anova(fit)

    ## also works with fitted objects of class aov
    res <- tamhaneT2Test(fit)
    summary(res)
    summaryGroup(res)
    res

    ## compare with pairwise.t.test
    WT <- pairwise.t.test(chickwts$weight,
                          chickwts$feed,
                          pool.sd = FALSE,
                          p.adjust.method = "none")
    p.adj.sidak <- function(p, m) sapply(p, function(p) min(1, 1 - (1 - p)^m))
    p.raw <- as.vector(WT$p.value)
    m <- length(p.raw[!is.na(p.raw)])
    PADJ <- matrix(ans <- p.adj.sidak(p.raw, m),
                   nrow = 5, ncol = 5)
    colnames(PADJ) <- colnames(WT$p.value)
    rownames(PADJ) <- rownames(WT$p.value)
    PADJ

    ## same without Welch's approximate solution
    summary(T2b <- tamhaneT2Test(fit, welch = FALSE))

[Package PMCMRplus version 1.6.1 Index]