R: Scheffe Test for Pairwise and Otherwise Comparisons

ScheffeTest {DescTools} | R Documentation

## Scheffe Test for Pairwise and Otherwise Comparisons

### Description

Scheffe's method applies to the set of estimates of all possible contrasts among the factor level means, not just the pairwise differences considered by Tukey's method.

### Usage

    ScheffeTest(x, ...)

    ## S3 method for class 'aov'
    ScheffeTest(x, which = NULL, contrasts = NULL,
                conf.level = 0.95, ...)
    ## Default S3 method:
    ScheffeTest(x, g = NULL, which = NULL,
                contrasts = NULL, conf.level = 0.95, ...)

### Arguments

`x` | either a fitted model object, usually an `aov` fit, when g is left to `NULL` or a response variable to be evalutated by g (which mustn't be `NULL` then).
`g` | the grouping variable.
`which` | character vector listing terms in the fitted model for which the intervals should be calculated. Defaults to all the terms.
`contrasts` | a r x c matrix containing the contrasts to be computed, while `r` is the number of factor levels and `c` the number of contrasts. Each column must contain a full contrast ("sum") adding up to 0. Note that the argument `which` must be defined, when non default contrasts are used. Default value of `contrasts` is `NULL`. In this case all pairwise contrasts will be reported.
`conf.level` | numeric value between zero and one giving the confidence level to use. If this is set to NA, just a matrix with the p-values will be returned.
`...` | further arguments, currently not used.

### Value

A list of classes `c("PostHocTest")`, with one component for each term requested in `which`. Each component is a matrix with columns `diff` giving the difference in the observed means, `lwr.ci` giving the lower end point of the interval, `upr.ci` giving the upper end point and `pval` giving the p-value after adjustment for the multiple comparisons.

There are print and plot methods for class `"PostHocTest"`. The plot method does not accept `xlab`, `ylab` or `main` arguments and creates its own values for each plot.

### Author(s)

Andri Signorell <andri@signorell.net>

### References

Robert O. Kuehl, Steel R. (2000) Design of experiments. Duxbury

Steel R.G.D., Torrie J.H., Dickey, D.A. (1997) Principles and Procedures of Statistics, A Biometrical Approach. McGraw-Hill

### See Also

`pairwise.t.test`, `TukeyHSD`

### Examples

    fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)

    ScheffeTest(x=fm1)
    ScheffeTest(x=fm1, which="tension")

    TukeyHSD(fm1)

    # some special contrasts
    y <- c(7,33,26,27,21,6,14,19,6,11,11,18,14,18,19,14,9,12,6,
           24,7,10,1,10,42,25,8,28,30,22,17,32,28,6,1,15,9,15,
           2,37,13,18,23,1,3,4,6,2)
    group <- factor(c(1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,3,
           3,3,3,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6))

    r.aov <- aov(y ~ group)

    ScheffeTest(r.aov, contrasts=matrix( c(1,-0.5,-0.5,0,0,0,
                                           0,0,0,1,-0.5,-0.5), ncol=2) )

    # just p-values:
    ScheffeTest(r.aov, conf.level=NA)

[Package DescTools version 0.99.24 Index]