Scheffe Test for Pairwise and Otherwise Comparisons

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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

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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

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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)

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