| ScheffeTest | R Documentation |
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.
ScheffeTest(x, ...)
## S3 method for class 'formula'
ScheffeTest(formula, data, subset, na.action, ...)
## 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, ...)
x |
either a fitted model object, usually an |
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 |
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. |
formula |
a formula of the form |
data |
an optional matrix or data frame (or similar: see
|
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 |
... |
further arguments, currently not used. |
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.
Andri Signorell <andri@signorell.net>
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
pairwise.t.test, TukeyHSD
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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