1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
|
\name{waste}
\alias{waste}
\docType{data}
\title{ Industrial Waste Data Set }
\usage{data("waste")}
\description{
Industrial waste output in a manufactoring plant.
}
\format{
This data frame contains the following variables
\describe{
\item{temp}{temperature, a factor at three levels: \code{low}, \code{medium}, \code{high}.}
\item{envir}{environment, a factor at five levels: \code{env1} \dots \code{env5}.}
\item{waste}{response variable: waste output in a manufacturing plant.}
}
}
\details{
The data are from an experiment designed to study the effect of temperature
(\code{temp}) and environment (\code{envir}) on waste output in a manufactoring plant.
Two replicate measurements were taken at each temperature / environment combination.
}
\source{
P. H. Westfall, R. D. Tobias, D. Rom, R. D. Wolfinger, Y. Hochberg (1999).
\emph{Multiple Comparisons and Multiple Tests Using the SAS System}.
Cary, NC: SAS Institute Inc., page 177.
}
\examples{
### set up two-way ANOVA with interactions
amod <- aov(waste ~ temp * envir, data=waste)
### comparisons of main effects only
K <- glht(amod, linfct = mcp(temp = "Tukey"))$linfct
K
glht(amod, K)
### comparisons of means (by averaging interaction effects)
low <- grep("low:envi", colnames(K))
med <- grep("medium:envi", colnames(K))
K[1, low] <- 1 / (length(low) + 1)
K[2, med] <- 1 / (length(low) + 1)
K[3, med] <- 1 / (length(low) + 1)
K[3, low] <- - 1 / (length(low) + 1)
K
confint(glht(amod, K))
### same as TukeyHSD
TukeyHSD(amod, "temp")
### set up linear hypotheses for all-pairs of both factors
wht <- glht(amod, linfct = mcp(temp = "Tukey", envir = "Tukey"))
### cf. Westfall et al. (1999, page 181)
summary(wht, test = adjusted("Shaffer"))
}
\keyword{datasets}
|
