```
# NOT RUN {
### multiple linear model, swiss data
lmod <- lm(Fertility ~ ., data = swiss)
### test of H_0: all regression coefficients are zero
### (ignore intercept)
### define coefficients of linear function directly
K <- diag(length(coef(lmod)))[-1,]
rownames(K) <- names(coef(lmod))[-1]
K
### set up general linear hypothesis
glht(lmod, linfct = K)
### alternatively, use a symbolic description
### instead of a matrix
glht(lmod, linfct = c("Agriculture = 0",
"Examination = 0",
"Education = 0",
"Catholic = 0",
"Infant.Mortality = 0"))
### multiple comparison procedures
### set up a one-way ANOVA
amod <- aov(breaks ~ tension, data = warpbreaks)
### set up all-pair comparisons for factor `tension'
### using a symbolic description (`type' argument
### to `contrMat()')
glht(amod, linfct = mcp(tension = "Tukey"))
### alternatively, describe differences symbolically
glht(amod, linfct = mcp(tension = c("M - L = 0",
"H - L = 0",
"H - M = 0")))
### alternatively, define contrast matrix directly
contr <- rbind("M - L" = c(-1, 1, 0),
"H - L" = c(-1, 0, 1),
"H - M" = c(0, -1, 1))
glht(amod, linfct = mcp(tension = contr))
### alternatively, define linear function for coef(amod)
### instead of contrasts for `tension'
### (take model contrasts and intercept into account)
glht(amod, linfct = cbind(0, contr %*% contr.treatment(3)))
### mix of one- and two-sided alternatives
warpbreaks.aov <- aov(breaks ~ wool + tension,
data = warpbreaks)
### contrasts for `tension'
K <- rbind("L - M" = c( 1, -1, 0),
"M - L" = c(-1, 1, 0),
"L - H" = c( 1, 0, -1),
"M - H" = c( 0, 1, -1))
warpbreaks.mc <- glht(warpbreaks.aov,
linfct = mcp(tension = K),
alternative = "less")
### correlation of first two tests is -1
cov2cor(vcov(warpbreaks.mc))
### use smallest of the two one-sided
### p-value as two-sided p-value -> 0.0232
summary(warpbreaks.mc)
### more complex models: Continuous outcome logistic
### regression; parameters are log-odds ratios
if (require("tram")) {
confint(glht(Colr(breaks ~ wool + tension,
data = warpbreaks),
linfct = mcp("tension" = "Tukey")))
}
# }
```

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