```
# NOT RUN {
## Two way anova:
data(warpbreaks)
m0 <- lm(breaks ~ wool + tension, data=warpbreaks)
m1 <- lm(breaks ~ wool * tension, data=warpbreaks)
LSmeans(m0)
LSmeans(m1)
## same as:
K <- LE_matrix(m0);K
linest(m0, K)
K <- LE_matrix(m1);K
linest(m1, K)
LE_matrix(m0, effect="wool")
LSmeans(m0, effect="wool")
LE_matrix(m1, effect="wool")
LSmeans(m1, effect="wool")
LE_matrix(m0, effect=c("wool", "tension"))
LSmeans(m0, effect=c("wool", "tension"))
LE_matrix(m1, effect=c("wool", "tension"))
LSmeans(m1, effect=c("wool", "tension"))
## Regression; two parallel regression lines:
data(Puromycin)
m0 <- lm(rate ~ state + log(conc), data=Puromycin)
## Can not use LSmeans / LE_matrix here because of
## the log-transformation. Instead we must do:
Puromycin$lconc <- log( Puromycin$conc )
m1 <- lm(rate ~ state + lconc, data=Puromycin)
LE_matrix(m1)
LSmeans(m1)
LE_matrix(m1, effect="state")
LSmeans(m1, effect="state")
LE_matrix(m1, effect="state", at=list(lconc=3))
LSmeans(m1, effect="state", at=list(lconc=3))
## Non estimable contrasts
## ## Make balanced dataset
dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3),
CC=factor(1:3)))
dat.bal$y <- rnorm(nrow(dat.bal))
## ## Make unbalanced dataset
# 'BB' is nested within 'CC' so BB=1 is only found when CC=1
# and BB=2,3 are found in each CC=2,3,4
dat.nst <- dat.bal
dat.nst$CC <-factor(c(1, 1, 2, 2, 2, 2, 1, 1, 3, 3,
3, 3, 1, 1, 4, 4, 4, 4))
mod.bal <- lm(y ~ AA + BB * CC, data=dat.bal)
mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst)
LSmeans(mod.bal, effect=c("BB", "CC"))
LSmeans(mod.nst, effect=c("BB", "CC"))
LSmeans(mod.nst, at=list(BB=1, CC=1))
LSmeans(mod.nst, at=list(BB=1, CC=2))
## Above: NA's are correct; not an estimable function
if( require( lme4 )){
warp.mm <- lmer(breaks ~ -1 + tension + (1|wool), data=warpbreaks)
LSmeans(warp.mm, effect="tension")
class(warp.mm)
fixef(warp.mm)
coef(summary(warp.mm))
vcov(warp.mm)
if (require(pbkrtest))
vcovAdj(warp.mm)
}
LSmeans(warp.mm, effect="tension")
# }
```

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