```
# NOT RUN {
data(Orthodont,package="nlme")
fm1 <- lmer(distance ~ age + (age|Subject), data=Orthodont)
## standardized residuals versus fitted values by gender
plot(fm1, resid(., scaled=TRUE) ~ fitted(.) | Sex, abline = 0)
## box-plots of residuals by Subject
plot(fm1, Subject ~ resid(., scaled=TRUE))
## observed versus fitted values by Subject
plot(fm1, distance ~ fitted(.) | Subject, abline = c(0,1))
## residuals by age, separated by Subject
plot(fm1, resid(., scaled=TRUE) ~ age | Sex, abline = 0)
require("lattice")
qqmath(fm1, id=0.05)
if (require("ggplot2")) {
## we can create the same plots using ggplot2 and the fortify() function
fm1F <- fortify.merMod(fm1)
ggplot(fm1F, aes(.fitted,.resid)) + geom_point(colour="blue") +
facet_grid(.~Sex) + geom_hline(yintercept=0)
## note: Subjects are ordered by mean distance
ggplot(fm1F, aes(Subject,.resid)) + geom_boxplot() + coord_flip()
ggplot(fm1F, aes(.fitted,distance))+ geom_point(colour="blue") +
facet_wrap(~Subject) +geom_abline(intercept=0,slope=1)
ggplot(fm1F, aes(age,.resid)) + geom_point(colour="blue") + facet_grid(.~Sex) +
geom_hline(yintercept=0)+geom_line(aes(group=Subject),alpha=0.4)+geom_smooth(method="loess")
## (warnings about loess are due to having only 4 unique x values)
detach("package:ggplot2")
}
# }
```

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