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The data are from a study of exercise therapies, where 37 patients were assigned to one of two weightlifting programs.
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A data frame with 259 observations on the following 4 variables.
ida factor with 37 levels
treatmenta factor with levels repetitions and
weights representing whether repetitions or weights were
increased, respectively
daya numeric vector
strengtha numeric vector
In the first program (treatment 1), the number of repetitions was increased as subjects became stronger. In the second program (treatment 2), the number of repetitions was fixed but the amount of weight was increased as subjects became stronger. Measures of strength were taken at baseline (day 0), and on days 2, 4, 6, 8, 10, and 12.
Original variable names have been adapted to R conventions. Data were
reshaped from wide to long format and the response named
strength.
http://biosun1.harvard.edu/~fitzmaur/ala
Freund RJ, Littell RC, Spector PC (1986) SAS Systems for Linear Models, Cary, NC: SAS Institute Inc.
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 | str(exercise)
summary(exercise)
if (require(lattice)) {
xyplot(strength ~ day | treatment, data=exercise, groups=id,
type="l", cex=0.5, col=1,
scales=list(alternating=1, rot=c(0, 1), tck=c(0.5, 0)),
xlab="Time (days)", ylab="Strength",
panel=function(x, y, ...) {
panel.superpose(x, y, ...)
ym <- tapply(y, factor(x), mean, na.rm=TRUE)
panel.xyplot(unique(x), ym, lwd=3, ...)
})
}
if (require(lme4)) {
## Problem 8.1.3
fm1 <- lmer(strength ~ day * treatment + (day | id), data=exercise)
VarCorr(fm1)
fm1ML <- update(fm1, REML=FALSE)
## Problem 8.1.4
fm2 <- lmer(strength ~ day * treatment + (1 | id), data=exercise)
fm2ML <- update(fm2, REML=FALSE)
anova(fm2ML, fm1ML)
## Problem 8.1.5
fixef(fm1); fixef(fm2)
## Problem 8.1.8
coef(fm1)
}
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