fev1: Subsample (N=300) of data on FEV1 and height for girls living...
In ALA: Data sets and examples for the book "Applied Longitudinal Analysis"

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The Six Cities Study of Air Pollution and Health was a longitudinal study designed to characterize lung growth as measured by changes in pulmonary function in children and adolescents, and the factors that influence lung function growth.

1	data(fev1)

A data frame with 1994 observations on the following 6 variables.

id: a factor with 300 levels
age: a numeric vector
height: a numeric vector
age0: a numeric vector; initial age
height0: a numeric vector; initial height
logFEV1: a numeric vector

A cohort of 13,379 children born on or after 1967 was enrolled in six communities across the U.S.: Watertown (Massachusetts), Kingston and Harriman (Tennessee), a section of St. Louis (Missouri), Steubenville (Ohio), Portage (Wisconsin), and Topeka (Kansas). Most children were enrolled in the first or second grade (between the ages of six and seven) and measurements of study participants were obtained annually until graduation from high school or loss to follow-up. At each annual examination, spirometry, the measurement of pulmonary function, was performed and a respiratory health questionnaire was completed by a parent or guardian.

The dataset contains a subset of the pulmonary function data collected in the Six Cities Study. The data consist of all measurements of FEV1, height and age obtained from a randomly selected subset of the female participants living in Topeka, Kansas. The random sample consists of 300 girls, with a minimum of one and a maximum of twelve observations over time.

Original variable names have been adapted to R conventions. age0 and height0 are just the first element of the age and height variables for each subject.

http://biosun1.harvard.edu/~fitzmaur/ala

Dockery DW, Berkey CS, Ware JH, Speizer FE, Ferris BG (1983) Distribution of FVC and FEV1 in children 6 to 11 years old. American Review of Respiratory Disease 128:405-412

str(fev1)
summary(fev1)

if (require(lattice)) {
    fev1.e <- exp(fev1$logFEV1)
    set.seed(1234); ids <- sample(levels(fev1$id), 50)
    bwplot(~ logFEV1, data=fev1)        # reveals the outlier mentioned in text
    subset(fev1, logFEV1 < -0.5)
    ## Fig. 8.4 (roughly)
    xyplot(log(fev1.e/height) ~ age, data=fev1, groups=id, type="b",
           subset=id %in% ids, cex=0.5, col=1,
           xlab="Age (years)", ylab="Log(FEV1/Height)",
           ylim=c(-0.3, 1.2))
}

if (require(lme4)) {
    ## Model in p. 213
    (fm1 <- lmer(logFEV1 ~ age + log(height) + age0 + log(height0) + (age | id),
                 data=fev1, subset=logFEV1 > -0.5))
    ## Table 8.3
    VarCorr(fm1)$id * 100

    ## Model in p. 216
    (fm2 <- update(fm1, . ~ . - (age | id) + (log(height) | id)))
}