Bodyweight of cows in a 2-by-2 factorial experiment

Bodyweight of cows in a 2-by-2 factorial experiment.

Format

A data frame with 598 observations on the following 5 variables.

animal: Animal factor, 26 levels
iron: Factor with levels Iron, NoIron
infect: Factor levels Infected, NonInfected
weight: Weight in (rounded to nearest 5) kilograms
day: Days after birth

Details

Diggle et al., 1994, pp. 100-101, consider an experiment that studied how iron dosing (none/standard) and micro-organism (infected or non-infected) influence the weight of cows.

Twenty-eight cows were allocated in a 2-by-2 factorial design with these factors. Some calves were inoculated with tuberculosis at six weeks of age. At six months, some calves were maintained on supplemental iron diet for a further 27 months.

The weight of each animal was measured at 23 times, unequally spaced. One cow died during the study and data for another cow was removed.

Source

Diggle, P. J., Liang, K.-Y., & Zeger, S. L. (1994). Analysis of Longitudinal Data. Page 100-101.

Retrieved Oct 2011 from https://www.maths.lancs.ac.uk/~diggle/lda/Datasets/

References

Lepper, AWD and Lewis, VM, 1989. Effects of altered dietary iron intake in Mycobacterium paratuberculosis-infected dairy cattle: sequential observations on growth, iron and copper metabolism and development of paratuberculosis. Research in veterinary science, 46, 289–296.

Arunas P. Verbyla and Brian R. Cullis and Michael G. Kenward and Sue J. Welham, (1999), The analysis of designed experiments and longitudinal data by using smoothing splines. Appl. Statist., 48, 269–311.

SAS/STAT(R) 9.2 User's Guide, Second Edition. https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect018.htm

Examples

if (FALSE) { # \dontrun{

  library(agridat)
  data(diggle.cow)
  dat <- diggle.cow
  
  # Figure 1 of Verbyla 1999
  libs(latticeExtra)
  useOuterStrips(xyplot(weight ~ day|iron*infect, dat, group=animal,
                        type='b', cex=.5, 
                        main="diggle.cow"))
  
  # Scaling
  dat <- transform(dat, time = (day-122)/10)


  if(require("asreml", quietly=TRUE)) {
    libs(asreml, latticeExtra)
    
    ## # Smooth for each animal.  No treatment effects. Similar to SAS Output 38.6.9
    
    m1 <- asreml(weight ~ 1 + lin(time) + animal + animal:lin(time), data=dat,
                 random = ~ animal:spl(time))
    p1 <- predict(m1, data=dat, classify="animal:time",
                  design.points=list(time=seq(0,65.9, length=50)))
    p1 <- p1$pvals
    p1 <- merge(dat, p1, all=TRUE) # to get iron/infect merged in
    foo1 <- xyplot(weight ~ day|iron*infect, dat, group=animal,
                   main="diggle.cow")
    foo2 <- xyplot(predicted.value ~ day|iron*infect, p1, type='l', group=animal)
    print(foo1+foo2)
  }
  
} # }