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

# \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)


  libs(asreml) # asreml4
  ## libs(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))
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Mon Jan 11 17:08:14 2021
#> Spline: design points closer than 0.00659 have been merged.
#>           LogLik        Sigma2     DF     wall    cpu
#>  1     -1685.373       89.4521    545 17:08:14    0.0
#>  2     -1662.656       79.4217    545 17:08:14    0.0
#>  3     -1633.382       66.9151    545 17:08:14    0.0
#>  4     -1607.650       55.6989    545 17:08:14    0.0
#>  5     -1590.169       46.6521    545 17:08:14    0.0
#>  6     -1584.009       41.6081    545 17:08:14    0.0
#>  7     -1582.756       39.2910    545 17:08:14    0.0
#>  8     -1582.631       38.5276    545 17:08:14    0.0
#>  9     -1582.625       38.3532    545 17:08:14    0.0
#> 10     -1582.625       38.3205    545 17:08:14    0.0
  p1 <- predict(m1, data=dat, classify="animal:time",
                design.points=list(time=seq(0,65.9, length=50)))
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Mon Jan 11 17:08:14 2021
#> Spline: design points closer than 0.00659 have been merged.
#>           LogLik        Sigma2     DF     wall    cpu
#>  1     -1582.625       38.3147    545 17:08:14    0.1
#>  2     -1582.625       38.3143    545 17:08:14    0.0
#>  3     -1582.625       38.3136    545 17:08:14    0.0
  p1 <- p1$pvals
  p1 <- merge(dat, p1, all=TRUE) # to get iron/infect merged in
  foo1 <- xyplot(weight ~ day|iron*infect, dat, group=animal)
  foo2 <- xyplot(predicted.value ~ day|iron*infect, p1, type='l', group=animal)
  print(foo1+foo2)

# }