diggle.cow {agridat} | R Documentation

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

### Description

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 http://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. http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect018.htm

### Examples

    data(diggle.cow)
    dat <- diggle.cow

    # Figure 1 of Verbyla 1999
    require(lattice)
    if(require(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)

    # ----------------------------------------------------------------------------

    ## Not run:
      # asreml3
      require(asreml)

      # 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",
                    predictpoints=list(time=seq(0,65.9, length=50)))
      p1 <- p1$pred$pval
      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)

    ## End(Not run)

    # ----------------------------------------------------------------------------

    ## Not run:
      ## require(asreml4)
      ## require(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)
      ## foo2 <- xyplot(predicted.value ~ day|iron*infect, p1, type='l', group=animal)
      ## print(foo1+foo2)

    ## End(Not run)

[Package agridat version 1.16 Index]