Weight gain calves in a feedlot — urquhart.feedlot • agridat

Weight gain calves in a feedlot, given three different diets.

data("urquhart.feedlot")

Format

A data frame with 67 observations on the following 5 variables.

animal: animal ID
herd: herd ID
diet: diet: Low, Medium, High
weight1: initial weight
weight2: slaughter weight

Details

Calves born in 1975 in 11 different herds entered a feedlot as yearlings. Each animal was fed one of three diets with low, medium, or high energy. The original sources explored the use of some contrasts for comparing breeds.

Herd	Breed
9	New Mexico Herefords
16	New Mexico Herefords
3	Utah State University Herefords
32	Angus
24	Angus x Hereford (cross)
31	Charolais x Hereford
19	Charolais x Hereford
36	Charolais x Hereford
34	Brangus
35	Brangus
33	Southern Select

Source

N. Scott Urquhart (1982). Adjustment in Covariance when One Factor Affects the Covariate Biometrics, 38, 651-660. Table 4, p. 659. https://doi.org/10.2307/2530046

References

N. Scott Urquhart and David L. Weeks (1978). Linear Models in Messy Data: Some Problems and Alternatives Biometrics, 34, 696-705. https://doi.org/10.2307/2530391

Also available in the 'emmeans' package as the 'feedlot' data.

Examples

# \dontrun{
  
  library(agridat)
  data(urquhart.feedlot)
  dat <- urquhart.feedlot

  libs(reshape2)
  d2 <- melt(dat, id.vars=c('animal','herd','diet'))

  libs(latticeExtra)
  useOuterStrips(xyplot(value ~ variable|diet*herd, data=d2, group=animal,
                        type='l',
                        xlab="Initial & slaughter timepoint for each diet",
                        ylab="Weight for each herd",
                        main="urquhart.feedlot - weight gain by animal"))


  # simple fixed-effects model 
  dat <- transform(dat, animal = factor(animal), herd=factor(herd))
  m1 <- lm(weight2 ~ weight1 + herd*diet, data = dat)
  coef(m1) # weight1 = 1.1373 match Urquhart table 5 common slope
#>       (Intercept)           weight1             herd9            herd16 
#>        280.222922          1.137315        -71.611764        -51.679186 
#>            herd19            herd24            herd31            herd32 
#>        -81.089896        -80.768678        -36.269082        -65.477829 
#>            herd33            herd34            herd35            herd36 
#>       -125.209353         -3.970439        -29.895929        -15.328810 
#>           dietLow        dietMedium     herd9:dietLow    herd16:dietLow 
#>        -33.622123        -12.783862        -16.960282          9.502665 
#>    herd19:dietLow    herd24:dietLow    herd31:dietLow    herd32:dietLow 
#>        121.696783         20.625565         50.806235         66.358372 
#>    herd33:dietLow    herd34:dietLow    herd35:dietLow    herd36:dietLow 
#>        222.442937        -27.760884         25.084868         -1.751030 
#>  herd9:dietMedium herd16:dietMedium herd19:dietMedium herd24:dietMedium 
#>                NA                NA         49.530167                NA 
#> herd31:dietMedium herd32:dietMedium herd33:dietMedium herd34:dietMedium 
#>        -29.462442        -67.216138         25.450529        -20.649019 
#> herd35:dietMedium herd36:dietMedium 
#>          2.447965       -116.014781 
  
  # random-effects model might be better, for example
  # libs(lme4)
  # m1 <- lmer(weight2 ~ -1 + diet + weight1 + (1|herd), data=dat)
  # summary(m1) # weight1 = 1.2269
  
# }