urquhart.feedlot {agridat} | R Documentation

## Weight gain calves in a feedlot

### Description

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

### Usage

    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. http://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. http://doi.org/10.2307/2530391

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

### Examples

    data(urquhart.feedlot)
    dat <- urquhart.feedlot

    if(require(reshape2) & require(latticeExtra)){
      d2 <- melt(dat, id.vars=c('animal','herd','diet'))

      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

    # random-effects model might be better, for example
    # require(lme4)
    # m1 <- lmer(weight2 ~ -1 + diet + weight1 + (1|herd), data=dat)
    # summary(m1) # weight1 = 1.2269

[Package agridat version 1.16 Index]