Feedlot data

This is an unbalanced analysis-of-covariance example, where one covariate is affected by a factor. Feeder calves from various herds enter a feedlot, where they are fed one of three diets. The weight of the animal at entry is the covariate, and the weight at slaughter is the response.

feedlot

Format

A data frame with 67 observations and 4 variables:

herd

a factor with levels 9 16 3 32 24 31 19 36 34 35 33, designating the herd that a feeder calf came from.

diet

a factor with levels Low Medium High: the energy level of the diet given the animal.

swt

a numeric vector: the weight of the animal at slaughter.

ewt

a numeric vector: the weight of the animal at entry to the feedlot.

Source

Urquhart NS (1982) Adjustment in covariates when one factor affects the covariate. Biometrics 38, 651-660.

Details

The data arise from a Western Regional Research Project conducted at New Mexico State University. Calves born in 1975 in commercial herds entered a feedlot as yearlings. Both diets and herds are of interest as factors. The covariate, ewt, is thought to be dependent on herd due to different genetic backgrounds, breeding history, etc. The levels of herd ordered to similarity of genetic background.

Note: There are some empty cells in the cross-classification of herd and diet.

Examples

feedlot.lm <- lm(swt ~ ewt + herd*diet, data = feedlot)

# Obtain EMMs with a separate reference value of ewt for each 
# herd. This reproduces the last part of Table 2 in the reference
emmeans(feedlot.lm,  ~ diet | herd,  cov.reduce = ewt ~ herd)
#> herd = 9:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low       839  32.7 36      773      906
#>  Medium    877  40.1 36      796      958
#>  High   nonEst    NA NA       NA       NA
#> 
#> herd = 16:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low       940  41.3 36      856     1024
#>  Medium    951  60.3 36      829     1073
#>  High   nonEst    NA NA       NA       NA
#> 
#> herd = 3:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low       981  32.8 36      915     1048
#>  Medium   1002  41.2 36      918     1085
#>  High     1015 105.0 36      803     1227
#> 
#> herd = 32:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low      1003  33.2 36      936     1070
#>  Medium    890  94.2 36      699     1081
#>  High      970  75.7 36      817     1124
#> 
#> herd = 24:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low       982  28.3 36      924     1039
#>  Medium    982  90.2 36      799     1165
#>  High   nonEst    NA NA       NA       NA
#> 
#> herd = 31:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low      1128  32.9 36     1062     1195
#>  Medium   1069  88.7 36      889     1249
#>  High     1111  56.6 36      996     1226
#> 
#> herd = 19:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low      1087  28.3 36     1030     1145
#>  Medium   1036  80.2 36      873     1199
#>  High      999  56.7 36      884     1114
#> 
#> herd = 36:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low      1155  65.3 36     1023     1288
#>  Medium   1062  65.8 36      928     1195
#>  High     1191  76.8 36     1035     1346
#> 
#> herd = 34:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low       987  53.1 36      879     1094
#>  Medium   1015  58.1 36      897     1132
#>  High     1048  57.4 36      931     1165
#> 
#> herd = 35:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low      1094  61.3 36      970     1218
#>  Medium   1092 111.0 36      867     1317
#>  High     1103  67.2 36      966     1239
#> 
#> herd = 33:
#>  diet   emmean    SE df lower.CL upper.CL
#>  Low      1207  80.4 36     1044     1370
#>  Medium   1031 140.0 36      748     1314
#>  High     1018  80.0 36      856     1180
#> 
#> Confidence level used: 0.95