Weight gain in pigs for different treatments

Weight gain in pigs for different treatments, with initial weight and feed eaten as covariates.

Usage

data("crampton.pig")

Format

A data frame with 50 observations on the following 5 variables.

treatment: feed treatment
rep: replicate
weight1: initial weight
feed: feed eaten
weight2: final weight

Details

A study of the effect of initial weight and feed eaten on the weight gaining ability of pigs with different feed treatments.

The data are extracted from Ostle. It is not clear that 'replicate' is actually a blocking replicate as opposed to a repeated measurement. The original source document needs to be consulted.

Source

Crampton, EW and Hopkins, JW. (1934). The Use of the Method of Partial Regression in the Analysis of Comparative Feeding Trial Data, Part II. The Journal of Nutrition, 8, 113-123. https://doi.org/10.1093/jn/8.3.329

References

Bernard Ostle. Statistics in Research, Page 458. https://archive.org/details/secondeditionsta001000mbp

Goulden (1939). Methods of Statistical Analysis, 1st ed. Page 256-259. https://archive.org/details/methodsofstatist031744mbp

Examples

if (FALSE) { # \dontrun{
  
  library(agridat)

  data(crampton.pig)
  dat <- crampton.pig

  dat <- transform(dat, gain=weight2-weight1)
  libs(lattice)
  # Trt 4 looks best
  xyplot(gain ~ feed, dat, group=treatment, type=c('p','r'),
         auto.key=list(columns=5),
         xlab="Feed eaten", ylab="Weight gain", main="crampton.pig")
  
  # Basic Anova without covariates
  m1 <- lm(weight2 ~ treatment + rep, data=dat)
  anova(m1)
  # Add covariates
  m2 <- lm(weight2 ~ treatment + rep + weight1 + feed, data=dat)
  anova(m2)
  # Remove treatment, test this nested model for significant treatments
  m3 <- lm(weight2 ~ rep + weight1 + feed, data=dat)
  anova(m2,m3) # p-value .07. F=2.34 matches Ostle
} # }