agridat (version 1.23)

ilri.sheep: Birth weight and weaning weight of Dorper x Red Maasi lambs

Description

Birth weight and weaning weight of 882 lambs from a partial diallel cross of Dorper and Red Maasi breeds.

Format

A data frame with 882 observations on the following 12 variables.

year

year of lamb birth, 1991-1996

lamb

lamb id

sex

sex of lamb, M=Male/F=Female

gen

genotype, DD, DR, RD, RR

birthwt

weight of lamb at birth, kg

weanwt

weight of lamb at weaning, kg

weanage

age of lamb at weaning, days

ewe

ewe id

ewegen

ewe genotype: D, R

damage

ewe (dam) age in years

ram

ram id

ramgen

ram genotype: D, R

Details

Red Maasai sheep in East Africa are perceived to be resistant to certain parasites. ILRI decided in 1990 to investigate the degree of resistance exhibited by this Red Maasai breed and initiated a study in Kenya. A susceptible breed, the Dorper, was chosen to provide a direct comparison with the Red Maasai. The Dorper is well-adapted to this area and is also larger than the Red Maasai, and this makes these sheep attractive to farmers.

Throughout six years from 1991 to 1996 Dorper (D), Red Maasai (R) and Red Maasai x Dorper crossed ewes were mated to Red Maasai and Dorper rams to produce a number of different lamb genotypes. For the purposes of this example, only the following four offspring genotypes are considered (Sire x Dam): D x D, D x R, R x D and R x R.

Records are missing in 182 of the lambs, mostly because of earlier death.

References

Baker, RL and Nagda, S. and Rodriguez-Zas, SL and Southey, BR and Audho, JO and Aduda, EO and Thorpe, W. (2003). Resistance and resilience to gastro-intestinal nematode parasites and relationships with productivity of Red Maasai, Dorper and Red Maasai x Dorper crossbred lambs in the sub-humid tropics. Animal Science, 76, 119-136. https://doi.org/10.1017/S1357729800053388

Gota Morota, Hao Cheng, Dianne Cook, Emi Tanaka (2021). ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science. Journal of Animal Science, Volume 99, Issue 2, February 2021, skaa402. https://doi.org/10.1093/jas/skaa402

Examples

if (FALSE) {
  
  library(agridat)
  data(ilri.sheep)
  dat <- ilri.sheep
  dat <- transform(dat, lamb=factor(lamb), ewe=factor(ewe), ram=factor(ram),
                   year=factor(year))
  # dl is linear covariate, same as damage, but truncated to [2,8]
  dat <- within(dat, {
    dl <- damage
    dl <- ifelse(dl < 3, 2, dl)
    dl <- ifelse(dl > 7, 8, dl)
    dq <- dl^2
  })

  dat <- subset(dat, !is.na(weanage))

  # EDA
  libs(lattice)
  ## bwplot(weanwt ~ year, dat, main="ilri.sheep", xlab="year", ylab="Wean weight",
  ##        panel=panel.violin) # Year effect
  bwplot(weanwt ~ factor(dl), dat,
         main="ilri.sheep", xlab="Dam age", ylab="Wean weight") # Dam age effect
  # bwplot(weanwt ~ gen, dat,
  #        main="ilri.sheep", xlab="Genotype", ylab="Wean weight") # Genotype differences
  xyplot(weanwt ~ weanage, dat, type=c('p','smooth'),
         main="ilri.sheep", xlab="Wean age", ylab="Wean weight") # Age covariate

  # case study page 4.18
  lm1 <- lm(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen, data=dat)
  summary(lm1)
  anova(lm1)

  # ----------

  libs(lme4)
  lme1 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen +
                 (1|ewe) + (1|ram), data=dat)
  print(lme1, corr=FALSE)
  lme2 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen +
                 (1|ewe), data=dat)
  lme3 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen +
                 (1|ram), data=dat)
  anova(lme1, lme2,  lme3)

  # ----------

  if(require("asreml", quietly=TRUE)){
    libs(asreml,lucid)
    # case study page 4.20
    m1 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen,
                 data=dat)
    # wald(m1)
  
    # case study page 4.26
    m2 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen,
                 random = ~ ram + ewe, data=dat)
    # wald(m2)
    
    # case study page 4.37, year means
    # predict(m2, data=dat, classify="year")
    ##   year predicted.value standard.error est.status
    ## 1   91       12.638564      0.2363652  Estimable
    ## 2   92       11.067659      0.2285252  Estimable
    ## 3   93       11.561932      0.1809891  Estimable
    ## 4   94        9.636058      0.2505478  Estimable
    ## 5   95        9.350247      0.2346849  Estimable
    ## 6   96       10.188482      0.2755387  Estimable
  }
  
}