alwan.lamb: For the 34 sheep sires, the number of lambs in each of 5 foot...

In kwstat/agridat: Agricultural Datasets

For the 34 sheep sires, the number of lambs in each of 5 foot shape classes.

Description

For the 34 sheep sires, the number of lambs in each of 5 foot shape classes.

Usage

data("alwan.lamb")

Format

A data frame with 340 observations on the following 11 variables.

year

numeric 1980/1981

breed

breed PP, BRP, BR

sex

sex of lamb M/F

sire0

sire ID according to Alwan

shape

sire ID according to Gilmour

count

number of lambs

sire

shape of foot

yr

numeric contrast for year

b1

numeric contrast for breeds

b2

numeric contrast for breeds

b3

numeric contrast for breeds

Details

There were 2513 lambs classified on the presence of deformities in their feet. The lambs represent the offspring of 34 sires, 5 strains, 2 years.

The variables yr, b1, b2, b3 are numeric contrasts for the fixed effects as defined in the paper by Gilmour (1987) and used in the SAS example. Gilmour does not explain the reason for the particular contrasts. The counts for classes LF1, LF2, LF3 were combined.

Source

Mohammed Alwan (1983). Studies of the flock mating performance of Booroola merino crossbred ram lambs, and the foot conditions in Booroola merino crossbreds and Perendale sheep grazed on hill country. Thesis, Massey University. https://hdl.handle.net/10179/5900 Appendix I, II.

References

Gilmour, Anderson, and Rae (1987). Variance components on an underlying scale for ordered multiple threshold categorical data using a generalized linear mixed model. Journal of Animal Breeding and Genetics, 104, 149-155. https://doi.org/10.1111/j.1439-0388.1987.tb00117.x

SAS/STAT(R) 9.2 Users Guide, Second Edition Example 38.11 Maximum Likelihood in Proportional Odds Model with Random Effects https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm

Examples

## Not run: 

  library(agridat)
  data(alwan.lamb)
  dat <- alwan.lamb

  # merge LF1 LF2 LF3 class counts, and combine M/F
  dat$shape <- as.character(dat$shape)
  dat$shape <- ifelse(dat$shape=="LF2", "LF3", dat$shape)
  dat$shape <- ifelse(dat$shape=="LF1", "LF3", dat$shape)
  dat <- aggregate(count ~ year+breed+sire0+sire+shape+yr+b1+b2+b3,
                   dat, FUN=sum)

  dat <- transform(dat,
                   year=factor(year), breed=factor(breed),
                   sire0=factor(sire0), sire=factor(sire))
  # LF5 or LF3 first is a bit arbitary...affects the sign of the coefficients
  dat <- transform(dat, shape=ordered(shape, levels=c("LF5","LF4","LF3")))
  
  # View counts by year and breed
  libs(latticeExtra)
  dat2 <- aggregate(count ~ year+breed+shape, dat, FUN=sum)
  useOuterStrips(barchart(count ~ shape|year*breed, data=dat2,
                          main="alwan.lamb"))

  # Model used by Gilmour and SAS
  dat <- subset(dat, count > 0) 
  libs(ordinal)
  m1 <- clmm(shape ~ yr + b1 + b2 + b3 + (1|sire), data=dat,
             weights=count, link="probit", Hess=TRUE)
  summary(m1) # Very similar to Gilmour results
  ordinal::ranef(m1) # sign is opposite of SAS

  ## SAS var of sires .04849
  ## Effect 	Shape 	Estimate 	Standard Error 	DF 	t Value 	Pr > |t|
  ## Intercept 	1 	0.3781 	0.04907 	29 	7.71 	<.0001
  ## Intercept 	2 	1.6435 	0.05930 	29 	27.72 	<.0001
  ## yr 	  	0.1422 	0.04834 	2478 	2.94 	0.0033
  ## b1 	  	0.3781 	0.07154 	2478 	5.28 	<.0001
  ## b2 	  	0.3157 	0.09709 	2478 	3.25 	0.0012
  ## b3 	  	-0.09887 	0.06508 	2478 	-1.52 	0.1289
  
  ## Gilmour results for probit analysis
  ## Int1   .370 +/- .052
  ## Int2  1.603 +/- .061
  ## Year  -.139 +/- .052
  ## B1    -.370 +/- .076
  ## B2    -.304 +/- .103
  ## B3     .098 +/- .070

  # Plot random sire effects with intervals, similar to SAS example
  plot.random <- function(model, random.effect, ylim=NULL, xlab="", main="") {
    tab <- ordinal::ranef(model)[[random.effect]]
    tab <- data.frame(lab=rownames(tab), est=tab$"(Intercept)")
    tab <- transform(tab,
                     lo = est - 1.96 * sqrt(model$condVar),
                     hi = est + 1.96 * sqrt(model$condVar))
    # sort by est, and return index
    ix <- order(tab$est)
    tab <- tab[ix,]
    
    if(is.null(ylim)) ylim <- range(c(tab$lo, tab$hi))
    n <- nrow(tab)
    plot(1:n, tab$est, axes=FALSE, ylim=ylim, xlab=xlab,
         ylab="effect", main=main, type="n")
    text(1:n, tab$est, labels=substring(tab$lab,2) , cex=.75)
    axis(1)
    axis(2)
    segments(1:n, tab$lo, 1:n, tab$hi, col="gray30")
    abline(h=c(-.5, -.25, 0, .25, .5), col="gray")
    return(ix)  
  }
  ix <- plot.random(m1, "sire")

  # foot-shape proportions for each sire, sorted by estimated sire effects
  # positive sire effects tend to have lower proportion of lambs in LF4 and LF5
  tab <- prop.table(xtabs(count ~ sire+shape, dat), margin=1)
  tab <- tab[ix,]
  tab <- tab[nrow(tab):1,] # reverse the order
  lattice::barchart(tab,
                    horizontal=FALSE, auto.key=TRUE,
                    main="alwan.lamb", xlab="Sire", ylab="Proportion of lambs",
                    scales=list(x=list(rot=70)),
                    par.settings = simpleTheme(col=c("yellow","orange","red")) )
  
  detach("package:ordinal") # to avoid VarCorr clash with lme4
  

## End(Not run)

kwstat/agridat documentation built on Nov. 2, 2024, 6:19 a.m.