agridat (version 1.23)

theobald.covariate: Multi-environment trial of corn silage, Year * Loc * Variety with covariate

Description

Corn silage yields for maize in 5 years at 7 districts for 10 hybrids.

Format

A data frame with 256 observations on the following 5 variables.

year

year, 1990-1994

env

environment/district, 1-7

gen

genotype, 1-10

yield

dry-matter silage yield for corn

chu

corn heat units, thousand degrees Celsius

Used with permission of Chris Theobald.

Details

The trials were carried out in seven districts in the maritime provinces of Eastern Canada. Different fields were used in successive years. The covariate CHU (Corn Heat Units) is the accumulated average daily temperatures (thousands of degrees Celsius) during the growing season at each location.

Examples

if (FALSE) {

  library(agridat)
  data(theobald.covariate)
  dat <- theobald.covariate
  libs(lattice)
  xyplot(yield ~ chu|gen, dat, type=c('p','smooth'),
         xlab =  "chu = corn heat units",
         main="theobald.covariate - yield vs heat")

  # REML estimates (Means) in table 3 of Theobald 2002
  libs(lme4)
  dat <- transform(dat, year=factor(year))
  m0 <- lmer(yield ~ -1 + gen + (1|year/env) + (1|gen:year), data=dat)
  round(fixef(m0),2)


  # Use JAGS to fit Theobald (2002) model 3.2 with 'Expert' prior
  # Requires JAGS to be installed
  if(0) { 
  libs(reshape2)
  ymat <- acast(dat, year+env~gen, value.var='yield')
  chu <- acast(dat, year+env~., mean, value.var='chu', na.rm=TRUE)
  chu <- as.vector(chu - mean(chu))  # Center the covariate
  dat$yr <- as.numeric(dat$year)
  yridx <- as.vector(acast(dat, year+env~., mean, value.var='yr', na.rm=TRUE))
  dat$loc <- as.numeric(dat$env)
  locidx <- acast(dat, year+env~., mean, value.var='loc', na.rm=TRUE)
  locidx <- as.vector(locidx)

  jdat <- list(nVar = 10, nYear = 5, nLoc = 7, nYL = 29, yield = ymat,
              chu = chu, year = yridx, loc = locidx)

  libs(rjags)
  m1 <- jags.model(file=system.file(package="agridat", "files/theobald.covariate.jag"),
    data=jdat, n.chains=2)

  # Table 3, Variety deviations from means (Expert prior)
  c1 <- coda.samples(m1, variable.names=(c('alpha')),
                     n.iter=10000, thin=10)
  s1 <- summary(c1)
  effs <- s1$statistics[,'Mean']
  # Perfect match (different order?)
  rev(sort(round(effs - mean(effs), 2))) 
  }
}