agridat (version 1.12)

yates.oats: Yield of oats in a split-block experiment

Description

The yield of oats from a split-plot field trial using three varieties and four levels of manurial treatment. The experiment was laid out in 6 blocks of 3 main plots, each split into 4 sub-plots. The varieties were applied to the main plots and the manurial (nitrogen) treatments to the sub-plots.

Format

block

block factor with 6 levels

nitro

nitrogen treatment in hundredweight per acre

gen

genotype factor, 3 levels

yield

yield in 1/4 lbs per sub-plot, each 1/80 acre.

x

x ordinate

y

y ordinate

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

# NOT RUN {
data(yates.oats)
dat <- yates.oats

desplot(yield ~ x*y, dat, out1=block, text=gen, col=nitro, cex=1,
        main="yates.oats")

# Roughly linear gradient across the field.  The right-half of each
# block has lower yield.  The blocking is inadequate!
require(lattice)
xyplot(yield ~ x|factor(nitro), dat, type = c('p', 'smooth'),
       xlab='x', span=1, as.table = TRUE, main="yates.oats")

# Typical split-plot analysis
if(require(lme4)){
m3 <- lmer(yield ~ nitro * gen + (1|block/gen), data=dat)
# Residuals still show structure
xyplot(resid(m3) ~ dat$x, xlab='x', type=c('p','smooth'),
       main="yates.oats")

# Add a linear trend for x
m4 <- lmer(yield ~ x + nitro * gen + (1|block/gen), data=dat)
# xyplot(resid(m4) ~ dat$x, type=c('p','smooth'), xlab='x')

## Compare fits
## AIC(m3,m4)
##    df      AIC
## m3  9 581.2372
## m4 10 557.9424
}

# }
# NOT RUN {
  # Demonstrate the use of the lsmeans package and asreml::predict

  # --- nlme ---
  require(nlme)
  require(lsmeans)
  dat2 <- yates.oats[-c(1,2,3,5,8,13,21,34,55),]
  m5l <- lme(yield ~ factor(nitro) + gen, random = ~1 | block/gen,
             data = dat2)

  # --- asreml ---
  require(asreml)
  m5a <- asreml(yield ~ factor(nitro) + gen,
                random = ~ block + block:gen, data=dat2)

  require(lucid)
  vc(m5l) # warning.  FIXME
  vc(m5a)

  lsmeans(m5l, "gen")
  predict(m5a, classify="gen")$predictions$pvals

# }
# NOT RUN {
# }