agridat (version 1.23)

kempton.slatehall: Slate Hall Farm 1976 spring wheat

Description

Yields for a Slate Hall Farm 1976 spring wheat trial.

Format

A data frame with 150 observations on the following 5 variables.

rep

rep, 6 levels

row

row

col

column

gen

genotype, 25 levels

yield

yield (grams/plot)

Details

The trial was a balanced lattice with 25 varieties in 6 replicates, 10 ranges of 15 columns. The plot size was 1.5 meters by 4 meters. Each row within a rep is an (incomplete) block.

Field width: 15 columns * 1.5m = 22.5m

Field length: 10 ranges * 4m = 40m

References

Gilmour, Arthur R and Robin Thompson and Brian R Cullis. (1994). Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models, Biometrics, 51, 1440-1450.

Examples

if (FALSE) {

  library(agridat)
  data(kempton.slatehall)
  dat <- kempton.slatehall

  # Besag 1993 figure 4.1 (left panel)
  libs(desplot)
  grays <- colorRampPalette(c("#d9d9d9","#252525"))
  desplot(dat, yield ~ col * row,
          aspect=40/22.5, # true aspect
          num=gen, out1=rep, col.regions=grays, # unknown aspect
          main="kempton.slatehall - spring wheat yields")

  # ----------

  # Incomplete block model of Gilmour et al 1995
  libs(lme4, lucid)
  dat <- transform(dat, xf=factor(col), yf=factor(row))
  m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:yf) + (1|rep:xf), data=dat)
  vc(m1)
  ##    groups        name variance stddev
  ##  rep:xf   (Intercept)    14810 121.7
  ##  rep:yf   (Intercept)    15600 124.9
  ##  rep      (Intercept)     4262  65.29
  ##  Residual                 8062  89.79
  
  
  # ----------

  if(require("asreml", quietly=TRUE)){
    libs(asreml,lucid)
  
    # Incomplete block model of Gilmour et al 1995
    dat <- transform(dat, xf=factor(col), yf=factor(row))
    m2 <- asreml(yield ~ gen, random = ~ rep/(xf+yf), data=dat)
    
    lucid::vc(m2)
    ##          effect component std.error z.ratio constr
    ##     rep!rep.var      4262      6890    0.62    pos
    ##  rep:xf!rep.var     14810      4865    3       pos
    ##  rep:yf!rep.var     15600      5091    3.1     pos
    ##      R!variance      8062      1340    6       pos
    
    # Table 4
    # asreml4
    # predict(m2, data=dat, classify="gen")$pvals
  }
  
}