Slate Hall Farm 1976 spring wheat — kempton.slatehall • agridat

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

Source

R A Kempton and P N Fox. (1997). Statistical Methods for Plant Variety Evaluation, Chapman and Hall. Page 84.

Julian Besag and David Higdon. 1993. Bayesian Inference for Agricultural Field Experiments. Bull. Int. Statist. Table 4.1.

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) { # \dontrun{

  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
  }
  
} # }