Slate Hall Farm 1976 spring wheat

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

# \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)
#>       grp        var1 var2  vcov  sdcor
#>    rep:xf (Intercept) <NA> 14810 121.7 
#>    rep:yf (Intercept) <NA> 15600 124.9 
#>       rep (Intercept) <NA>  4263  65.29
#>  Residual        <NA> <NA>  8062  89.79
  ##    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
  
  
  # ----------

  # asreml3 & asreml4
  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)
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Fri Dec 11 17:48:28 2020
#>           LogLik        Sigma2     DF     wall    cpu
#>  1      -734.184      26778.41    125 17:48:28    0.0
#>  2      -720.066      16594.96    125 17:48:28    0.0
#>  3      -711.122      11175.53    125 17:48:28    0.0
#>  4      -708.253       8997.74    125 17:48:28    0.0
#>  5      -707.791       8149.58    125 17:48:28    0.0
#>  6      -707.786       8062.41    125 17:48:28    0.0
  
  vc(m2)
#>   effect component std.error z.ratio bound %ch
#>      rep      4263      6877    0.62     P 0.4
#>   rep:yf     15600      5090    3.1      P 0  
#>   rep:xf     14810      4866    3        P 0  
#>  units!R      8062      1341    6        P 0  
  ##          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
  # asreml3
  # predict(m2, data=dat, classify="gen")$predictions$pvals
  # asreml4
  # predict(m2, data=dat, classify="gen")$pvals

# }