kempton.slatehall {agridat}R Documentation

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

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

## Not run: 

  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
  
  
  # ----------

  # 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)
  
  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
  # asreml3
  # predict(m2, data=dat, classify="gen")$predictions$pvals
  # asreml4
  # predict(m2, data=dat, classify="gen")$pvals


## End(Not run)

[Package agridat version 1.18 Index]