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

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

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

    # ----------------------------------------------------------------------------

    ## Not run:
      # Incomplete block model of Gilmour et al 1995
      require(lme4)
      require(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

    ## End(Not run)

    # ----------------------------------------------------------------------------

    ## Not run:
      # Incomplete block model of Gilmour et al 1995
      # asreml3
      require(asreml)
      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
      predict(m2, data=dat, classify="gen")$predictions$pvals

    ## End(Not run)

    # ----------------------------------------------------------------------------

    ## Not run:
      # Incomplete block model of Gilmour et al 1995
      ## require(asreml4)
      ## require(lucid)
      ## m2 <- asreml(yield ~ gen, random = ~ rep/(xf+yf), data=dat)

      ## vc(m2)
      ## ##   effect component std.error z.ratio bound
      ## ##      rep      4262      6890    0.62     P   0
      ## ##   rep:yf     15600      5091    3.1      P   0
      ## ##   rep:xf     14810      4865    3        P   0
      ## ## units(R)      8062      1340    6        P   0

      ## # Table 4
      ## predict(m2, data=dat, classify="gen")$pvals
      ## ##    gen predicted.value std.error    status
      ## ## 1  G01            1280      60.2 Estimable
      ## ## 2  G02            1550      60.2 Estimable
      ## ## 3  G03            1420      60.2 Estimable
      ## ## 4  G04            1450      60.2 Estimable
      ## ## 5  G05            1530      60.2 Estimable

    ## End(Not run)

    # ----------------------------------------------------------------------------

[Package agridat version 1.16 Index]