kempton.slatehall: Slate Hall Farm 1976 spring wheat

Description Format Details Source References Examples

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.

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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)

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

agridat documentation built on May 2, 2019, 4:01 p.m.