agridat (version 1.12)

kempton.slatehall: Slate Hall Farm 1976 spring wheat

Description

Yields for a trial at Slate Hall Farm in 1976.

Format

A data frame with 150 observations on the following 5 variables.

rep

Rep factor, 6 levels

row

Row

col

Column

gen

Genotype factor, 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.

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 {
data(kempton.slatehall)
dat <- kempton.slatehall
dat <- transform(dat, xf=factor(col), yf=factor(row))

desplot(yield ~ col * row, dat, num=gen, out1=rep,
        main="kempton.slatehall")

# Incomplete block model of Gilmour et al 1995
if(require(lme4) & require(lucid)){
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

}

# }
# NOT RUN {
# Incomplete block model of Gilmour et al 1995
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, classify="gen")$predictions$pvals

# }