agridat (version 1.12)

durban.splitplot: Split-plot barley variety trial with fungicide treatments

Description

Split-plot barley variety trial with fungicide treatments.

Format

A data frame with 560 observations on the following 6 variables.

yield

Yield, tonnes/ha

block

Block factor, 4 levels

gen

Genotype factor, 70 levels

fung

Fungicide factor, 2 levels

row

Row

bed

Bed (column)

Details

Grown in 1995-1996 at the Scottish Crop Research Institute. Split-plot design with 4 blocks, 2 whole-plot fungicide treatments, and 70 barley varieties or variety mixes. Total area was 10 rows (north/south) by 56 beds (east/west).

Examples

# NOT RUN {
data(durban.splitplot)
dat <- durban.splitplot

# Durban 2003, Figure 2
m20 <- lm(yield~gen*fung, data=dat)
dat$resid <- m20$resid
require(lattice)
# xyplot(resid~row, dat, type=c('p','smooth'), main="durban.splitplot")
# xyplot(resid~bed, dat, type=c('p','smooth'), main="durban.splitplot")

# Figure 4 doesn't quite match due to different break points
coplot(resid~bed|row, data=dat, number=8, cex=.5,
       panel=function(x,y,...) panel.smooth(x,y,span=.4,...))
title("durban.splitplot")

# }
# NOT RUN {
# Figure 6 - field trend
require(gam)
m2lo <- gam(yield ~ gen*fung + lo(row, bed, span=.082), data=dat)
new2 <- expand.grid(row=unique(dat$row), bed=unique(dat$bed))
new2 <- cbind(new2, gen="G01", fung="F1")
p2lo <- predict(m2lo, new=new2)
wireframe(p2lo~row+bed, new2, aspect=c(1,.5), main="Field trend")

# Table 5, variance components.  Table 6, F tests
require(asreml)
dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
dat <- dat[order(dat$rowf, dat$bedf),]
m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat,
               rcov=~ar1v(rowf):ar1(bedf))
m2a2 <- update(m2a2)

require(lucid)
vc(m2a2)
##                effect component std.error z.ratio constr
##       block!block.var 0.0000001        NA      NA  bound
##  block:fung!block.var 0.01207    0.01513      0.8    pos
##       units!units.var 0.02463    0.002465    10      pos
##            R!variance 1                NA      NA    fix
##            R!rowf.cor 0.8836     0.03647     24    uncon
##            R!rowf.var 0.1262     0.04432      2.8    pos
##            R!bedf.cor 0.9202     0.02847     32    uncon

anova(m2a2)
# }