agridat (version 1.12)

shafii.rapeseed: Rapeseed yield multi-environment trial, 3 years

Description

Rapeseed yield multi-environment trial, 3 years

Format

A data frame with 648 observations on the following 5 variables.

year

Year, numeric: 87, 88, 89

loc

Location factor, 14 levels

rep

Rep factor, 3 levels

gen

Genotype factor, 6 levels

yield

Yield, kg/ha

Details

SAS codes for the analysis can be found at http://www.uiweb.uidaho.edu/ag/statprog/ammi/

The data are from the U.S. National Winter Rapeseed trials conducted in 1986, 1987, and 1988. Trial locations included Georgia (GGA, TGA), Idaho (ID), Kansas (KS), Mississippi (MS), Montana (MT), New York (NY), North Carolina (NC), Oregon (OR), South Carolina (SC), Tennessee (TN), Texas (TX), Virginia (VA), and Washington (WA).

References

Bahman Shafii and William J Price, 1998. Analysis of Genotype-by-Environment Interaction Using the Additive Main Effects and Multiplicative Interaction Model and Stability Estimates, JABES, 3, 335--345.

Examples

# NOT RUN {
data(shafii.rapeseed)
dat <- shafii.rapeseed

dat$gen <- with(dat, reorder(gen, yield, mean))
dat$loc <- with(dat, reorder(loc, yield, mean))
dat$yield <- dat$yield/1000

dat <- transform(dat, rep=factor(rep), year=as.factor(as.character(year)))
dat$locyr = paste(dat$loc, dat$year, sep="")

# The 'means' of reps
datm <- aggregate(yield~gen+year+loc+locyr, data=dat, FUN=mean)
datm <- datm[order(datm$gen),]
datm$gen <- as.character(datm$gen)
datm$gen <- factor(datm$gen,
                       levels=c("Bienvenu","Bridger","Cascade",
                         "Dwarf","Glacier","Jet"))
dat$locyr <- reorder(dat$locyr, dat$yield, mean)

require(lattice)
# This picture tells most of the story
# Now change symbols
op <- tpg <- trellis.par.get()
tpg$superpose.symbol$pch <- c('7','8','9')
trellis.par.set(tpg)
dotplot(loc~yield|gen,group=year,data=dat,
        auto.key=list(columns=3),
        main="shafii.rapeseed",ylab="Location")
#dotplot(loc~yield|gen,group=year,data=datm,auto.key=TRUE,
#        main="shafii.rapeseed")

# AMMI biplot.  Remove gen and locyr effects.
m1.lm <- lm(yield ~ gen + locyr, data=datm)
datm$res <- resid(m1.lm)
# Convert to a matrix
require("reshape2")
dm <- melt(datm, measure.var='res', id.var=c('gen', 'locyr'))
dmat <- acast(dm, gen~locyr)
# AMMI biplot.  Figure 1 of Shafii (1998)
biplot(prcomp(dmat), main="shafii.rapeseed - AMMI biplot")
trellis.par.set(op) # Unset graphics changes

# }