RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend.

Format

A data frame with 150 observations on the following 4 variables.

yield: yield of wheat
gen: genotype, factor with 50 levels
col: column/block
row: row

Details

RCB experiment on wheat at El Batan, Mexico. There are three single-column replicates with 50 varieties in each replicate.

Plot dimensions are not given by Besag.

Data retrieved from https://web.archive.org/web/19991008143232/www.stat.duke.edu/~higdon/trials/elbatan.dat

Used with permission of David Higdon.

Source

Julian Besag and D Higdon, 1999. Bayesian Analysis of Agricultural Field Experiments, Journal of the Royal Statistical Society: Series B,61, 691--746. Table 1. https://doi.org/10.1111/1467-9868.00201

References

Wilkinson 1984.

Besag & Seheult 1989.

Examples

# \dontrun{

library(agridat)
data(besag.elbatan)
dat <- besag.elbatan

libs(desplot)
desplot(dat, yield~col*row,
        num=gen, # aspect unknown
        main="besag.elbatan - wheat yields")


# Besag figure 1
library(lattice)
xyplot(yield~row|col, dat, type=c('l'),
       layout=c(1,3), main="besag.elbatan wheat yields")


# RCB
m1 <- lm(yield ~ 0 + gen + factor(col), dat)
p1 <- coef(m1)[1:50]

# Formerly used gam package, but as of R 3.1, Rcmd check --as-cran
# is complaining
# Calls: plot.gam ... model.matrix.gam -> predict -> predict.gam -> array
# but it works perfectly in interactive mode !!!
# Remove the FALSE to run the code below
if(is.element("gam", search())) detach(package:gam)
libs(mgcv)
#> Loading required package: nlme
#> 
#> Attaching package: 'nlme'
#> The following object is masked from 'package:lme4':
#> 
#>     lmList
#> The following object is masked from 'package:dplyr':
#> 
#>     collapse
#> This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
m2 <- mgcv::gam(yield ~ -1 + gen + factor(col) + s(row), data=dat)
plot(m2, residuals=TRUE, main="besag.elbatan")
pred <- cbind(dat, predict(m2, dat, type="terms"))
# Need to correct for the average loess effect, which is like
# an overall intercept term.
adjlo <-  mean(pred$"s(row)")
p2 <- coef(m2)[1:50] + adjlo

# Compare estimates
lims <- range(c(p1,p2))
plot(p1, p2, xlab="RCB prediction",
     ylab="RCB with smooth trend (predicted)",
     type='n', xlim=lims, ylim=lims,
     main="besag.elbatan")
text(p1, p2, 1:50, cex=.5)
abline(0,1,col="gray")

# }