besag.triticale {agridat} R Documentation

## Four-way factorial agronomic experiment in triticale

### Description

Four-way factorial agronomic experiment in triticale

### Usage

`data("besag.triticale")`

### Format

A data frame with 54 observations on the following 7 variables.

`yield`

yield, g/m^2

`row`

row

`col`

column

`gen`

genotype / variety, 3 levels

`rate`

seeding rate, kg/ha

`nitro`

nitrogen rate, kw/ha

`regulator`

growth regulator, 3 levels

### Details

Experiment conducted as a factorial on the yields of triticale. Fully randomized. Plots were 1.5m x 5.5m, but the orientation is not clear.

Besag and Kempton show how accounting for neighbors changes non-significant genotype differences into significant differences.

### Source

Julian Besag and Rob Kempton (1986). Statistical Analysis of Field Experiments Using Neighbouring Plots. Biometrics, 42, 231-251. Table 2. https://doi.org/10.2307/2531047

None.

### Examples

```## Not run:

library(agridat)
data(besag.triticale)
dat <- besag.triticale
dat <- transform(dat, rate=factor(rate), nitro=factor(nitro))
dat <- transform(dat, xf=factor(col), yf=factor(row))

libs(desplot)
desplot(dat, yield ~ col*row,
# aspect unknown
main="besag.triticale")

# Besag & Kempton are not perfectly clear on the model, but
# indicate that there was no evidence of any two-way interactions.
# A reduced, main-effect model had genotype effects that were
# "close to significant" at the five percent level.
# The model below has p-value of gen at .04, so must be slightly
# different than their model.
m2 <- lm(yield ~ gen + rate + nitro + regulator + yf, data=dat)
anova(m2)

# Similar, but not exact, to Besag figure 5
dat\$res <- resid(m2)
libs(lattice)
xyplot(res ~ col|as.character(row), data=dat,
as.table=TRUE, type="s", layout=c(1,3),
main="besag.triticale")

libs(asreml) # asreml4

# Besag uses an adjustment based on neighboring plots.
# This analysis fits the standard AR1xAR1 residual model

dat <- dat[order(dat\$xf, dat\$yf), ]
m3 <- asreml(yield ~ gen + rate + nitro + regulator +
gen:rate + gen:nitro + gen:regulator +
rate:nitro + rate:regulator +
nitro:regulator + yf, data=dat,
resid = ~ ar1(xf):ar1(yf))
wald(m3) # Strongly significant gen, rate, regulator
##                 Df Sum of Sq Wald statistic Pr(Chisq)
## (Intercept)      1   1288255        103.971 < 2.2e-16 ***
## gen              2    903262         72.899 < 2.2e-16 ***
## rate             1    104774          8.456  0.003638 **
## nitro            1       282          0.023  0.880139
## regulator        2    231403         18.676 8.802e-05 ***
## yf               2      3788          0.306  0.858263
## gen:rate         2      1364          0.110  0.946461
## gen:nitro        2     30822          2.488  0.288289
## gen:regulator    4     37269          3.008  0.556507
## rate:nitro       1      1488          0.120  0.728954
## rate:regulator   2     49296          3.979  0.136795
## nitro:regulator  2     41019          3.311  0.191042
## residual (MS)          12391

## End(Not run)
```

[Package agridat version 1.18 Index]