besag.triticale | R Documentation |

Four-way factorial agronomic experiment in triticale

data("besag.triticale")

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

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.

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.

## 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)

agridat documentation built on June 15, 2022, 9:07 a.m.

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