besag.triticale {agridat} | 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. http://doi.org/10.2307/2531047
None.
data(besag.triticale) dat <- besag.triticale if(require(desplot)){ desplot(yield ~ col*row, data=dat, # aspect unknown main="besag.triticale") } ## Not run: # 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. dat <- transform(dat, rate=factor(rate), nitro=factor(nitro)) dat <- transform(dat, xf=factor(col), yf=factor(row)) m2 <- lm(yield ~ gen + rate + nitro + regulator + yf, data=dat) anova(m2) # Similar, but not exact, to Besag figure 5 dat$res <- resid(m2) require(lattice) xyplot(res ~ col|as.character(row), data=dat, as.table=TRUE, type="s", layout=c(1,3), main="besag.triticale") ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: # asreml3 require(asreml) # Besag uses an adjustment based on neighboring plots. # This analysis fits the standard AR1xAR1 residual model dat <- dat[order(dat$xf, dat$yf), ] m3 <- asreml(yield ~ gen + rate + nitro + regulator + gen:rate + gen:nitro + gen:regulator + rate:nitro + rate:regulator + nitro:regulator + yf, data=dat, rcov = ~ ar1(xf):ar1(yf)) anova(m3) # Strongly significant gen, rate, regulator ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: ## require(asreml4) ## # Besag uses an adjustment based on neighboring plots. ## # This analysis fits the standard AR1xAR1 residual model ## dat <- dat[order(dat$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)