Description Usage Format Details Source References Examples

Multi-environment trial evaluating 36 maize genotypes in 9 locations

1 | ```
data("acorsi.grayleafspot")
``` |

A data frame with 324 observations on the following 3 variables.

`gen`

genotype, 36 levels

`env`

environment, 9 levels

`rep`

replicate, 2 levels

`y`

grey leaf spot severity

Experiments conducted in 9 environments in Brazil in 2010-11. Each location had an RCB with 2 reps.

The response variable is the percentage of leaf area affected by gray leaf spot within each experimental unit (plot).

Acorsi et al. use this data to illustrate the fitting of a generalized AMMI model with non-normal data.

C. R. L. Acorsi, T. A. Guedes, M. M. D. Coan, R. J. B. Pinto,
C. A. Scapim, C. A. P. Pacheco, P. E. O. Guimaraes, C. R. Casela. (2016).
Applying the generalized additive main effects and multiplicative
interaction model to analysis of maize genotypes resistant to grey leaf spot.
*Journal of Agricultural Science*.
https://doi.org/10.1017/S0021859616001015

Electronic data and R code kindly provided by Marlon Coan.

None

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library(agridat)
data(acorsi.grayleafspot)
dat <- acorsi.grayleafspot
# Acorsi figure 2. Note: Acorsi used cell means
op <- par(mfrow=c(2,1), mar=c(5,4,3,2))
libs(lattice)
boxplot(y ~ env, dat, las=2,
xlab="environment", ylab="GLS severity")
title("acorsi.grayleafspot")
boxplot(y ~ gen, dat, las=2,
xlab="genotype", ylab="GLS severity")
par(op)
## Not run:
# GLM models
# glm main-effects model with logit u(1-u) and wedderburn u^2(1-u)^2
# variance functions
# glm1 <- glm(y~ env/rep + gen + env, data=dat, family=quasibinomial)
# glm2 <- glm(y~ env/rep + gen + env, data=dat, family=wedderburn)
# plot(glm2, which=1); plot(glm2, which=2)
# GAMMI models of Acorsi. See also section 7.4 of Turner
# "Generalized nonlinear models in R: An overview of the gnm package"
# full gnm model with wedderburn, seems to work
libs(gnm)
set.seed(1)
gnm1 <- gnm(y ~ env/rep + env + gen + instances(Mult(env,gen),2),
data=dat,
family=wedderburn, iterMax =800)
deviance(gnm1) # 433.8548
# summary(gnm1)
# anova(gnm1, test ="F") # anodev, Acorsi table 4
## Df Deviance Resid. Df Resid. Dev F Pr(>F)
## NULL 647 3355.5
## env 8 1045.09 639 2310.4 68.4696 < 2.2e-16 ***
## env:rep 9 12.33 630 2298.1 0.7183 0.6923
## gen 35 1176.23 595 1121.9 17.6142 < 2.2e-16 ***
## Mult(env, gen, inst = 1) 42 375.94 553 745.9 4.6915 < 2.2e-16 ***
## Mult(env, gen, inst = 2) 40 312.06 513 433.9 4.0889 3.712e-14 ***
# maybe better, start simple and build up the model
gnm2a <- gnm(y ~ env/rep + env + gen,
data=dat,
family=wedderburn, iterMax =800)
# add first interaction term
res2a <- residSVD(gnm2a, env, gen, 2)
gnm2b <- update(gnm2a, . ~ . + Mult(env,gen,inst=1),
start = c(coef(gnm2a), res2a[, 1]))
deviance(gnm2b) # 692.19
# add second interaction term
res2b <- residSVD(gnm2b, env, gen, 2)
gnm2c <- update(gnm2b, . ~ . + Mult(env,gen,inst=1) + Mult(env,gen,inst=2),
start = c(coef(gnm2a), res2a[, 1], res2b[,1]))
deviance(gnm2c) # 433.8548
# anova(gnm2c) # weird error message
# note, to build the ammi biplot, use the first column of res2a to get
# axis 1, and the FIRST column of res2b to get axis 2. Slightly confusing
emat <- cbind(res2a[1:9, 1], res2b[1:9, 1])
rownames(emat) <- gsub("fac1.", "", rownames(emat))
gmat <- cbind(res2a[10:45, 1], res2b[10:45, 1])
rownames(gmat) <- gsub("fac2.", "", rownames(gmat))
# match Acorsi figure 4
biplot(gmat, emat, xlim=c(-2.2, 2.2), ylim=c(-2.2, 2.2), expand=2, cex=0.5,
xlab="Axis 1", ylab="Axis 2",
main="acorsi.grayleafspot - GAMMI biplot")
## End(Not run)
``` |

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