agridat (version 1.23)

acorsi.grayleafspot: Multi-environment trial evaluating 36 maize genotypes in 9 locations

Description

Multi-environment trial evaluating 36 maize genotypes in 9 locations

Usage

data("acorsi.grayleafspot")

Format

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

Details

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.

References

None

Examples

if (FALSE) {

  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)
  
  # 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")
}