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.

Source

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.

References

None

Examples

if (FALSE) { # \dontrun{

  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")
} # }