Number of cotton bolls for different levels of defoliation.

Number of cotton bolls, nodes, plant height, and plant weight for different levels of defoliation.

Usage

data("silva.cotton")

Format

A data frame with 125 observations on the following 4 variables.

stage: growth stage
defoliation: level of defoliation, 0, 25, 50, 75, 100
plant: plant number
rep: replicate
reproductive: number of reproductive structures
bolls: number of bolls
height: plant height
nodes: number of nodes
weight: weight of bolls

Details

Data come from a greenhouse experiment with cotton plants. Completely randomized design with 5 replicates, 2 plants per pot.

Artificial defoliation was used at levels 0, 25, 50, 75, 100 percent.

Data was collected per plant at five growth stages: vegetative, flower-bud, blossom, fig and cotton boll.

The primary response variable is the number of bolls. The data are counts, underdispersed, correlated.

Zeviana et al. used this data to compared Poisson, Gamma-count, and quasi-Poisson GLMs.

Bonat & Zeviani used this data to fit multivariate correlated generalized linear model.

Used with permission of Walmes Zeviani.

Electronic version from: https://www.leg.ufpr.br/~walmes/data/desfolha_algodao.txt

Source

Silva, Anderson Miguel da; Degrande, Paulo Eduardo; Suekane, Renato; Fernandes, Marcos Gino; & Zeviani, Walmes Marques. (2012). Impacto de diferentes niveis de desfolha artificial nos estadios fenologicos do algodoeiro. Revista de Ciencias Agrarias, 35(1), 163-172. https://www.scielo.mec.pt/scielo.php?script=sci_arttext&pid=S0871-018X2012000100016&lng=pt&tlng=pt.

References

Zeviani, W. M., Ribeiro, P. J., Bonat, W. H., Shimakura, S. E., Muniz, J. A. (2014). The Gamma-count distribution in the analysis of experimental underdispersed data. Journal of Applied Statistics, 41(12), 1-11. https://doi.org/10.1080/02664763.2014.922168 Online supplement: https://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014

Regression Models for Count Data. https://cursos.leg.ufpr.br/rmcd/applications.html#cotton-bolls

Wagner Hugo Bonat & Walmes Marques Zeviani (2017). Multivariate Covariance Generalized Linear Models for the Analysis of Experimental Data. Short-cource at: 62nd RBras and 17th SEAGRO meeting/ https://github.com/leg-ufpr/mcglm4aed

Examples

if (FALSE) { # \dontrun{
  
library(agridat)
data(silva.cotton)
dat <- silva.cotton
dat$stage <- ordered(dat$stage,
                     levels=c("vegetative","flowerbud","blossom","boll","bollopen"))
# make stage a numeric factors
dat <- transform(dat,
                 stage = factor(stage, levels = unique(stage),
                                labels = 1:nlevels(stage)))

# sum data across plants, 1 pot = 2 plants
dat <- aggregate(cbind(weight,height,bolls,nodes) ~
                    stage+defoliation+rep, data=dat, FUN=sum)

  # all traits, plant-level data
  libs(latticeExtra)
  foo <- xyplot(weight + height + bolls + nodes ~ defoliation | stage,
                data = dat, outer=TRUE,
                xlab="Defoliation percent", ylab="", main="silva.cotton",
                as.table = TRUE, jitter.x = TRUE, type = c("p", "smooth"),
                scales = list(y = "free"))
  combineLimits(useOuterStrips(foo))


if(0){

  # poisson glm with quadratic effect for defoliation
  m0 <- glm(bolls ~ 1, data=dat, family=poisson)
  m1 <- glm(bolls ~ defoliation+I(defoliation^2), data=dat, family=poisson)
  m2 <- glm(bolls ~ stage:defoliation+I(defoliation^2), data=dat, family=poisson)
  m3 <- glm(bolls ~ stage:(defoliation+I(defoliation^2)), data=dat, family=poisson)
  par(mfrow=c(2,2)); plot(m3); layout(1)
  anova(m0, m1, m2, m3, test="Chisq")

  # predicted values
  preddat <- expand.grid(stage=levels(dat$stage),
                         defoliation=seq(0,100,length=20))
  preddat$pred <- predict(m3, newdata=preddat, type="response")

  # Zeviani figure 3
  libs(latticeExtra)
  xyplot(bolls ~ jitter(defoliation)|stage, dat,
         as.table=TRUE,
         main="silva.cotton - observed and model predictions",
         xlab="Defoliation percent",
         ylab="Number of bolls") +
    xyplot(pred ~ defoliation|stage, data=preddat,
           as.table=TRUE,
           type='smooth', col="black", lwd=2)
}

if(0){
  # ----- mcglm -----
  dat <- transform(dat, deffac=factor(defoliation))

  libs(car)

  vars <- c("weight","height","bolls","nodes")
  splom(~dat[vars], data=dat,
        groups = stage,
        auto.key = list(title = "Growth stage",
                        cex.title = 1,
                        columns = 3),
        par.settings = list(superpose.symbol = list(pch = 4)),
        as.matrix = TRUE)

  splom(~dat[vars], data=dat,
        groups = defoliation,
        auto.key = list(title = "Artificial defoliation",
                        cex.title = 1,
                        columns = 3),
        as.matrix = TRUE)

  # multivariate linear model.
  m1 <- lm(cbind(weight, height, bolls, nodes) ~ stage * deffac,
           data = dat)
  anova(m1)

  summary.aov(m1)

  r0 <- residuals(m1)

  # Checking the models assumptions on the residuals.
  car::scatterplotMatrix(r0,
                         gap = 0, smooth = FALSE, reg.line = FALSE, ellipse = TRUE,
                         diagonal = "qqplot")

  }
} # }