agridat (version 1.23)

silva.cotton: Number of cotton bolls for different levels of defoliation.

Description

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

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) {
  
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")

  }
}