silva.cotton {agridat} | R Documentation

## 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.

### 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. http://www.scielo.mec.pt/scielo.php?script=sci_arttext&pid=S0871-018X2012000100016&lng=pt&tlng=pt.

Used with permission of Walmes Zeviani.

Electronic version from: http://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. http://doi.org/10.1080/02664763.2014.922168 Online supplement: http://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014

Regression Models for Count Data. http://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

    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
    if(require(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))
    }

    ## Not run:

      # 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
      require(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)

    ## End(Not run)

    ## Not run:
      # ----- mcglm -----
      dat <- transform(dat, deffac=factor(defoliation))

      library(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")

    ## End(Not run)

[Package agridat version 1.16 Index]