cotton_defol: Number of Bolls in Cotton under Artifitial Defoliation

Description Format References Examples

Description

These data are the results of a greenshouse factorial experiment performed to evaluate the effect of defoliation on the production of cotton at different growth stages. The experiment is a 5\times 5 factorial with 5 replications in a complete randomized design. The experimental unit was a pot with 2 cotton plants. The response variable is the number of bolls produced at the end of the crop cycle. The observed number of cotton bolls is a count variable with underdispersion (sample variance less than the sample mean).

Format

A data.frame with 125 records and 4 variables, described below.

References

Silva, A. M., Degrande, P. E., Suekane, R., Fernandes, M. G., Zeviani, W. M. (2012). Impacto de diferentes níveis de desfolha artificial nos estádios fenológicos do algodoeiro. Revista de Ciências Agrárias, 35(1), 163–172. http://www.scielo.mec.pt/pdf/rca/v35n1/v35n1a16.pdf.

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, http://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014.

Examples

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library(lattice)
library(latticeExtra)

data(cotton_defol)
str(cotton_defol)

# x11(width = 7, height = 2.8)
xyplot(bolls ~ defol | phenol,
       data = cotton_defol,
       layout = c(NA, 1),
       type = c("p", "smooth"),
       xlab = "Artificial defoliation level",
       ylab = "Number of bolls produced",
       xlim = extendrange(c(0:1), f = 0.15),
       jitter.x = TRUE)

# Sample mean and variance in each treatment cell.
mv <- aggregate(bolls ~ phenol + defol,
                data = cotton_defol,
                FUN = function(x) {
                    c(mean = mean(x), var = var(x))
                })
str(mv)

xlim <- ylim <- extendrange(c(mv$bolls), f = 0.05)

# Evidence of underdispersion.
xyplot(bolls[, "var"] ~ bolls[, "mean"],
       data = mv,
       grid = TRUE,
       aspect = "iso",
       type = c("p", "r"),
       xlim = xlim,
       ylim = ylim,
       ylab = "Sample variance",
       xlab = "Sample mean") +
    layer(panel.abline(a = 0, b = 1, lty = 2))

JrEduardo/gammacount documentation built on May 8, 2019, 4:41 p.m.