hnp (version 1.2-7)

cbb: Coffee berry borer trapping data

Description

Data on counts of coffee berry borer obtained using different traps through time.

Usage

data(cbb)

Format

A data frame with 288 observations on the following 4 variables.

week	numeric	week of observation (1 to 24)
block	factor	levels I II III IV
trap	factor	levels CV F SF
count	numeric	number of observed insects

Details

The coffee berry borer is a major pest of commercial coffee. The insect directly attacks the coffee fruit in development causing severe losses in bean production and quality. This data set was obtained in an experiment conducted by Mota (2013), where three types of traps (SF, F, CV) were randomized in each of four equidistant lines (blocks) of a coffee field. Each week, over a 24 week period, the insects were removed from the traps and counted.

References

Mota, L. H. C. (2013) Desenvolvimento de armadilha de auto-inoculacao para o controlde de Hypothenemus hampei (Ferrari, 1867) (Coleoptera: Curculionidae) com Beauveria bassiana (Bals.) Vuil (Ascomycota: Hypocreales) em tecido sinetico. Master's dissertation, ESALQ-USP

Examples

data(cbb)
# exploratory plot
require(latticeExtra)
trellis.par.set(strip.background=list(col="lightgrey"))
useOuterStrips(xyplot(count ~ week | block + trap, data=cbb,
  layout=c(3,1),type="l", col=1, xlab="Week", ylab="Insect counts"))

# Poisson fit
model1 <- glm(count ~ block + trap*factor(week),
              data=cbb, family=poisson)
anova(model1, test="Chisq")
sum(resid(model1, ty="pearson")^2)
summary(model1)
hnp(model1, sim=19, conf=1)

if (FALSE) {
hnp(model1) # default call
}

# Quasi-Poisson fit
model2 <- glm(count ~ block + trap*factor(week), data=cbb,
              family=quasipoisson)
anova(model2, test="F")
summary(model2)
hnp(model2, sim=19, conf=1)

if (FALSE) {
hnp(model2) # default call
}

## for discussion on the analysis of this data set,
## see Demetrio et al. (2014)