progeny {hnp}

Sitophilus zeamais progeny

Description

Progeny of Sitophilus zeamais, the maize weevil, when treated with different organic extracts

Usage

data(progeny)

Format

A data frame with 40 observations on the following 2 variables.

extract	factor	levels leaf, branch, seed and control
y	numeric	number of emerged insects after 60 days

Details

Petri dishes containing 10g of corn were treated with extracts prepared with different parts of the plant Annona mucosa (seeds, leaves and branches) at a concentration of 1500 mg/kg or just water (control), using a completely randomized design with 10 replicates. Then 20 S. zeamais adults were placed in each Petri dish and the focus is on the numbers of emerged insects (progeny) after 60 days, see Ribeiro et al (2013).

Source

Demétrio, C. G. B., Hinde, J. and Moral, R. A. (2014) Models for overdispersed data in entomology. In Godoy, W. A. C. and Ferreira, C. P. (Eds.) Ecological modelling applied to entomology. Springer.

References

Ribeiro, L. P., Vendramin, J. D., Bicalho, K. U., Andrade, M. S., Fernandes, J. B., Moral, R. A., Demétrio, C. G. B. (2013) Annona mucosa Jacq. (Annonaceae): A promising source of bioactive compounds against Sitophilus zeamais Mots. (Coleoptera: Curculionidae). J Stored Prod Res 55:6-14

Moral, R. A., Hinde, J. and Demétrio, C. G. B. (2017) Half-normal plots and overdispersed models in R: the hnp package. Journal of Statistical Software 81(10):1-23.

Examples

data(progeny)

# Poisson fit
model1 <- glm(y ~ extract, family=poisson, data=progeny)
anova(model1, test="Chisq")                
                 
# Quasi-Poisson fit    
model2 <- glm(y ~ extract, family=quasipoisson, data=progeny)
summary(model2)$dispersion
anova(model2, test="F")

# half-normal plots
par(mfrow=c(1,2),cex=1.4, cex.main=0.9, pty='s')
hnp(model1, pch=4, main="(a) Poisson; log-linear",
    xlab="Half-normal scores", ylab="Deviance residuals")
hnp(model2, pch=4, main="(b) Quasi-Poisson; log-linear",
    xlab="Half-normal scores", ylab="Deviance residuals")
    
anova(model1, test="Chisq") # Poisson model
anova(model2, test="F") # quasi-Poisson model
summary(model1) # Poisson model
summary(model2) # quasi-Poisson model

# now with factor level parameterisation
summary(update(model1,.~.-1))
summary(update(model2,.~.-1))

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