moths data frame has 41 rows and 4 columns.
These data are from a study of the effect of habitat on the
densities of two species of moth (A and P). Transects were
set across the search area. Within transects, sections
were identified according to habitat type.
This data frame contains the following columns:
length of transect
number of type A moths found
number of type P moths found
a factor with levels
Sharyn Wragg, formerly of Australian National University
print("Quasi Poisson Regression - Example 8.3") rbind(table(moths[,4]), sapply(split(moths[,-4], moths$habitat), apply,2, sum)) A.glm <- glm(formula = A ~ log(meters) + factor(habitat), family = quasipoisson, data = moths) summary(A.glm) # Note the huge standard errors moths$habitat <- relevel(moths$habitat, ref="Lowerside") A.glm <- glm(A ~ habitat + log(meters), family=quasipoisson, data=moths) summary(A.glm)$coef ## Consider as another possibility A2.glm <- glm(formula = A ~ sqrt(meters) + factor(habitat), family = quasipoisson(link=sqrt), data = moths) summary(A2.glm)