Abstract
As in a Poisson generalized linear mixed model (GLMM), one can also add into a binomial generalized linear model (GLM) random variation beyond what is stipulated by the binomial distribution. The chapter examines the relationship between precipitation during the breeding season and reproductive success; wet springs are likely to depress the proportion of successful nests. One can assume that all shrike populations have the same relation- ship between breeding success and standardized spring precipitation, but at different levels, corresponding to a random-intercepts model. This means assuming that every shrike population has a specific response to precipitation but that both intercept and slope are “similar” among populations. As for the Poisson case, the introduction of random effects into a binomial GLM in WinBUGS is particularly straightforward and transparent. Fitting the resulting binomial GLMM in WinBUGS is very helpful for the general understanding of this class of models.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have