Analysing disease incidence data from designed experiments by generalized linear mixed models

As a result of aggregation or clustering of sampling units, disease inciden ce data from designed experiments frequently show overdispersion relative t o die binomial distribution. This paper discusses generalized linens mixed models (GLMM) suitable for analysing overdispersed disease incidence data. The methods are exemplified using data from a randomized complete block exp eriment on the incidence of downy mildew (Plasmopara viticola) of grape (Vi tis lambrusca). Hints are given regarding implementation of the methods usi ng the %GLIMMIX macro for the SAS system.