Statistical Modelling 3 (2003), 273–290

Generalized linear mixed models for strawberry inflorescence data

Diana J. Cole
Institue of Mathematics and Statistics,
University of Kent,
Canterbury, Kent CT2 7NF,

Byron J.T. Morgan, and Martin S. Ridout
University of Kent,


Strawberry inflorescences have a variable branching structure. This paper demonstrates how the inflorescence structure can be modelled concisely using binomial logistic generalized linear mixed models. Many different procedures exist for estimating the parameters of generalized linear mixed models, including penalized likelihood, EM, Bayesian techniques, and simulated maximum likelihood. The main methods are reviewed and compared for fitting binomial logistic generalized linear mixed models to strawberry inflorescence data. Simulations matched to the original data are used to show that a modified EM method due to Steele (1996) is clearly the best, in terms of speed and mean-squared-error performance, for data of this kind.


Correlated binomial; Gauss-Hermite quadrature; GLMMs; Laplace importance sampling; modified EM; penalized likelihood; random effects; simulated maximum likelihood; variance components.


MATLAB program for estimating parameters using the modified EM method (ModifiedEMmethod.m), example for a strawberry data set (Exampledataset.m), and a MATLAB program for generating a set of strawberry data (generatestrawberrydata.m) in zipped archive.