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Caprice 1-2
Inference for Generalized Linear Mixed Models (304899)
*Christina Knudson, University of St. ThomasKeywords: modeling, inference, correlation, applied statistics, methodology, R
Generalized linear mixed models (GLMMS) are flexible enough to model data ranging from speed dating to salamander mating. These models are an extension of the linear model: GLMMs model a variety of response types (e.g. binomial, Poisson) and allow for correlation among the observations. We will begin this talk by briefly reviewing the requirements for most classical methods of statistical inference for linear models. Then we will loosen the modeling requirements until we arrive at GLMMs. Finally, we will explore an R package for fitting these models (R package "glmm") and perform some statistical inference on these models using example data sets.