57-3
Generalized Linear Mixed Models.

Monday, November 4, 2013: 2:50 PM
Marriott Tampa Waterside, Grand Ballroom E, Second Level

Mark S. West, Agricultural Research Service, United States Department of Agriculture, Fort Collins, CO
Strong inferences require ample data collected from well-planned studies. Data collected from such studies contain information to determine which plausible statistical model is most appropriate and provides good power to detect real effects.  The generalized linear mixed models framework provides a wealth of models (whether the data are measured on a continuous scale or are discrete counts), and are suitable for a wide variety of experimental designs, including those with random effects. We will explain concepts underlying generalized linear mixed models and discuss approaches for powering experiments so that inferences can be made with confidence. Examples using SAS and R will be provided.
See more from this Division: ASA Section: Biometry and Statistical Computing
See more from this Session: Symposium--Advanced Statistical Approaches to Reach Strong Inferences From Agronomic and Environmental Studies

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