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Tips and Strategies for Mixed Modeling

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Tips and Strategies for Mixed Modeling
presented by Phil Gibbs, SAS Institute, Inc.

This presentation discusses best practices for using mixed modeling procedures for optimum efficiency.

Inherently, mixed modeling with SAS/STAT® procedures, such as GLIMMIX, MIXED, and NLMIXED is computationally intensive. Therefore, considerable memory and CPU time can be required. The default algorithms in these procedures might fail to converge for some data sets and models. This paper provides recommendations for circumventing memory problems and reducing execution times for your mixed modeling analyses. This paper also shows how the new HPMIXED procedure can be beneficial for certain situations, as with large sparse mixed models. Lastly, the discussion focuses on the best way to interpret and address common notes, warnings, and error messages that can occur with the estimation of mixed models in SAS software.

Presented at the SAS Users of Oregon Conference 2013 in Salem, OR.
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