Abstract
Stepwise model selection is a commonly used technique in regression when there are many candidate independent variables and limited time to develop a model. This approach was adapted to the mixed models framework and gives good results, established by simulation with a known model and by application to real world data. Model selection is done using an information criterion (selected by the user). The application is primarily written in Perl. The Perl code tracks which variables are in or out of the model, calculates the information criterion, and writes and submits SAS code. Proc Mixed in SAS is used to compute the log-likelihood for a model, which is used to calculate the information criterion, which then is used to judge whether the model has improved by adding or dropping a variable, or by changing the covariance structure of the residuals. The software is currently restricted to the case where the random part of the model is assumed to be known, but how to augment the software to also select the structure for the random part of the model is discussed.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Kramer, Matthew
(2004).
"AUTOMATIC MODEL SELECTION IN THE MIXED MODELS FRAMEWORK,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1155
AUTOMATIC MODEL SELECTION IN THE MIXED MODELS FRAMEWORK
Stepwise model selection is a commonly used technique in regression when there are many candidate independent variables and limited time to develop a model. This approach was adapted to the mixed models framework and gives good results, established by simulation with a known model and by application to real world data. Model selection is done using an information criterion (selected by the user). The application is primarily written in Perl. The Perl code tracks which variables are in or out of the model, calculates the information criterion, and writes and submits SAS code. Proc Mixed in SAS is used to compute the log-likelihood for a model, which is used to calculate the information criterion, which then is used to judge whether the model has improved by adding or dropping a variable, or by changing the covariance structure of the residuals. The software is currently restricted to the case where the random part of the model is assumed to be known, but how to augment the software to also select the structure for the random part of the model is discussed.
