Graphical tools for model selection in generalized linear models

K. Murray*, S. Heritier, S. Müller

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Model selection techniques have existed for many years; however, to date, simple, clear and effective methods of visualising the model building process are sparse. This article describes graphical methods that assist in the selection of models and comparison of many different selection criteria. Specifically, we describe for logistic regression, how to visualize measures of description loss and of model complexity to facilitate the model selection dilemma. We advocate the use of the bootstrap to assess the stability of selected models and to enhance our graphical tools. We demonstrate which variables are important using variable inclusion plots and show that these can be invaluable plots for the model building process. We show with two case studies how these proposed tools are useful to learn more about important variables in the data and how these tools can assist the understanding of the model building process.

Original languageEnglish
Pages (from-to)4438-4451
Number of pages14
JournalStatistics in Medicine
Issue number25
Publication statusPublished - 10 Nov 2013
Externally publishedYes


  • model selection curves
  • Akaike information criterion
  • graphical methods
  • Bayesian information criterion
  • variable selection
  • model selection
  • generalized linear models


Dive into the research topics of 'Graphical tools for model selection in generalized linear models'. Together they form a unique fingerprint.

Cite this