title: Graphical tools for model selection in generalized linear models.
abstract: 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.
authors.affiliation: School of Mathematics and Statistics, University of Sydney, Carslaw Building (F07), NSW 2006, Australia. kevin.murray@uwa.edu.au
authors.forename: K
authors.initials: K
authors.lastname: Murray
authors.forename: S
authors.initials: S
authors.lastname: Heritier
authors.forename: S
authors.initials: S
authors.lastname: Müller
mesh_terms: D001499:Bayes Theorem; D015897:Comorbidity; D003198:Computer Simulation; D003424:Crohn Disease / Q000453:epidemiology*; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D006699:Home Care Services / Q000706:statistics & numerical data; D006801:Humans; D007257:Information Theory; D016014:Linear Models; D016015:Logistic Models; D010166:Palliative Care / Q000379:methods / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012907:Smoking / Q000453:epidemiology*; D014904:Western Australia
keywords: Akaike information criterion; Bayesian information criterion; generalized linear models; graphical methods; model selection; model selection curves; variable selection