Literature DB >> 23716396

Graphical tools for model selection in generalized linear models.

K Murray1, S Heritier, S Müller.   

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.
Copyright © 2013 John Wiley & Sons, Ltd.

Keywords:  Akaike information criterion; Bayesian information criterion; generalized linear models; graphical methods; model selection; model selection curves; variable selection

Mesh:

Year:  2013        PMID: 23716396     DOI: 10.1002/sim.5855

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  2 in total

1.  Respiratory infections in acute stroke: nasogastric tubes and immobility are stronger predictors than dysphagia.

Authors:  Emily Brogan; Claire Langdon; Kim Brookes; Charley Budgeon; David Blacker
Journal:  Dysphagia       Date:  2014-01-21       Impact factor: 3.438

2.  FARMS: A New Algorithm for Variable Selection.

Authors:  Susana Perez-Alvarez; Guadalupe Gómez; Christian Brander
Journal:  Biomed Res Int       Date:  2015-07-26       Impact factor: 3.411

  2 in total

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