9. Statistical Modelling
Published Online: 6 NOV 2012
DOI: 10.1002/9781118448908.ch9
Copyright © 2013 John Wiley & Sons, Ltd
Book Title

The R Book, Second Edition
Additional Information
How to Cite
Crawley, M. J. (2012) Statistical Modelling, in The R Book, Second Edition, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118448908.ch9
Publication History
- Published Online: 6 NOV 2012
- Published Print: 7 DEC 2012
ISBN Information
Print ISBN: 9780470973929
Online ISBN: 9781118448908
- Summary
- Chapter
Keywords:
- statistical modelling;
- principle of parsimony (Occam's razor);
- model simplification steps, caveats;
- model formulae in R, creating formula objects;
- update function in model simplification;
- model checking, heteroscedasticity/non-normality of errors;
- summary of statistical models in R;
- optional arguments in model-fitting functions;
- Akaike's information criterion;
- model simplification by stepwise deletion, treatment /Helmert/Sum
Summary
This chapter contains sections titled:
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First things first
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Maximum likelihood
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The principle of parsimony (Occam's razor)
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Types of statistical model
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Steps involved in model simplification
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Model formulae in R
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Multiple error terms
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The intercept as parameter 1
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The update function in model simplification
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Model formulae for regression
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Box-Cox transformations
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Model criticism
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Model checking
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Influence
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Summary of statistical models in R
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Optional arguments in model-fitting functions
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Akaike's information criterion
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Leverage
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Misspecified model
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Model checking in R
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Extracting information from model objects
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The summary tables for continuous and categorical explanatory variables
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Contrasts
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Model simplification by stepwise deletion
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Comparison of the three kinds of contrasts
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Aliasing
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Orthogonal polynomial contrasts: contr.poly
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Summary of statistical modeling
