More on the modelling books
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model.Rmd
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model.Rmd
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### Other references
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### Other references
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More so than any other part of the book, these chapters are opinionated, and I'm not aware of any other presentation of exploratory model analysis.
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The modelling chapters are even more opinionated than the rest of the book. I approach modelling from a somewhat different perspective to most others, and there is relatively little space devoted to it. Modelling really deserves a book on its own, so I'd highly recommend that you read at least one of these three books:
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* Regression modelling strategies by Frank Harrell.
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* *Statistical Modeling: A Fresh Approach* by Danny Kaplan,
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<http://www.mosaic-web.org/go/StatisticalModeling/>. This book provides
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a gentle introduction to modelling, where you build your intuition,
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mathematical tools, and R skills in parallel.
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* *Statistical Modeling: A Fresh Approach* by Danny Kaplan;
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* *An Introduction to Statistical Learning* by Gareth James, Daniela Witten,
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Trevor Hastie, and Robert Tibshirani, <http://www-bcf.usc.edu/~gareth/ISL/>
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(available online for free). This book presents a family of modern modelling
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techniques collectively known as statistical learning.
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* *An Introduction to Statistical Learning* by James, Witten, Hastie, and Tibshirani
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* *Applied Predictive Modeling* by Max Kuhn and Kjell Johnson,
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<http://appliedpredictivemodeling.com>. This book is a companion to the
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* *Applied Predictive Modeling* by Kuhn and Johnson.
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__caret__ package, and provides practical tools for dealing with real-life
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predictive modelling challenges.
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For much of the insirpiration of the visualisations of these models, and extensions to more complex families, you might like "MODEL VIS PAPER"
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For much of the insirpiration of the visualisations of these models, and extensions to more complex families, you might like "MODEL VIS PAPER"
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