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R Statistics Cookbook

You're reading from   R Statistics Cookbook Over 100 recipes for performing complex statistical operations with R 3.5

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789802566
Length 448 pages
Edition 1st Edition
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Author (1):
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Francisco Juretig Francisco Juretig
Author Profile Icon Francisco Juretig
Francisco Juretig
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with R and Statistics FREE CHAPTER 2. Univariate and Multivariate Tests for Equality of Means 3. Linear Regression 4. Bayesian Regression 5. Nonparametric Methods 6. Robust Methods 7. Time Series Analysis 8. Mixed Effects Models 9. Predictive Models Using the Caret Package 10. Bayesian Networks and Hidden Markov Models 11. Other Books You May Enjoy

Using a model for prediction

Once we have trained a model and recovered the marginal posterior densities, we will probably want to use our model for predicting/scoring new samples. This is not as easy as in the classical approach, because our parameters are no longer fixed values, but distributions. This means that the predictions won't be point estimates/values, but a range of possible values, each one of them with an associated probability.

Getting ready

We will use STAN, which can be installed via install.packages("rstan").

How to do it...

We will use...

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