Chi Yau - R Tutorial with Bayesian Statistics Using OpenBUGS
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Part III of the text is about Bayesian statistics. It begins with closed analytic solutions and basic BUGS models for simple examples. Then it covers OpenBUGS for Bayesian ANOVA and regression analysis. Finally, it shows how to build more complex Bayesian models and demonstrates CODA for Markov Chain Monte Carlo (MCMC) convergence.
The last part of this text discusses advanced GPU computing in R using the RPUDPLUS package. Topics include hierarchical clustering, Kendalls tau, support vector machines and Bayesian classification. It illustrates the importance of High Performance Computing (HPC) in the future of statistics. The text concludes with a new section on hierarchical multinomial logit model for marketing research.
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with Bayesian Statistics
Using OpenBUGS
Copyright 2012-2015 by Chi Yau
All rights reserved.
Reproduction or translation of any part of
this work beyond that permitted by Sections
107 and 108 of the 1976 United States Copyright
Act without the permission of the copyright
holder is unlawful.
This text provides R tutorials on statistics including hypothesis testing, ANOVA and linear regressions.It fulfills popular demands by users of r-tutor.comfor exercise solutions and offline access.
Part III of the text is about Bayesian statistics. It begins with closed analytic solutions and basic BUGS models for simple examples. Then it covers OpenBUGS for Bayesian ANOVA and regression analysis.Finally, it shows how to build more complex Bayesian models and demonstrates CODA for Markov Chain Monte Carlo (MCMC) convergence.
The last part of this text discusses advanced GPU computing in Rusing the RPUDPLUS package.Topics include hierarchical clustering, Kendall's tau,support vector machines and Bayesian classification.It illustrates the importance of High Performance Computing (HPC)in the future of statistics. The text concludes with an extra section on hierarchical multinomial logit model for marketing research.
This eBook is published in Amazon Kindle format. It is a great tool for self-study. The R source code is available for download in r-tutor.com.
I am grateful to users who sent me donations in the past. It provides meencouragement and support in ways unimaginable. I would also like to thank users who alert me of errors.I really appreciate it if you can email me further feedback to user.feedback@r-tutor.com.
Chi Yau
Palo Alto, California
April, 2014
PART I
PART II
PART III
PART IV
Appendix
PART I
1.2
1.3
1.4
1.5
1.6
2.2
2.3
2.4
2.5
2.6
3.2
3.3
4.2
5.2
5.3
5.4
PART II
6.2
6.3
6.4
6.5
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.2
8.3
8.4
8.5
8.6
8.7
8.8
8.9
8.10
8.11
8.12
8.13
8.14
9.2
9.3
9.4
9.5
9.6
9.7
9.8
9.9
9.10
10.2
10.3
10.4
10.5
10.6
10.7
11.2
11.3
11.4
11.5
11.6
11.7
11.8
11.9
12.2
12.3
12.4
12.5
12.6
13.2
13.3
14.2
15.2
15.3
16.2
16.3
16.4
17.2
17.3
17.4
17.5
17.6
17.7
17.8
18.2
18.3
18.4
18.5
18.6
19.2
PART III
20.2
20.3
21.2
21.3
22.2
22.3
23.2
23.3
23.4
24.2
24.3
25.2
25.3
25.4
26.2
26.3
27.2
28.2
28.3
29.2
29.3
PART IV
30.2
30.3
30.4
30.5
30.6
30.7
30.8
30.9
30.10
Appendix
A2.
A3.
R Introduction
We begin with a series of introductory R tutorials. It serves as background material for our discussions on .
The only hardware requirement for most of the R tutorials is a PC with the latest free open source R software installed. R has extensive documentation and active online community support. It is the perfect environment to get started in statistical computing.
We can visit the R homepage for its latest update,and find a list of CRAN mirrorsthat provide installation files in nearby locations.
R offers plenty of options for loading external data, including Excel, Minitab and SPSS files. We have included a tutorial on for the purpose.
After R is started, there is a console awaiting for input. At the prompt (>), we can enter numbers and perform calculations right away.
We assign values to variables with the assignment operator "=".Just typing the variable by itself at the prompt will print out the value.We should note that another form of assignment operator "<-" is also in use.
R functions are invoked by its name, then followed by the parenthesis, and zero or more arguments.The following apply the function c to combine three numeric values into a vector.
All text after the pound sign # within the same line is considered a comment.
Sometimes we need additional functionality beyond those offered by the core R library.In order to install an extension package, we should invoke the install.packages function at the prompt and follow the instruction.
R provides extensive documentation. For example, entering ?c or help(c)at the prompt gives documentation of the function c in R. Please give it a try.
If we are not sure about the name of the function we are looking for, we can perform a fuzzy search with the
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