Readings
R Coding standards (Syntax style guide) for the class. PLEASE READ THIS. WE EXPECT ALL R SCRIPTS TO BE DONE IN THIS STYLE GUIDE.
Weekly readings:
- January 10th & 15th 2013 - Read chapters 1 & 2 in Dalgaard, and chapter 1 in Vasishth (ignore the bit about LaTex for now). Go through the R tutorial (after Thursdays class).
- January 17th & 24th 2013 - Read chapter 2 in Vasishth, and chapters 1 & 2 in Gotelli and Ellison. The material in Gotelli and Ellison is a kinder gentler introduction to probability, so you may wish to start there, and then move onto Vasishth (which you may then be ok just using for R code). chapter 3 in Dalgaard is optional, as all the material is covered in Vasishth.
- January 24th 2013 - Read Chapter 3 in Gotelli and Ellison & Chapter 4 in Dalgaard.
- Jan 29th 2013 - Gotelli and Ellison: Chapter 4 & pages 117-121 of Chapter 5.
- February 1st 2013 - Read Chapter 3 in Vasishth and Broe. Chapter 4 and pages 117-121 in Gotelli and Ellison.
- February 6th and 8th. - Chapters 6-7 of Gotelli and Ellison. You may also want to check out (optional reading) the PDF in the folder (Feb6th) "Power Analysis and Experimental Design", which briefly summarizes many of the important concepts relating experimental design and power (which is the focus of Chapter 4 of Vasishth, which I have not yet assigned as a reading).
- February 24th - (regression) Chapter 9 in Gotelli and Ellison (for conceptual background), and chapter 6 in Dalgaard (for R specific implementation). IF you would like a more mathematically rigorous introduction to this material, let me know.
- March 23rd & 27th - (These will only make sense once you have done the readings from Feb 24th).
- Dalgaard - chapter/section 7.5, the anova table in regression analysis ( pages 141-143).
- Dalgaard - chapter/section 12.1, pages 195-198.
- March 29th - Dalgaard chapters 11 & 12.
- April 10th - Gotelli and Ellison from Pages 290-304 (First part of chapter 10. Dalgaard pages 127-136 (First part of chapter 7).
Also see the R introduction manual http://cran.r-project.org/doc/manuals/R-intro.pdf chapter 11 for help with building models.
Note: Books highlighted in blue will be very helpful to develop your skills to get you ready for ZOL851 (MSU).
Primary Literature
Just as you need to keep yourself abreast of the developments in your particular field of study (from theory to important empirical studies) it is equally important to keep informed with developments in the statistical methods that inform your area of expertise. Readings from the primary literature are designed to supplement the material presented in lecture and will increase your general understanding and facilitate discussion. All readings will be made available through the wiki.
Required:
Gotelli, N.J. & Ellison, A.M. 2004. A Primer of Ecological Statistics. Sinauer.
This slim book represents a good introduction for many of the basic concepts in inferences, probability, experimental design and statistics. It has a basic introduction to a number of different approaches for estimation and inference, and in particular its short guide to experimental design is quite good. It is not really a “hands-on” approach, so you will not learn how to actually run the analyses or examine diagnostics of models. This Book needs to be purchased (there is no access to a digital copy from the MSU library).
Link to book at Amazon. ~ 42$ with shipping.
Link to book at Sinauer. 39$ + Free shipping for students!
Vasishth, S. and Michael Broe. 2010. The foundations of statistics: A simulation-based approach. Springer. This text is available online VIA the MSU library & a draft PDF is available on the course Wiki.
(Note: I am not sure if this is a permanent link, so download the PDF of the chapters, otherwise use the draft of this book). If the folks at UW have any problem accessing this please let me know.
Dalgaard, P. 2008. Introductory Statistics with R. 2nd ed. Springer. This book is most useful in helping develop you basic skills with using R for performing basic manipulations with your data set, as well as simple plotting and statistical procedures. It will also be useful as review of some basic statistical concepts, for which you may need to be reminded. It is quite clear, and its examples are easy to follow. This text is available online VIA the MSU library. If you need access (and your library does not have it), please contact Ian.
http://catalog.lib.msu.edu/record=b7233478~S39a
Links to the book website
http://staff.pubhealth.ku.dk/~pd/ISwR.htm
Recommended texts (in alphabetical order, not importance): These are a variety of texts that may not only serve you well for this class, but for your graduate work and beyond. We are not suggesting that you purchase all of these, books, but this list may come in handy as you develop your skills, and want to further explore other areas of interest.
Programming in R.
Adler, J. 2009. R In a Nutshell. O’Rielly.
This book is a great introduction and overview to PROGRAMMING in R, including a lot of cools tips and tricks to make life easier. In particular basic programming, data management and plotting are covered well. What is not covered so well is statistical analysis (which would serve as a good place to look up functions, but not for understanding them). Still a good book overall.
Braun, W.J. & Murdoch, DJ. 2008. A First course in Statistical Programming with R. Cambridge.
This slim book covers a lot of the concepts with regards to how to efficiently write your code in R for statistical computation. If you plan to use R to do more than run regression models and make very simple graphics, I recommend it. It will be especially useful for people who plan to use R for power analysis (or any other) simulations, and numerical optimization (for maximum likelihood for instance). It is less useful for R programming in general, or for string manipulation (or handling the class systems, S3 & S4 in R).
Crawley, M.J. 2007. THE R BOOK. Wiley. (This also belongs in the Statistics set of books)
This is a behemoth of a book, but it provides fantastic coverage of R from introduction to intermediate (and some advanced methods) with respect to programming, plotting, data management and statistics. MSU also has online access to the book, so there is no reason to not use it. I may also require some readings out of this on occassion.
This text is available online VIA the MSU library.
http://catalog.lib.msu.edu/record=b7184579~S39a
Matloff, Norman. 2011. The art of R programming: A Tour of Statistical Software Design. No Starch Press.
This new book is a really great introduction to both R programming in general and how R was designed with a combination of object oriented and functional programming styles in mind. It is also very useful in helping to write efficient code with R data structures, and vectorized computation. I think it will really help folks coming from C like languages avoid major trip ups, and is by and large an enjoyable read.
Statistics ( both conceptual and R specific)
Quinn, G.P. Keough, M.J. 2002. Experimental Design and data analysis for biologists.
This covers much of the same material as Gotelli and Ellison, and is a good introductory book, and reference book. It has some useful material on multivariate statistics as well. No real practical examples in R (or SAS). If you have Gotellia and Ellison, you probably do not need this, but it is a good alternative.
Seefeld, K. Linder, E. 2007. Statistics Using R with Biological Examples.
A free online book. This book teaches statistics starting at a reasonably introductory level within a strongly Bayesian and computational framework. In particular the chapters on probability are excellent!
http://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf
Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with S. 4th edition. Springer, New York, NY.
One of the classic texts used for both R & S (R is derived from S, and is extremely similar). This is not a “how to do/learn statistics” text, but how to use R/S to perform statistics. Still it is extremely invaluable as a resource. This text is available online VIA the MSU library.
R Resources
To download R (and the starting point for virtually all things R)
http://cran.r-project.org/
The manuals for using & programming in R . “An Introduction to R” & “R Data Import/Export” are the important ones for this class. The rest are largely useful if you really get into programming.
http://cran.r-project.org/manuals.html
The contributed documents section on the CRAN site is REALLY USEFUL. It has lots of books and tutorials of exceptional quality. http://cran.r-project.org/other-docs.html
The “Task views” section describes some of the packages/libraries in R that are useful for particular tasks ( examples include “multivariate”, “Bayesian”, “Ecological and environmental data” & “spatial data” to name a few).
http://cran.r-project.org/web/views/
Tips for using R
http://pj.freefaculty.org/R/Rtips.html
Here is a useful little (less than 100 pages) book on programming in R, that is quite clear and recent.
http://probability.ca/cran/doc/contrib/Lam-IntroductionToR_LHL.pdf
Another nice short guide in programming in R.
http://faculty.washington.edu/tlumley/Rcourse/R-fundamentals.pdf
kick-starting R
http://cran.r-project.org/doc/contrib/Lemon-kickstart/
Course notes for “Statistical Programming in R/S”
http://socserv.mcmaster.ca/jfox/Courses/R-course/index.html
Programming in R web site with examples
http://www.faculty.ucr.edu/~tgirke/Documents/R_BioCond/R_Programming.html
"R Inferno" is a nice, very funny short document on programming in R, and how to A) Use R well B) avoid common mistakes & C) Not treat R as a C like language.
http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
Examples of high end graphics and plots in R with source codes
http://addictedtor.free.fr/graphiques/
The R wiki –lots of useful example code bits. In particular this has useful information on Regression & mixed models in R, and examples from the Ecological Detective for R.
http://wiki.r-project.org
The R style guide... Good programming guidelines to make your code human readable
http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html
The R programming Wikibook...
http://en.wikibooks.org/wiki/R_Programming
http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R
This latter page just has some links to Data mining algorithms in R.
Other (statistics) web sites that may be useful to you.
Electronic Statistics textbook resource – EXCELLENT RESOURCE!!!!
http://www.statsoft.com/textbook/stathome.html
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