Principal component analysis (PCA) is a mathematical transformation of possibly(correlated) variables into a number of uncorrelated variables called principal components. The resulting components from this transformation is defined in such a way that t...
Monthly Archives: November 2010
Slides from first Utah.edu & R.P. RUG meeting
Here are the slides from the first University of Utah and Research Park R Users Group meeting. They discuss getting help and finding packages. R
How to make beautiful bubble charts with R
Nathan Yau has just published at FlowingData a step-by-step guide on making bubble charts in R. It's actually pretty simple: read in data, sqrt-transform the "bubble" variable (to scale the bubbles by area, not radius), and use the symbols function to plot. It's the last step, though, that really ups the presentation quality: read R's PDF file into Illustrator...
R and AOL in NYC
R and the NYC R User Group get brief mentions in this article about AOL's offices in New York City. The NYC UseRs meet at AOL and (ironically) the next meeting on Dec 9 is on the topic of R at Google. New York Observer: Bringing Some Sizzle to the Dial-Up King (via)
R Style Guide
Each year I have the pleasure (actually it’s quite fun) of teaching R programming to first year mathematics and statistics students. The vast majority of these students have no experience of programming, yet think they are good with computers because they use facebook! The class has around 100 students, and there are eight practicals. In 
Robust adaptive Metropolis algorithm [arXiv:10114381]
![Robust adaptive Metropolis algorithm [arXiv:10114381]](http://www.r-bloggers.com/wp-content/uploads/2010/11/am-adapt-student.jpg?w=102&h=117)
Matti Vihola has posted a new paper on arXiv about adaptive (random walk) Metropolis-Hastings algorithms. The update in the (lower diagonal) scale matrix is where is the current acceptance probability and the target acceptance rate; is the current random noise for the proposal, ; is a step size sequence decaying to zero. The spirit of 
Learn Logistic Regression (and beyond)
One of the current best tools in the machine learning toolbox is the 1930s statistical technique called logistic regression. We explain how to add professional quality logistic regression to your analytic repertoire and describe a bit beyond that. A statistical analyst working on data tends to deliberately start simple move cautiously to more complicated methods. Related posts:
makefiles for Sweave, R and LaTeX using Eclipse on Windows
This post provides a brief introduction to make and makefiles. In particular it describes how to set up make on Windows with an emphasis on using make in Eclipse on projects involving R, Sweave, and LaTeX. Overview make is software that uses makefile...
makefiles for Sweave, R and LaTeX using Eclipse on Windows
This post provides a brief introduction to make and makefiles. In particular it describes how to set up make on Windows with an emphasis on using make in Eclipse on projects involving R, Sweave, and LaTeX. Overview make is software that uses makefile...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
