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...
168 search results for "PCA"
My Day at ACM Data Mining Camp III
My first time at ACM Data Mining Camp was so awesome, that I was thrilled the make the trip up to San Jose for the November 2010 version. In July, I gave a talk at the Emerging Technologies for Online Learning Symposium conference with a faculty member in the Department of Statistics, at the Fairmont. The place was amazing,...
An analysis of the Stackoverflow Beta sites
In the last six months or so, the behemoth of Q & A sites stackoverflow, decided to change tack and launch a number of other non-computing-language sites. To launch a site in the stackoverflow family, sites have to spend time gathering followers in Area51. Once a site has gained a critical mass, a new StackExchange 
How to Start Using (pgf)Sweave in LyX in One Minute
regor Gorjanc published an interesting article “Using Sweave with LyX” in R News in 2008, which (I believe) makes it much easier to use Sweave. I use command-line tools a lot every day, but I am still “GUI-addicted”. (I don’t want to comment more about Microsoft Word here.) LyX is a somewhat WYSIWYG tool based
Grabbing Tables in Webpages Using the XML Package
ables are pretty common in web pages as data sources, and the most direct way to get these data is probably to copy and paste. This is OK if there are only two or three tables, and when we need to grab 5000 tables in 1000 web pages, we may not really wish to fulfill
On the Gory Loops in R
his blog post is mainly for Stat 579 students on the homework for week 7, since I received too many “gory” loops in the homework submissions and I think it would help a bit to write my thoughts on R loops for beginners. The immortal motto for newbies in programming is: If you want to
Principal Component Analysis (PCA) vs Ordinary Least Squares (OLS): A Visual Explanation
Over at stats.stackexchange.com recently, a really interesting question was raised about principal component analysis (PCA). The gist was “Thanks to my college class I can do the math, but what does it MEAN?” I felt like this a number of times in my life. Many of my classes were focused on the technical implementations they kinda
SyntaxHighlighter Brush for the R Language
al Galili requested in the R-help mailing list for a SyntaxHighlighter brush for the R language, so that WordPress users can highlight their R code easily. I promised to contribute a few minutes on this task, and here is the result: /** * Author: Yihui Xie * URL: http://yihui.name/en/2010/09/syntaxhighlighter-brush-for-the-r-language * License: GPL-2 | GPL-3 */
Eigenimages: The AT&T Cambridge Faces Database
I picked up the AT&T Laboratories Cambridge database of faces for a clustering application. The database consists of images of 40 distinct subjects, each in 10 different facial positions and expressions. Typically, the goal of clustering in these data is to recover the ‘true’ partition, or that which isolates images of distinct subjects. Each image
Global Temperature Proxy Reconstructions ~ now with CO2 forcing
Previously, I did a simple Bayesian projection of recent temperature using proxy data and the methods shown in McShane and Wyner (2010). I showed that when you take out the last 30 years of data (1969~1998), the projection does not track the recent uptick in temperatures well. The “projection” is a simple unparametric bootstrap which 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
