In this post I present combined long term and recent trend charts of UAH Channel 5 temperature anomalies using R’s figure inside figure capability. This approach provides a better picture of global temperature trends than UAH’s day-of-y...
Monthly Archives: May 2010
Book Review – Modern Applied Statistics with S by W. N. Venables and B. D. Ripley (Springer 2003)
Order this book from Amazon Modern Applied Statistics with S (Fourth Edition) is one of the oldest and most popular books on Applied Statistics using R and S-plus. A large number of topics in Applied Statistics are covered in this book and it is certainly not for the faint hearted. A sound knowledge of
Computational Statistics
Do not resort to Monte Carlo methods unnecessarily. When I received this 2009 Springer-Verlag book, Computational Statistics, by James Gentle a while ago, I briefly took a look at the table of contents and decided to have a better look later… Now that I have gone through the whole book, I can write a short 
Sweave with Emacs and ESS, problem solved!
With help from Seb, the nagging problem I had when trying to sweaving with Emacs and ESS (http://old.nabble.com/ESS-and-Sweave-td28339734.html) has been solved.First of all, apply the patch for ess-swv.el, provided by Seb. Second, set the default ...
Using the update function during variable selection
When fitting statistical models to data where there are multiple variables we are often interested in adding or removing terms from our model and in cases where there are a large number of terms it can be quicker to use the update function to start with a formula from a model that we have already
Forsythe’s algorithm
In connection with the Bernoulli factory post of last week, Richard Brent arXived a short historical note recalling George Forsythe’s algorithm for simulating variables with density when (the extension to any upper bound is straightforward). The idea is to avoid computing the exponential function by simulating uniforms until since the probability of this event is 
Basket Option Pricing: Step by Step
%7D%20=%20S_t%5E%7B(0)%7D%20r_t%20dt%20%5C%5C%20dS_t%5E%7B(1)%7D%20=%20S_t%5E%7B(1)%7D%20(%5Cmu_t%5E%7B(1)%7D%20dt%20+%20%5Csigma_t%5E%7B(1)%7D%20dW_t%5E%7B(1)%7D)%20%5C%5C%20dS_t%5E%7B(2)%7D%20=%20S_t%5E%7B(2)%7D%20(%5Cmu_t%5E%7B(2)%7D%20dt%20+%20%5Csigma_t%5E%7B(2)%7D%20dW_t%5E%7B(2)%7D)%20%5C%5C%20%5Ctext%7Bwhere%20%7D%20%20dW_t%5E%7B(1)%7DdW_t%5E%7B(2)%7D=%5Crho%20dt&bg=ffffff&%23038;fg=000000&%23038;s=0)
I find options fascinating because they deal with the abstract ideas of volatility and correlation, both of which are unobservable and can often seem like wild animal spirits (take the current stock market as an example). Understanding these subtle concepts is never easy, but it is essential in pricing some of the more exotic
Initial Post
As a web application and database developer who has spent most of my time in the world Java, Ruby, Python and SQL, the R language has some unfamiliar functionality.This blog will be comprised of tips and tricks for making charts with the R language.
Connecting R and Python
There are a few ways to do this, but the only one that worked for me was to use Rserve and rconnect. In R, do this: 1 2 3 install.packages("Rserve") library(Rserve) Rserve(debug = FALSE, port=6311, args=NULL) Then you can connect in Python very easily. Here is a test in Python: 1 2 rcmd = pyRserve.rconnect(host='localhost', port=6311) print(rcmd('rnorm(100)'))
Revolution’s 2010 roadmap
As part of all the news from yesterday, we also announced our vision and roadmap for the Revolution R product line for 2010. You can see a short summary of our vision in this two-minute video, or see more details in the roadmap whitepaper available for download. But here's a quick overview of our plans: First, we intend to...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
