Recently I've been doing a lot of work with predictive models using logistic regression. Logistic regression is great for determing probable outcomes of a independent binary target variable. R is a great tool for accomplishing this task.&nb...
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Webinar: Revolution R is 100% R and More
I'll be hosting a webinar tomorrow (Wednesday) aimed at R users who want to know more about how Revolution R Enterprise extends open source R for big data, Web services, multi-core processing, debugging and more. For R users at schools and universities, I'll also explain how you can download and use Revolution R Enterprise free of charge. The full...
Webinar: Revolution R is 100% R and More
I'll be hosting a webinar tomorrow (Wednesday) aimed at R users who want to know more about how Revolution R Enterprise extends open source R for big data, Web services, multi-core processing, debugging and more. For R users at schools and universities, I'll also explain how you can download and use Revolution R Enterprise free of charge. The full...
Using the "Divide by 4 Rule" to Interpret Logistic Regression Coefficients
I was recently reading a bit about logistic regression in a book on hierarchical/multilevel modeling when I first learned about the "divide by 4 rule" for quickly interpreting coefficients in a logistic regression model in terms of the predicted probabilities of the outcome. The idea is pretty simple. The logistic curve (predicted probabilities) is steepest at the center where...
Example 8.17: Logistic regression via MCMC
In examples 8.15 and 8.16 we considered Firth logistic regression and exact logistic regression as ways around the problem of separation, often encountered in logistic regression. (Re-cap: Separation happens when all the observations in a category sha...
Forecasting workshop: Switzerland, June 2011
I will be running a workshop on Statistical Forecasting: Principles and Practice in Switzerland, 20-22 June 2011. Check out the venue: Waldhotel Doldenhorn, Kandersteg! So if you fancy a trip to the beautiful Swiss Alps next June, read on… Outline Forecasting is required in many situations: deciding whether to build another power generation plant in
Another boring blog
I recently decided to create two blogs as outlets for my research. The first (The Richness of Life) focuses more on the organisms I work with as an ecologist and my general interest as a student of natural history. This blog on Quantitative...
Random variable generation (Pt 2 of 3)
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Acceptance-rejection methods This post is based on chapter 1.4 of Advanced Markov Chain Monte Carlo. Another method of generating random variates from distributions is to use acceptance-rejection methods. Basically to generate a random number from , we generate a RN from an envelope distribution , where . The acceptance-rejection algorithm is as follows: Repeat until 
Top 10 things that suck about Sweave
People rave about Sweave and the literate programing paradigm and I am guilty as charged. I speak Sweave, I think Sweave, I dream Sweave. As a matter of fact my default mode of operation is Sweave and anything else is an … Continue reading →
Controlling Amazon Web Services using rJava and the AWS Java SDK
I’ve been messing around with using Amazon Web Services for a while. I’ve had some projects where I wanted to upload files to S3 or fire off EMR jobs. I’ve been controlling AWS services using a hodgepodge of command line tools and the R system() function to call the tools from the command line.
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
