After reading Blog About Stats' Open Data Index Blog Post I decided to browse a bit in the Open Data Index. Choosing Netherlands and following Emission of Pollutants I ended on a page from National Institute for Public Health. The page&n...

First of all this text is not just about an intuitive perspective on the beta distribution but at least as much about the idea of looking behind a measured empirical probability and thinking of it as a product of chance itself. … Continue reading →
The post An intuitive interpretation of the beta distribution appeared first on

Advanced R programming topics Similarly as last year, BNOSAC is offering the short course on 'Advanced R programming topics' at the Leuven Statistics Research Center (Belgium). The course is now part of FLAMES (Flanders Training Network for Methodology and Statistics) and can be found here http://www.flames-statistics.eu/training/advanced-r-programming-topics. Subscription is no longer possible unless you ask kindly to LStat. RApache and developing web applications with...

I am glad to have found the R package GGally. GGally is a convenient package built upon ggplot2 that contains templates for different plots to be combined into a plot matrix through the function ggpairs. It is a nice alternative to the more limited pairs function. The package has also functions to deal with parallel

In the last episode (which was quite some time ago) we looked into comparisons of means with linear models. This time, let’s visualise some linear models with ggplot2, and practice another useful R skill, namely how to simulate data from known models. While doing this, we’ll learn some more about the layered structure of a

In a recent post, I discussed the occurrence of hurricanes in the North Atlantic basin. The data comes from the National Oceanic and Atmospheric Association, a member of the US federal government. The data spans a bit more than 150 years. In that post, I make the observation that the data supports a model wherein

I am doing a second installment of the lunch seminars about data analysis with R for the members of the Wright lab. It’s pretty much the same material as before — data frames, linear models and some plots with ggplot2 — but I’ve sprinkled in some more exercises during the seminars. I’ve tried emphasising scripting a

e-mails with the latest R posts.

(You will not see this message again.)