In case of continuously collected data, e.g. observations from a monitoring network, spatial interpolation of this data cannot be done manually. Instead, the interpolation should be done automatically. To achieve this goal, I developed the automap package. automap builds on… See more ›
A friend recently asked whether I trusted the inferences from heteroskedastic probit models. I said no, because the heteroskedastic probit does not allow a researcher to distinguish between non-constant variance and a mis-specified mean function. In particular, my friend had a hypothesis that the variance of the latent outcome (commonly called "y-star") should increase with an
In last week’s post I used R heatmaps to visualise the performance of NFL Quarterbacks in 2012. This was done in a 2 step process, Clustering QB performance based on the 12 performance metrics using hierarchical clustering Plotting the performance … Continue reading →
This month has seen the release of the 2011 census data for England and Wales at Output Area Level. This offers the possibility to map various attributes about people and places for very small geographic areas. Output Areas represent the most detailed geography for which Census data are released and are the building blocks for
Quick reminder: The next Cologne R user group meeting is scheduled for tomorrow, 6 February 2013. All details and the agenda are available on the KölnRUG Meetup site. Please sign up if you would like to come along. Notes from the last Cologne R user group meeting are available here.
Thanks also to...
With only 24 hours remaining in the 2012 NFL season, this is a good time to review how the league's QBs performed during the regular season using performance data from KFFL and the heat mapping capabilities of R. #scale data … Continue reading →
R script# Listenning to NPR about gun deaths in the US got me thinking.# Find the article here:# http://www.slate.com/articles/news_and_politics/crime/2012/12/gun_death_tally_every_american_gun_death_since_newtown_sandy_hook_shooting.html# Let's create...
This is the third article of the Maps in R series. After having shown how to draw a map without placing data on it and how to plot point data on a map, in this installment the creation of a … Continue reading →
The dataset used in Bayesian Core for the chapter on image processing is a Landsat picture of Lake of Menteith in Scotland (close to Loch Lomond). (Yes, Lake of Menteith, not Loch Menteith!) Here is the image produced in the book. I just got an email from Matt Moores at QUT that the image is 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
