This post is minimalistic. Consider this: Now let's have look at what's inside x: But is it really true? Here you go. A colleague of mine was once ruined by this for an entire day before we realized what was…
Blog Archives
AIC & BIC vs. Crossvalidation
Model selection is a process of seeking the model in a set of candidate models that gives the best balance between model fit and complexity (Burnham & Anderson 2002). I have always used AIC for that. But you can also…
Session 1: Gridding data for multi-scale macroecological analyses
These are materials for the first practical lesson of the Spatial Scale in Ecology course. All of the data and codes are available here. The class covered a 1.5h session. R code for the session is also at the end … Continue reading →
Not all proportion data are binomial outcomes
It really is trivial. Not every proportion is frequency. There are things that have values bounded between 0 and 1 and yet they are neither probabilities, nor frequencies. Why do I even bother to write this? Because some kinds of … Continue reading →
Predictors, responses and residuals: What really needs to be normally distributed?
Introduction Many scientists are concerned about normality or non-normality of variables in statistical analyses. The following and similar sentiments are often expressed, published or taught: "If you want to do statistics, then everything needs to be normally distributed." "We normalized … Continue reading →
Data-driven science is a failure of imagination
Professor Hans Rosling certainly is a remarkable figure. I recommend watching his performances. Especially the BBC's "Joy of Stats" is exemplary. Rosling sells passion for data, visual clarity and great deal of comedy. He represents the data-driven paradigm in science. What … Continue reading →
Fast Conway’s game of life in R
Here I demonstrate a simple way to code Conway's game of life (GoL) in R and to produce the animation above. Cellular automata in R are usually painfully slow if you iterate through all grid cells in an array. A … Continue reading →
The simplest Species Distribution Model in OpenBUGS & R
This post demonstrates the simplest Species Distribution Model based on logistic regression for presence/absence data. I heavily simplified the example from Kéry (2010): Introduction to WinBUGS for Ecologists, Chapter 20.
Using R for parallelizing OpenBUGS on a single Windows PC
It seems that most of the R-parallelizing business takes place on Linux clusters. And it makes sense. Why would you want to paralellize R on just a few processors (2 or 4) of a Windows laptop PC when the whole … Continue reading →
Linear regression in OpenBUGS
I always wondered why is it so difficult to find an OpenBUGS example of simple linear regression on the Web. Curiously, such example is even missing in the OpenBUGS help. The only nice example so far is in the book … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
