This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between a single variable Y (the outcome, or dependent variable) measured on a continuous or near-continuous scale and one or more predictor (independent or explanatory variable), X. If 
It's been some time since my last post on football. And we're talking about european soccer here.So I finally managed to write some functions which allow me to extract player stats from www.transfermarkt.de. The site tracks lots of stats in the world o...
R’s formula interface is sweet but sometimes confusing. ANOVA is seldom sweet and almost always confusing. And random (a.k.a. mixed) versus fixed effects decisions seem to hurt peoples’ heads too. So, let’s dive into the intersection of these three. I’m aware that there are lots of packages for running ANOVA models that make things nicer
From: http://www.r-bloggers.com/data-driven-science-is-a-failure-of-imagination/I think I like this distinction between Bayesian and Frequentist statistics: "we are nearly always ultimately curious about the Bayesian probability of the hypothesis ...
I was having a conversation with an acquaintance about courses that were particularly useful in our work. My forestry degree involved completing 50 compulsory + 10 elective† courses; if I had to choose courses that were influential and/or really useful … Continue reading →
I wanted to thank those of you who have emailed and commented on my last post and its Link to Item Response Theory Presentations Using R. In the Psychometrics Centre website, it is not until Topic 7 called Unidimensional IRT models for binar...
The other night in my office I got into a discussion with my office mate, the brilliant scientist / amazing skier Dr. Thor Veen about how to understand the random effect variance term in a mixed-effects model. Thor teaches the R statistics course here at UBC, and last night a student came to the office...
Here is improved code for calculating QIC from geeglm in geepack in R (original post). Let me know how it works. I haven’t tested it much, but is seems that QIC may select overparameterized models. In the code below, I … Continue reading →
Today at Davis R Users’ Group, Rosemary Hartman took us through her work in progress fitting general additive models to organism presence/absence data. Below is her presentation and script. You can get the original script and data here
Also, check the comments below for some discussion of other options for this type of analysis, such as...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
