OK, so, the title of this article is actually Do not log-transform count data, but, as @ascidacea mentioned, you just can’t resist adding the “bitches” to the end. Onwards. If you’re like me, when you learned experimental stats, you were taught to worship at the throne of the Normal Distribution. Always check your data and
This is tremendously cool. A nice intuitive web-based interface for the lme4 package in R (and you neither need to know R or understand the intricacies of the lme4 package) that gives you pdf output and plots. If you just want to play around and not worry about coding things up, it’s a great little
I’m back for another pean to ANOVA-loving experimental ecology. Barplots (or point-range plots – but which is better is a discussion for a different time) are the thing! And a friend of mine was recently asking me how to do a decent barplot with ggplot2 for data of moderate complexity (e.g., a 2-way ANOVA). So
OK, so, I know I already raved about one Hadley Wickham project and how it has changed my life last week. But what can I say, the man is a genius. And if you are using R (and let’s face it, you should be) and you want simple sexy graphs made quick, the man has
I haven’t posted anything about R-nerdery in quite some time. But I have to pause for a moment, and sing the praises of a relatively new package that has made my life exponentially easier. The plyr package. R has the capability to apply a single function to a vector or list using apply or mapply,
I was completely tickled last year with the oh so amusing Statz Rappers. It kept me and my nerdy stats friends laughing for days. Rapping. Stats. The Internet. Good times. But little did I know that rapping about statistics was really just hitting its stride on youtube. This is Why We Plot began my trip
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
