ANOVA, model selection, and pairwise contrasts among treatments using R.

Some time ago I wrote about how to fit a linear model and interpret its summary table in R. At the time I used an example in which the response variable depended on two explanatory variables and on their interaction. It was a rather specific article, in which I overlooked some essential steps in the process of selection and interpretation of a statistical model. In the present article and in the relative worked examples I will expand the topic a bit, explaining 1) how to select the most parsimonious model relatively to a dataset using function anova() and 2) how to use summary(), together with relevel(), for testing for significant differences between pairs of experimental treatments (R script here). Continue reading

About p-values

I stepped into a blog post by Pia Parolin titled “Do all biological processes need to be statistically significant?” ( ). It sounds, at moments, the frustrated cry of the field biologist observing cool patterns and building cool theories on it until he/she faces that bloody p-value=0.051. Who hasn’t been there? Yet Pia’s article contains more than that, and it raises interesting issues (also see the article’s comments). Here are some notes of mine. Continue reading

Spatial and movement analyses in R: links

I am not a spatial ecologist, but I learned a few things by overhearing friends and colleagues talking, and by helping some students fixing their R codes. Here is a list of resources i came in touch with for creating and modifying maps, handling spatial coordinates, analyzing movement data, and producing spatial distribution models in R: Continue reading

Multivariate analyses in R: links

One of my PhD projects is about studying the effects of temperature on protist diversity in a natural environment; to this aim I had to learn about diversity metrics, ordinations, Mantel tests, MANOVA, etc… Here is a collection of links I found useful to navigate the jungle of multivariate statistics: Continue reading