Lately, I’ve been using loops to fit a number of different models and storing the models (or their predictions) in a list (or matrix)–for instance, when bootstrapping. The problem I was running into was the for loop screeching to a halt as soon as a model kicked back an error. I wanted the function to register an error for that entry, then skip to the next one and finish off the loop.
Yesterday, BioScience released a pre-print version of a manuscript by Emmett Duffy and others entitled “Envisioning a Marine Biodiversity Observation Network”. As Emmett is my immediate supervisor (disclaimer!), I was asked to disseminate widely, and I figured why not post it on my blog, and provide a few comments along the way!
(This is the first installment of what will hopefully become a regular series. We run a small journal club in the Department of Biological Sciences at VIMS and thought we would summarize our weekly discussion and post it on the internet in the hopes that others will provide some additional insight.)
This week, a paper titled “Avatars of information: towards an inclusive evolutionary synthesis” by Étienne Danchin appeared in Trends in Ecology & Evolution. The aim of this paper was to present a unifying framework under which to study evolution that includes both genetic and non-genetic modes of inheritance. We’re all familiar with the concept of genetic inheritance, where the expression of certain phenotypes are directly linked to the presence and activation of a particular gene: Mendel and his peas, spots on guinea pigs, cystic fibrosis, and so on. Many fewer of us are familiar with the concept of non-genetic inheritance, which invokes an almost Lamarckian idea of evolution.
Just got back from the 42nd Annual Benthic Ecology Meeting in Savannah, GA. Can’t say it was very warm for springtime in the Old South (and we never did find any good ribs) but the company was excellent, as always, and the talks fascinating. I thought I’d write up a few brief highlights (for me, anyways):
Linear mixed effects models are a powerful technique for the analysis of ecological data, especially in the presence of nested or hierarchical variables. But unlike their purely fixed-effects cousins, they lack an obvious criterion to assess model fit.
I’ve long extolled the virtues of using ggplot2 as a graphing tool for R for its versatility and huge feature set. One of my favorite aspects of ggplot2 is the ability to tweak every aspect of the plot using intuitive commands. With the recent release of version 0.9.2 though, creator Hadley Wickham overhauled the theme options, which broke my preferred black theme, theme_black(), found here. I’ve updated theme_black() to work with the current version of ggplot 0.9.3.1. Enjoy!
The other day, I posted an introductory demo to mapping in R using some of the built-in maps. But of course there are only a few regions represented in the “maps” package: the US states, the US as a whole, Italy, France, and the world. Even then, there are some limitations to these: for instance, the USSR is still alive and kicking in the world of “maps.” If you are living in the 21st century–or working somewhere other than these locations–you may want to supply your own, more updated maps. The most popular filetype is, of course, the GIS shapefile. But to access, visualize, manipulate, and plot on shapefiles, it was formerly necessary to use ArcGIS, which is proprietary and thus costly. I’ll show you how to do it all in R!