I’ve been hacking away at this post for a while now, for a few reasons. First, I’m a git novice, so I’m still trying to learn my way around the software. Second, this is an intimidating topic for those who are not used to things like the command line, so it was a challenge to identify which ideas were critical to cover, and which could be ignored without too much of a loss in functionality. Finally, there are always lots of little kinks to work out, especially in a software that is cross-platform. Therefore, please take the following with a grain of salt and let me know if anything is unclear, needs work, or is flat out wrong!
I wanted to share a post by my friend Sharon Baruch-Mordo at my other blog, BioDV, on communicating biodiversity science. Sharon is a thoughtful, insightful scientist and I think she makes some really awesome points.
In her article, she challenges readers to, “write a 500 word essay about your science for a popular media outlet.” Never one to back down from a challenge, I thought I would give it a shot. My effort is below (499 words!)…what do you think Sharon?
I wanted to quickly highlight an article by Cameron Walker titled “Collaboration: A problem shared” that appeared in Nature Jobs this week. It highlights some of my research done as part of the Dimensions of Biodiversity Distributed Graduate Seminar. I’ve wrote about collaboration before, specifically this program, so I was happy to see it get the press it deserves! Moving forward, I think educational models such as this one will be critical in preparing young ecologists to answer relevant questions in ecology–so check it out!
Lately I’ve been running a lot of complex models with huge datasets, which is grinding my computer to a halt for hours. Streamlining code can only go so far, but R is limited because the default session runs on only 1 core. In a time when computers have at least 2 cores, if not more, why not take advantage of that extra computing power? (Heck, even my phone has 2 cores.*)
Luckily, R comes bundled with the “parallel” package, which helps to distribute the workload across multiple cores. It’s a cinch to set up on a local machine:
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.
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.
[Updated October 13, 2015: Development of the R function has moved to my piecewiseSEM package, which can be found here under the function