Recology has moved, go to http://recology.info/2011/11/my-talk-on-doing-phylogenetics-in-r
I gave a talk today on doing very basic phylogenetics in R, including getting sequence data, aligning sequence data, plotting trees, doing trait evolution stuff, etc.
Please comment if you have code for doing bayesian phylogenetic inference in R. I know phyloch has function mrbayes, but can't get it to work...
Showing posts with label Picante. Show all posts
Showing posts with label Picante. Show all posts
Friday, November 18, 2011
Thursday, October 13, 2011
Phylogenetic community structure: PGLMMs
Recology has moved, go to http://recology.info/2011/10/phylogenetic-community-structure-pglmms
So, I've blogged about this topic before, way back on 5 Jan this year.
Matt Helmus, a postdoc in the Wootton lab at the University of Chicago, published a paper with Anthony Ives in Ecological Monographs this year (abstract here). The paper addressed a new statistical approach to phylogenetic community structure.
As I said in the original post, part of the power of the PGLMM (phylogenetic generalized linear mixed models) approach is that you don't have to conduct quite so many separate statistical tests as with the previous null model/randomization approach.
Their original code was written in Matlab. Here I provide the R code that Matt has so graciously shared with me. There are four functions and a fifth file has an example use case. The example and output are shown below.
Look for the inclusion of Matt's PGLMM to the picante R package in the future.
Here are links to the files as GitHub gists:
PGLMM.data.R: https://gist.github.com/1278205
PGLMM.fit.R: https://gist.github.com/1284284
PGLMM.reml.R: https://gist.github.com/1284287
PGLMM.sim.R: https://gist.github.com/1284288
PGLMM_example.R: https://gist.github.com/1284442
Enjoy!
The example
..and the figures...
So, I've blogged about this topic before, way back on 5 Jan this year.
Matt Helmus, a postdoc in the Wootton lab at the University of Chicago, published a paper with Anthony Ives in Ecological Monographs this year (abstract here). The paper addressed a new statistical approach to phylogenetic community structure.
As I said in the original post, part of the power of the PGLMM (phylogenetic generalized linear mixed models) approach is that you don't have to conduct quite so many separate statistical tests as with the previous null model/randomization approach.
Their original code was written in Matlab. Here I provide the R code that Matt has so graciously shared with me. There are four functions and a fifth file has an example use case. The example and output are shown below.
Look for the inclusion of Matt's PGLMM to the picante R package in the future.
Here are links to the files as GitHub gists:
PGLMM.data.R: https://gist.github.com/1278205
PGLMM.fit.R: https://gist.github.com/1284284
PGLMM.reml.R: https://gist.github.com/1284287
PGLMM.sim.R: https://gist.github.com/1284288
PGLMM_example.R: https://gist.github.com/1284442
Enjoy!
The example
..and the figures...
Wednesday, January 5, 2011
New approach to analysis of phylogenetic community structure
Anthony Ives, of University of Wisconsin-Madison, and Matthew Helmus of the Xishuangbanna Tropical Botanical Garden, present a new statistical method for analyzing phylogenetic community structure in an early view paper in Ecological Monographs. See the abstract here.
Up to now, most phylogenetic community structure papers have calculated metrics and used randomization tests to determine if observed metrics are different from random. The approach of Ives and Helmus fits models to observed data, instead of calculating single metrics.
Furthermore, their approach gets around the limitation in studies of phylogenetic community structure of conducting many separate statistical tests, thereby inflating your chances of finding a significant effect purely by chance.
Their approach uses generalized linear mixed models (GLMMs). They provide Matlab code for running these models, but R code will be available in the Picante package in the future.
Up to now, most phylogenetic community structure papers have calculated metrics and used randomization tests to determine if observed metrics are different from random. The approach of Ives and Helmus fits models to observed data, instead of calculating single metrics.
Furthermore, their approach gets around the limitation in studies of phylogenetic community structure of conducting many separate statistical tests, thereby inflating your chances of finding a significant effect purely by chance.
Their approach uses generalized linear mixed models (GLMMs). They provide Matlab code for running these models, but R code will be available in the Picante package in the future.
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