The purpose of this lab (Friday 28th 2pm) is to provide a very basic primer on running basic lm, glm and glmms in a Bayesian framework using the R2jags package (and of course JAGS). As such, the goal is not to debate the relative merits of Bayesian vs frequentist approaches, but hopefully to demystify the fitting of Bayesian models, and more specifically demonstrate that in a wide variety of (more basic) use cases the parameter estimates obtained from the two approaches are typically very similar.
We will be attempting to reproduce a small element of the analysis from a recently published article in Journal of Ecology (for which all the data is available at datadryad.org).
Kessler, M., Salazar, L., Homeier, J., Kluge, J. (2014), Species richness-productivity relationships of tropical terrestrial ferns at regional and local scales. Journal of Ecology, 102: 1623-1633.
Code (jagstut.Rmd) and data (KessDiv.csv) available from dropbox or github. View the compiled html version here.
See you Friday,
Andrew
Tuesday 25 November 2014
Friday 7 November 2014
Eco-Stats ARC Discovery grant - $295,900 for 2015-17
The ARC Discovery grant results were released yesterday - this is the main place in Australia where you can get funding for fundmanetal research, although it is super-competitive. I was lucky enough to get a grant up - "Advances in biodiversity modelling - analysis of high-dimensional counts", $295,900 over three years. This funding will be used to hire a post-doctoral researcher to help improve methods of multi-species modelling, thinking about questions like how to model species interaction in a parsimonious way, and accounting for measurement error in covariates. Job ad coming soon...
Nerd Nite Sydney
So I was asked to speak at Nerd Nite Sydney last night - they describe it as "a bit like the Discovery Channel... with beer" but it was heaps more fun than that.
I did the Rick Astley thing... yeah, again... and talked a little about exciting times for statistics (new technology etc) and the hard times (low levels of statistical literacy are often a barrier to progress/informed discussion)
You can find out more about Nerd Nite Sydney at http://sydney.nerdnite.com/ or on social media
I did the Rick Astley thing... yeah, again... and talked a little about exciting times for statistics (new technology etc) and the hard times (low levels of statistical literacy are often a barrier to progress/informed discussion)
You can find out more about Nerd Nite Sydney at http://sydney.nerdnite.com/ or on social media
Great STATS talk @ #nerdnightsyd @ecostats
I think I need to talk stats with you - HELP appreciated pic.twitter.com/wkm0DfHgCe
— John Martin (@Cockatoowingtag) November 6, 2014Sunday 2 November 2014
boral: Reliable construction materials for good model building...
The ecostats group are happy to introduce a new R package called boral -- Bayesian Ordination and Regression AnaLysis, for analysis of multivariate data (community composition data especially) in ecology!!!
Boral uses Bayesian MCMC estimation via JAGS (Just Another Gibbs Sampler) to fit three types of models:
1) GLMs fitted independently to each species (like in another R package mvabund, developed by us)
2) Purely latent variable models for model-based unconstrained ordination (see Hui et al., 2014, http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12236/abstract for details)
3) GLMs fitted to each species while accounting for correlation between species e.g., due to species interaction.
Check it out at: http://cran.r-project.org/web/packages/boral/index.html
And even better, check out the video (which we promise has no dancing and no singing unlike another certain video...): https://www.youtube.com/watch?v=vyMsgyytcUI
Boral uses Bayesian MCMC estimation via JAGS (Just Another Gibbs Sampler) to fit three types of models:
1) GLMs fitted independently to each species (like in another R package mvabund, developed by us)
2) Purely latent variable models for model-based unconstrained ordination (see Hui et al., 2014, http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12236/abstract for details)
3) GLMs fitted to each species while accounting for correlation between species e.g., due to species interaction.
Check it out at: http://cran.r-project.org/web/packages/boral/index.html
And even better, check out the video (which we promise has no dancing and no singing unlike another certain video...): https://www.youtube.com/watch?v=vyMsgyytcUI
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