(Generalised) linear (mixed) models with R2jags: an extremely brief primer...

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

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

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, 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

R-lab: Inference with Spatially correlated data with nlme

Hi guys,

Problem: You have data that may be spatially correlated, and you worry this might affect your inference (p-values and confidence intervals for fixed effects). 

 Solution : This Friday, 31st October at 2pm, R-lab on inference with Spatially correlated data with the nlme package.

Download R code and data here.

See you there,
Gordana

September Eco-Stats lab: model-based multivariate analysis in ecology (mvabund package and recent additions)

What does mvabund do?
Analyses multivariate data (especially abundance of presence-absence data) using simultaneous univariate models and design-based inference.

The main functions are manyglm, which fits a GLM to each response variable, and anova/summary, which use row-resampling for valid multivariate inference (i.e. taking into account correlation between variables)

Designed specially for multivariate abundance data in ecology, species-by-site stuff, which has two key properties that need to be dealt with:
(1) strong mean-variance relationship.
(2) correlation between response variables (e.g. due to species interaction)

Why is mvabund better than using PRIMER, PC-ORD, etc?
A few reasons, see this R script for details (and code to work through):

R-lab on Mixed models in Ecology

Hi guys,

This Friday, 29th August at 2pm, there will be in an R-lab on mixed models in ecology, a topic which many of you are interested in digging deeper into. I've set up too links to dropbox for the slides as well as a csv file which we will be playing with (dataset courtesy of Sylvia Hay =D)

Thanks.

PDF slides
Example 1: Bird counts
Example 2: Nested design dataset

(Corrected) code for bird analysis

Yours non-significantly,
FH