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

Sexy, unconstrained models all ready for you to play with!

When trying to visualize how sites vary in terms of species composition, for too long ecologists have been using distance-based methods of unconstrained ordination such as NMDS and CA, with little but precedence to guide them on what dissimilarity measure to use and what transformation and/or standardization to apply.

In collaboration with some folks in New Zealand and Finland, we've been working on a couple of model-based approaches to unconstrained ordination, which offer several advantages such as explicitly accounting for key properties of the data and model variable tools to select key aspects of the analysis. Simulations also show our proposed methods either perform the same or way better than distance-based approaches at the getting the ordinations correct!

Check out our manuscript, now available for early view at:

http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12236/abstract

Eco-Stats Lab June 2014: Phylogenetic model adequacy

In this lab (June 29th) I'll introduce a new package that we're developing called "arbutus" after a funny looking tree that grows in the Pacific Northwest.  The package is designed to test the adequacy of models of trait evolution on phylogenies.  We've started out looking at relatively simple models but it should be possible to test more and more complex models.

A few links:

1) the slides and code for the lab

2) the package repository

3) the pre-print of the paper

As this work is still in progress, any feedback on theory, usability or aesthetics is more than welcome!

Andrew Letten wins Best Ecology and Evolution talk at Postgraduate Review Forum

Andrew Letten, from the Centre for Ecosystems Science, honorary eco-stats member, and champion of bringing more statistics into his work in ecology, picked up the top talk in Ecology and Evolution at the UNSW BEES 2 day Postgrad Review Forum. He gave a cracker on species niche differentiation and trying to find empirical evidence for niche differentiation along soil moisture gradients. He showed the Biology department some cutting edge methods in ecological statistics like mvabund (see video) and joint species distribution modelling.
Great work Andrew!