It's boral time!

Details for the next R-lab are now available:

Day: Friday 19th June 2pm (it's been pushed a week early for conference reasons)

Location: Bioscience Level 6

Topic: boral -- A R package for bayesian analysis of multivariate abundance data in ecology

Links: 

1) Presentation slides Go here

2) R script: Go here

Notes: Please be aware that we will be using the MCMC package JAGS, as well as the R package boral and mvabund. If you are using your own laptop, then you could save some time by installing those prior to coming. Thanks!

FH

Missing Data Analysis

The May Eco-Stats Lab (Friday 29th, 2pm, Bioscience level 6) will be on the missing data analysis, using the method of Multiple Imputation.

One often encounters missing data in almost all types of studies. Ecological data is also commonly subject to missing data. However, most of the statistical analysis methods are designed for complete datasets. A common way to handle missing data is to remove cases with missing values in order to obtain a complete dataset, which reduces the sample size and thus the statistical power. This approach can result in biased estimates for descriptive statistics and regression coefficients as well. An alternative approach is to impute (fill-in) the missing data by plausible values multiple times, analyse each imputed dataset separately, and then combine the results together. This method is called Multiple Imputation (MI) and was proposed by Rubin (1987).

In this lab we will explore the method of MI implemented in the mice package (van Buuren & Groothuis-Oudshoorn, 2011), which stands for multivariate imputation by chained equations. For more details see

http://www.jstatsoft.org/v45/i03/

Click here

Traits, community ecology and demented accountants

I've added a post on trait modelling on the Methods blog, title as above, to coincide with the April MEE Special Issue from the 2013 Eco-Stats Symposium.

You'd be surprised how hard it is to find a Creative Commons image of people in suits in the field...

Zero inflation in ecology

The April Eco-Stats Lab (Friday 24th, 2pm, Bioscience level 6) will be on zero inflated data in ecology. 

It's very common for ecological data to contain many zeros. To account for this we may need to:

1. Use zero inflated regression models

2. Do absolutely nothing (i.e. fit standard glm's)

In this lab we'll talk about why many zeros may occur in ecology, and the appropriate ways to account for them in your analysis.We will mostly use the pscl package in R.

Special Issue from Eco-Stats Symposium in MEE April 2015 issue

The Eco-Stats Symposium led to a series of papers which make up the April Special Issue of Methods in Ecology and Evolution.  Read the blog post on the topic for a little more info.  This includes contributions to species distribution modelling (point process models, measurement error, jointly estimating observer bias across multiple spp), multivariate analysis (a method for unconstrained ordination, trait analysis), diversity estimation (rarefaction for phylogenetic diversity, how to weight branches in functional diversity computation).

Planning is well underway for a follow-up Eco-Stats Conference in December 2015 at UNSW, featuring Otso Ovaskainen (Helsinki), Doug Wu (East Anglia), Jay ver Hoef (Alaska), Melodie McGeoch (Monash) and plenty more.  We'll send out a call for registrations and poster abstracts in the next month or so.

Species traits in multivariate analysis

The March Eco-Stats Lab (Friday 27th, 2pm, Bioscience level 6) will be on incorporating species traits into multivariate analysis (or equivalently, community level models), using some functions recently added to the mvabund package.

Often ecologists collect data (especially abundance or presence-absence data) simultaneously across many taxa, with the intention of studying what occurs where (and why). This tutorial focusses on the why - methods to help us move towards a functional explanation of community abundances. McGill et al (2006) and Shipley (2010) argue passionately for the need for this.

A common strategy in any field looking at "why" is to look for predictor variables that can explain the response. In the case of studying why some taxa are abundant at a site while others are not, the relevant predictors are species traits. These come in a matrix, different traits in different columns, different taxa in different rows.

We will explore methods for using such a matrix of traits in a multivariate analysis, using the mvabund package (version 3.10.1 or later).  Full details here:
             http://rpubs.com/dwarton/68823
We will also discuss the relationship of these methods with standard analysis of multivariate abundance data, SDMs, and Bill Shipley's CATS models.

*** Note you need mvabund 3.10.1 or later... ***

UPDATE (13/5/15): mvabund 3.10.4 is now available from CRAN.  It has a formula argument for more control of the traitglm model you fit, and composition and col.intercepts arguments to control whether or not you include row/column effects in the model (to focus on relative abundance), and a block resampling option on anova.traitglm (useful for example if you have repeated measures).

Congratulations Francis!

In the last few weeks Francis Hui has pulled off two big results:
- his PhD has been awarded, with his thesis going through without changes, but with plenty of complements.
- he was awarded the 2015 E&ERC prize for best student paper for the below article in the Journal of the American Statistical Association:
http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2014.951444

Well done Francis!  Super effort.  He is currently working on a post-doc at ANU with Alan Welsh and Samuel Mueller, developing new methodology for mixed models.