Welcome to Loic and Wesley!

We are very fortunate to have two new research associates joining us in Eco-Stats - Loic Thibaut and Wesley Brooks.

Loic came from a PhD at James Cook with Sean Connelly, and he will be thinking about how to resample generalised linear mixed models.

Wesley came here from Wisconsin, where he did a PhD with Jun Zhu, and he will be looking at the issue of spatial confounding and how it affects point process models.

Time to event analysis (Survival analysis) in ecology

The August Eco-Stats Lab (Friday 28th, 2pm, Bioscience level 6) will be on time to event analysis in ecology.

When modelling the time taken for an event to happen (e.g.death) we often use time to event analysis (survival analysis) rather than regression models. A common feature of these data is that for some subjects the even did not happen at all during the study period, called (right) censoring, and survival analysis can elegantly incorporate the information from these subjects. Regression models on the other hand have no easy way to include these subjects.

Survival analysis can often be applied to ecological data, e.g.

- Arrival of a parasite

- Survival times for animals/plants 

- Germination timing

- Response to stimulus

- How long fruit remain on plants before they are eaten

We will use the survival package in R to analyse some ecological time to event data.  You can find the code/explanation here, and the data here.

Workshop - Introduction to Regression Modelling on R, December 6-7

We will be running a two-day workshop at UNSW in the lead-up to the Eco-Stats conference in December.  This course is aimed at ecologists who recall some intro stats and want to get up to speed on more modern methods of modelling data using R.

The core idea in the course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models - including linear regression, t-tests, ANOVA, ANCOVA, logistic regression and chi-square tests. Learning these methods in a systematic way, instead of as a "cookbook" of different methods, enables a systematic approach to key steps in analysis (like assumption checking) and extension to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models).

The course will be taught by the UNSW Eco-Stats group (it will be led by Francis Hui and Gordana Popovic, with contributions by David Warton and others).

Register via the conference website at http://www.eco-stats.unsw.edu.au/register.html

Forecasting with time series data

Day: Friday 31st July 2pm

Location: Bioscience Level 6

Topic: Forecasting with time series data

###########################################################################

## Forecasting with Times Series Data.

## We will use Rob Hyndman's forecast R-package.

## For more details on the package and time-series forecasting in general, 
## see https://www.otexts.org/fpp

library(forecast)

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...