oliverhooker at prstatistics.com
Wed Nov 30 04:26:21 PST 2016
'Model base multivariate analysis of abundance (presence/absence) data
Delivered by Prof. David Warton, Melbourne University
This course will run from 16th – 20th January 2017 at Juniper Hall
Field Station, Dorking, Surrey, just south of London, England.
This course will provide an introduction to modern multivariate
techniques, with a special focus on the analysis of abundance or
presence/absence data. Multivariate analysis in ecology has been
changing rapidly in recent years, with a focus now on formulating a
statistical model to capture key properties of the observed data, rather
than transformation of data using a dissimilarity-based framework.
In recent years, model-based techniques have been developed for
hypothesis testing, identifying indicator species, ordination,
clustering, predictive modelling, and use of species traits as
predictors to explain interspecific variation in environmental response.
These techniques are more interpretable than alternatives, have better
statistical properties, and can be used to address new problems, such as
the prediction of a species’ spatial distribution from its traits
alone making this course suitable for those researching marine mammal
PhD students, research postgraduates, and practicing academics as well
as persons in industry working with multivariate data, especially when
recorded as presence/absences or some measure of abundance (counts,
biomass, % cover, etc).
Course content is as follows
Day 1: Revision of (univariate) regression analysis
o Revision of key “Stat 101” messages, the linear model, generalised
linear model and linear mixed model.
o Main packages: lme4.
Day 2: Computer-intensive inference and multiple responses
o The parametric bootstrap, permutation tests and the bootstrap, model
selection, classical multivariate analysis, allometric line fitting.
o Main packages: lme4, mvabund, glmnet, smatr.
Day 3: Multivariate abundance data
o Key properties, hypothesis testing, indicator species, compositional
analysis, non-standard models.
o Main packages: mvabund.
Day 4: Explaining cross-species patterns
o Classifying species based on environmental response, species traits as
predictors, studying species interactions.
o Main packages: Speciesmix, mvabund, lme4.
Day 5: Model-based ordination and inference
o Latent variable models for ordination, model-based inference for
fourth corner models.
o Main packages: boral, mvabund.
Please email any inquiries to oliverhooker at prstatistics.com or visit our
Please feel free to distribute this material anywhere you feel is
Upcoming courses - email for details oliverhooker at prstatistics.com
1. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January
2. ADVANCED PYTHON FOR BIOLOGISTS (February 2017)
3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R
4. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017)
5. ADVANCES IN MULTIVAIRAITE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April
6. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017)
7. ADVANCING IN STATISTICAL MODELLING USING R (April 2017)
8. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (May 2017)
9. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017)
10. GEOMETRIC MORPHOMETRICS USING R (June)
11. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017)
12. INTRODUCTION TO METHODS FOR REMOTE SENSING (July 2017)
13. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017)
14. ECOLOGICAL NICHE MODELLING (October 2017)
15. GENETIC DATA ANALYSIS USING R (October TBC)
16. INTRODUCTION TO PYTHON FOR BIOLOGISTS (October TBC)
17. INTRODUCTION TO BIOINFORMATICS USING LINUX (October TBC)
18. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November TBC)
19. PHYLOGENETIC DATA ANALYSIS USING R (November TBC)
20. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS
21. ADVANCING IN STATISTICAL MODELLING USING R (December 2017)
Oliver Hooker PhD.
128 Brunswick Street
+44 (0) 7966500340
More information about the MARMAM