Recent activity

'Model base multivariate analysis of abundance (presence/absence) data using R'

Oliver Hooker, December 1, 2016
 

'Model base multivariate analysis of abundance (presence/absence) data using R'

Delivered by Prof. David Warton, Melbourne University

http://www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-data-using-r-mbmv01/

This course will run from 16th – 20th January 2017 at Juniper Hall Field Station, Dorking, Surrey, just south of London, England.

OVERVIEW 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 sea bird spatial data.

INTENDED AUDIENCE 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@prstatistics.com or visit our website www.prstatistics.com

Please feel free to distribute this material anywhere you feel is suitable

Upcoming courses - email for details oliverhooker@prstatistics.com

  1. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January 2017) http://www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-data-using-r-mbmv01/

  2. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) http://www.prstatistics.com/course/advanced-python-biologists-apyb01/

  3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R (February 2017) http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/

  4. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/

  5. ADVANCES IN MULTIVAIRAITE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April 2017) http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp02/

  6. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017) http://www.prstatistics.com/course/introduction-to-statistics-and-r-for-biologists-irfb02/

  7. ADVANCING IN STATISTICAL MODELLING USING R (April 2017) http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr05/

  8. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (May 2017) http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/

  9. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017)

  10. GEOMETRIC MORPHOMETRICS USING R (June) http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/

  11. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) http://www.prstatistics.com/course/bioinformatics-for-geneticists-and-biologists-bigb02/

  12. INTRODUCTION TO METHODS FOR REMOTE SENSING (July 2017)

  13. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-spae05/

  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 (November 2017)

  21. ADVANCING IN STATISTICAL MODELLING USING R (December 2017)