'Model base multivariate analysis of abundance (presence/absence) data using R'
3 Places left! 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. 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 [email protected] or visit our website www.prstatistics.com Please feel free to distribute this material anywhere you feel is suitable Upcoming courses - email for details [email protected] 1. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January 2017) #MBMV http://www.prstatistics.com/course/model-base-multivariate-analysis-of- abundance-data-using-r-mbmv01/ 2. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) #APYB http://www.prstatistics.com/course/advanced-python-biologists-apyb01/ 3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R (February 2017) #SIMM http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r- simm03/ 4. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) #NTWA http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/ 5. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April 2017) #MVSP 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) #IRFB http://www.prstatistics.com/course/introduction-to-statistics-and-r-for- biologists-irfb02/ 7. ADVANCING IN STATISTICAL MODELLING USING R (April 2017) #ADVR http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr05/ 8. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (May 2017) #IBHM http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical- modelling-using-r-ibhm02/ 9. GEOMETRIC MORPHOMETRICS USING R (June 2017) #GMMR http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/ 10. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017) #MASE http://www.prstatistics.com/course/multivariate-analysis-of-spatial- ecological-data-using-r-mase01/ 11. TIME SERIES MODELS FOR ECOLOGISTS USING R (JUNE 2017 (#TSME) 12. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) #BIGB http://www.prstatistics.com/course/bioinformatics-for-geneticists-and- biologists-bigb02/ 13. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAE http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r- spae05/ 14. ECOLOGICAL NICHE MODELLING (October 2017) #ENMR http://www.prstatistics.com/course/ecological-niche-modelling-using-r- enmr01/ 15. INTRODUCTION TO BIOINFORMATICS USING LINUX (October 2017) #IBUL 16. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (November 2017) #ABME http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists- epidemiologists-abme03/ 17. INTRODUCTION TO METHODS FOR REMOTE SENSING (November 2017) #IRMS 18. INTRODUCTION TO PYTHON FOR BIOLOGISTS (November 2017) #IPYB 19. DATA VISUALISATION AND MANIPULATION USING PYTHON (December 2017) #DVMP 20. ADVANCING IN STATISTICAL MODELLING USING R (December 2017) #ADVR 21. GENETIC DATA ANALYSIS USING R (October TBC) 22. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November TBC) 23. PHYLOGENETIC DATA ANALYSIS USING R (November TBC) 24. STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS (TBC)
