'Model base multivariate analysis of abundance (presence/absence) data using R'
Delivered by Prof. David Warton, Melbourne University http://prstatistics.com/course/model-based-multivariate-analysis-of- abundance-data-using-r-mbmv/ 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. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (August) 2. INTRODUCTION TO PYTHON FOR BIOLOGISTS (October) 3. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (October) 4. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (October) 5. PHYLOGENETIC DATA ANALYSIS USING R (October/November) 6. SPATIALANALYSIS OF ECOLOGICAL DATA USING R (November) 7. ADVANCING IN STATISTICAL MODELLING USING R (December) 8. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March) 9. INTRODUCTION TO GEOMETRIC MORPHOMETRICS USING R (June) Dates still to be confirmed - email for details [email protected] • STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R • INTRODUCTION TO R AND STATISTICS FOR BIOLOGISTS • BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS Oliver Hooker PR statistics 3/1 128 Brunswick Street Glasgow G1 1TF +44 (0) 7966500340 www.prstatistics.com www.prstatistics.com/organiser/oliver-hooker/Oliver Hooker
