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Model base multivariate analysis of abundance data using R (MBMV01)
16th January 2017 - 20th January 2017£540 - £650
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.
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).
We offer two packages
• COURSE ONLY – Includes lunch and refreshments.
• ALL INCLUSIVE – For an additional £235.00. Includes breakfast, lunch, dinner, refreshments, minibus to and from meeting point and accommodation. Accommodation is multiple occupancy (max 3 people) and shared single sex showers and toilets. Arrival Sunday 15th January and departure Friday 20th January PM.
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Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PR~statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PR~statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
A mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of introductory statistics and univariate regression analysis.
Assumed computer background
Previous experience with data analysis using R is required.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
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Meet at Juniper Hall at approximately 18:30 (Download directions PDF).
Monday 16th – Classes from 09:00 to 17:00
Module 1: Revision of (univariate) regression analysis.
Revision of key “Stat 101” messages, the linear model, generalised linear model and linear mixed model.
Main packages: lme4.
Tuesday 17th – Classes from 09:00 to 17:00
Module 2: Computer-intensive inference and multiple responses.
The parametric bootstrap, permutation tests and the bootstrap, model selection, classical multivariate analysis, allometric line fitting.
Main packages: lme4, mvabund, glmnet, smatr.
Wednesday 18th – Classes from 09:00 to 17:00
Module 3: Multivariate abundance data
Key properties, hypothesis testing, indicator species, compositional analysis, non-standard models.
Main packages: mvabund.
Thursday 19th – Classes from 09:00 to 17:00
Module 4: Explaining cross-species patterns
Classifying species based on environmental response, species traits as predictors, studying species interactions.
Main packages: Speciesmix, mvabund, lme4.
Friday 20th – Classes from 09:00 to 16:00
Module 5: Model-based ordination and inference
Latent variable models for ordination, model-based inference for fourth corner models.
Main packages: boral, mvabund.