Model-Based Multivariate Analysis Of Abundance Data Using R (MBMVPR)
1st January 2030
£350.00
Course Format
Pre Recorded
About This Course
Intended Audiences
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 Details
Last Up-Dated – 12:02:2021
Duration  – Approx. 30 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
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
An understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Assumed computer background
Previous experience with data analysis using R is required. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
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COURSE PROGRAMME
Day 1 – approx. 3 hours
Revision of key “Stat 101” messages.
Day 2 – approx. 3 hours
Revision of (univariate) regression analysis: the linear model, generalised linear model.
Main packages: lme4.
Day 3 – approx. 3 hours
Linear mixed models, the parametric bootstrap, permutation tests and the bootstrap.
Main packages: lme4, mvabund.
Day 4 – approx. 3 hours
Model selection, classical multivariate analysis.
Main packages: glmnet.
Day 5 – approx. 3 hours
Multivariate abundance data: hierarchical models, key properties, hypothesis testing.
Main packages: mvabund.
Day 6 – approx. 3 hours
Multivariate abundance data: design-based inference for dependent data, indicator species.
Main packages: mvabund.
Day 7 – approx. 3 hours
Compositional data, explaining cross-species patterns using traits.
Main packages: mvabund.
Day 8 – approx. 3 hours
Classifying species based on environmental response, predictive models
Main packages: Speciesmix, mvabund, lme4.
Day 9 – approx. 3 hours
Model-based ordination and inference
Main packages: gllvm.
Day 10 – approx. 3 hours
Inferring interactions form co-occurrence data
Main packages: gllvm, ecoCopula.
Course Instructor
Prof. David Warton
Personal website
David is an ecological statistician who advances methodology for data analysis in ecology to improve the ability of ecologists to answer important research questions with a focus on developing and translating modern statistical approaches to important ecological problems.
His cross-disciplinary research involves evaluating the methods for data analysis currently used in ecology, and where necessary, developing new methodologies to assist ecologists answer key research questions. This has led to contributions to current practice in ecology in multivariate analysis, allometric line-fitting and the analysis of presence-only data.