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ONLINE COURSE – Model-based multivariate analysis of abundance data using R (MBMV03)
1st February 2021 - 12th February 2021
£395.00
Event Date
Monday, February 1st, 2021
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course Details
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.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – Australian Eastern Daylight Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
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).
Venue
Delivered remotely
Course Information
Availability – 40 places
Duration – 10 days
Contact hours – Approx. 30 hours
ECT’s – Equal to 3 ECT’s
Language – English
Other payment options are available please email oliverhooker@prstatistics.com
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/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.
https://cran.r-project.org/
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.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com
Assumed quantitative knowledge
Coming soon..
Assumed computer background
Coming soon..
Equipment and software requirements
Attendees will need to install/update R/RStudio and various additional R packages.
This can be done on Macs, Windows, and Linux.
R – https://cran.r-project.org/
RStudio – https://www.rstudio.com/products/rstudio/download/
PLEASE READ – 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 oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
There will additional Q and A support for people who can follow during real time – this will be from 21:30 to 22:00 EDT
WEEK 1
Monday 1st – Classes from 10:00 to 13:00 EDT
Revision of key “Stat 101” messages
Tuesday 2nd – Classes from 10:00 to 13:00 EDT
Revision of (univariate) regression analysis: the linear model, generalised linear model.
Main packages: lme4.
Wednesday 3rd – Classes from 10:00 to 13:00 EDT
Linear mixed models, the parametric bootstrap, permutation tests and the bootstrap.
Main packages: lme4, mvabund.
Thursday 4th – Classes from 10:00 to 13:00 EDT
Model selection, classical multivariate analysis.
Main packages: glmnet.
Friday 5th – Classes from 10:00 to 13:00 EDT
Multivariate abundance data: hierarchical models, key properties, hypothesis testing.
Main packages: mvabund.
WEEK 2
Monday 8th – Classes from 10:00 to 13:00 EDT
Multivariate abundance data: design-based inference for dependent data, indicator species.
Main packages: mvabund.
Tuesday 9th – Classes from 10:00 to 13:00 EDT
Compositional data, explaining cross-species patterns using traits.
Main packages: mvabund.
Wednesday 10th – Classes from 10:00 to 13:00 EDT
Classifying species based on environmental response, predictive models
Main packages: Speciesmix, mvabund, lme4.
Thursday 11th – Classes from 10:00 to 13:00 EDT
Model-based ordination and inference
Main packages: gllvm.
Friday 12th – Classes from 10:00 to 13:00 EDT
Inferring interactions form co-occurrence data
Main packages: gllvm, ecoCopula.
Course Instructor
Dr. Antoine Becker-Scarpitta
Works at – University of Helsink
Teaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
Antoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and from the Natural History Museum of Paris, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.
