ONLINE COURSE – Model-based multivariate analysis of abundance data using R (MBMV03) This course will be delivered live
16 November 2020 - 27 November 2020£510.00
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
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 email@example.com for full details or to discuss how we can accommodate you).
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).
Venue – Delivered remotely
Availability – 20 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 firstname.lastname@example.org
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 email@example.com 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.
Prof. David Warton
Works at – Senior Lecturer, Psychology Department, Nottingham Trent University, England
Teaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R; Generalised Linear, Nonlinear and General Additive Models; Python for data science, machine learning, and scientific computing
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
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.
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 firstname.lastname@example.org
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
Monday 16th – Classes from 10:00 to 13:00 EDT
Revision of key “Stat 101” messages
Tuesday 17th – Classes from 10:00 to 13:00 EDT
Revision of (univariate) regression analysis: the linear model, generalised linear model.
Main packages: lme4.
Wednesday 18th – Classes from 10:00 to 13:00 EDT
Linear mixed models, the parametric bootstrap, permutation tests and the bootstrap.
Main packages: lme4, mvabund.
Thursday 19th – Classes from 10:00 to 13:00 EDT
Model selection, classical multivariate analysis.
Main packages: glmnet.
Friday 20th – Classes from 10:00 to 13:00 EDT
Multivariate abundance data: hierarchical models, key properties, hypothesis testing.
Main packages: mvabund.
Monday 23rd – Classes from 10:00 to 13:00 EDT
Multivariate abundance data: design-based inference for dependent data, indicator species.
Main packages: mvabund.
Tuesday 24th – Classes from 10:00 to 13:00 EDT
Compositional data, explaining cross-species patterns using traits.
Main packages: mvabund.
Wednesday 25th – Classes from 10:00 to 13:00 EDT
Classifying species based on environmental response, predictive models
Main packages: Speciesmix, mvabund, lme4.
Thursday 26th – Classes from 10:00 to 13:00 EDT
Model-based ordination and inference
Main packages: gllvm.
Friday 27th – Classes from 10:00 to 13:00 EDT
Inferring interactions form co-occurrence data
Main packages: gllvm, ecoCopula.
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)