Model-based multivariate analysis of abundance data using R (MBMV03)
20 May 2019 - 24 May 2019£300.00 - £600.00
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 – Myuna Bay Sport and Recreation, Wangi Road, Myuna Bay, New South Wales 2264 Australia, – Google Map – The nearest train station is Dora Creek railway station, Address: Dora Creek NSW 2264, Australia, it is located on the Main Northern line in New South Wales, Australia. It serves the City of Lake Macquarie town of Dora Creek. It is a 5 minute drive from the centre so a short affordable taxi ride.
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms. Arrival Monday 11th PM and departure Friday15th PM.
To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.
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.
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
Meet at Myuna Bay Sport and recreation at approx. 18:30
Monday 20th – Classes from 09:00 to 17:00
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 21st – Classes from 09:00 to 17:00
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 22nd – Classes from 09:00 to 17:00
Multivariate abundance data
Key properties, hypothesis testing, indicator species, compositional analysis, non-standard models.
Main packages: mvabund.
Thursday 23rd – Classes from 09:00 to 17:00
Explaining cross-species patterns
Classifying species based on environmental response, species traits as predictors, studying species interactions.
Main packages: Speciesmix, mvabund, lme4.
Friday 24th – Classes from 09:00 to 16:00
Model-based ordination and inference
Latent variable models for ordination, model-based inference for fourth corner models.
Main packages: boral, mvabund.
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)