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Multivariate analysis of spatial ecological data using R (MASE01)
19 June 2017 - 23 June 2017650.00£ - 780.00£
This 5-day course will cover the concepts, methods, and R tools that can be used to analyse spatial data in ecology. The start of the course will cover the basics of linear models and spatial data processing in R and provide a common ground for more advanced techniques encountered later on the course. We will cover spatial statistical techniques for both continuous and discrete data response types. We will put special emphasis on understanding mechanisms using visual tools, and quantifying uncertainty for model parameters and predictions. We will introduce Markov chain Monte Carlo methods for spatial hierarchical models and discuss special topics relevant for study design and conservation and management applications. Modules will consist of introductory lectures, guided computer coding, and exercises for the participants, analysing real data.
Research postgraduates, practicing academics and primary investigators in spatial ecology and management and environmental professionals in government and industry.
Venue – Northwest Atlantic Fisheries Centre, St. John’s – Google map – Directions
Availability – 32 places
Duration – 5 days
Contact hours – Approx. 37 hours
ECT’s – Equal to 3 ECT’s
Language – English
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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.
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Round-table discussions about the analysis requirements of attendees (option for them to bring their own data). 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 statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Assumed computer background
Familiarity with R. 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
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Monday 19th – Classes from 08:30 to 16:30
Review of regression analysis
Module 1. Introduction (types of response and predictor variables), data exploration (summaries, visualization, classification and regression trees [CART]).
Module 2. Linear, nonlinear, and multiple regression (use of lm and gam functions in R, visual interpretation, model diagnostics, model selection, generalized additive models [GAM]).
Module 3. Generalized linear models (use of glm and gam functions in R, visual interpretation, model diagnostics, model selection, generalized additive models [GAM]).
Tuesday 20th – Classes from 08:30 to 16:30
Spatial and temporal point processes
Module 4. Spatial data manipulation in R (raster and vector layers, GIS operations, mapping).
Module 5. Statistical models for point processes (homogeneous and non-homogeneous Poisson process, non-parametric measures of autocorrelation).
Wednesday 21st – Classes from 08:30 to 16:30
Continuous data on regular and irregular grid points in space
Module 6. Kriging and spatial smoothing for continuous data.
Module 7. Spatial generalizations of GLM: Markov random fields and hierarchical models.
Thursday 22nd – Classes from 08:30 to 16:30
Linear and Generalized linear mixed models in the context of spatial data
Module 8: Markov Chain Monte Carlo methods with applications in spatial data
Module 9. Movement models, species distribution models, critical habitat delineation.
Friday 23rd – Classes from 08:30 to 16:30
Module 10. Principles of optimal design for regression, effects of sampling design related biases on regression, measurement error in predictor variables.
Module 11. Philosophical considerations and other related discussion.