On demand: Regression models with spatial or spatial-temporal correlation using R-INLA

Online course with on-demand video

Introduction to regression models with spatial or spatial-temporal correlation using R-INLA

This online course comprises 5 modules (plus one bonus module), representing a total of approximately 40 hours of work. Each module includes multiple video files featuring theory presentations, followed by exercises using real datasets, and video files discussing the solutions. All video content is on-demand and can be accessed online as often as you like, at any time, for a period of 12 months.

This course is also sometimes used for live online sessions, providing opportunities for real-time interaction. Each module represents a full teaching day.

A discussion board enables daily interaction between instructors and participants. You can also use the discussion board to ask any questions about the course material. The course fee includes a 1-hour face-to-face video chat with the instructors, which you can use to discuss your own data or seek clarification on course topics.

A detailed course outline is provided below. We begin with an introduction how to add spatial dependency to regression models using frequentist tools. After discussing the limitations of this approach, we switch to Bayesian techniques. R-INLA is used to implement regression models and generalised linear models (GLM) with spatial, and spatial-temporal dependency. The course also contains a short revision of generalised linear models (GLM). Additionally, we will explain the beta, Gamma, and Tweedie distributions.

Throughout the course we will use the R-INLA package in R. This is a non-technical. Provided you have the required knowledge, this is an easy-to-follow course. Each exercise includes a dataset, R solution code, and a video explaining the R solution.

Preparation material (containing on-demand video):

  • Revision exercise on multiple linear regression.
  • Introduction to matrix notation.
  • Introduction to DHARMa.
  • What is a variogram.

Module 1:

  • General introduction.
  • Theory presentation on adding temporal dependency, and spatial dependency to a regression model using frequentist techniques.
  • One exercise showing how to add spatial dependency to a regression model using frequentist tools.
  • Brief introduction to Bayesian analysis.
  • Conjugate priors.
  • Diffuse versus informative priors

Module 2:

  • Theory presentation on INLA.
  • Exercise showing how to execute a linear regression model in R-INLA.
  • Theory presentation on adding spatial correlation to a regression model using in R-INLA.
  • Exercise showing how to add spatial correlation to a linear regression model.

Module 3:

  • Exercise showing how to execute a Poisson GLM in R-INLA.
  • Exercise showing how to add spatial correlation to a Poisson GLM.
  • Exercise showing how to add spatial correlation to a negative binomial GLM.

Module 4:

  • Exercise showing how to add spatial correlation to a Bernoulli GLM.
  • Exercise showing how to add spatial correlation to a gamma GLM.
  • Exercise showing how to add spatial correlation to a beta GLM.

Module 5:

  • Theory presentation on adding spatial-temporal correlation in R-INLA.
  • Exercise showing how to add spatial-temporal correlation to a Poisson or negative binomial GLM.
  • Exercise showing how to add spatial-temporal correlation to a Tweedie GLM.
  • Exercise showing how to add spatial-temporal correlation to a Bernoulli GLM.

Bonus Module:

  • Dealing with natural barriers (e.g. an island for fisheries data).
    • Theory presentation of the barrier model.
    • Exercise showing how to deal with spatial correlation around an island for a coral reef data set using a beta GLM.
  • Regression models and GLMs with spatial correlation for areal data.
    • Theory presentation on the analysis of areal data.
    • Exercise showing how to execute a Poisson (and negative binomial) GLM with spatial correlation using areal data in R-INLA.

 

Pre-required knowledge: Good knowledge of R, data exploration, linear regression and GLM (Poisson, negative binomial, Bernoulli). Short revisions are provided. This is a non-technical course.

Cancellation policy: What if you are not able to participate? Once participants are given access to course exercises with R solution codes, pdf files of certain book chapters, pdf files of PowerPoint or Prezi presentations and video solution files, all course fees are non-refundable and non-transferable to another participant.

Copyright: Sharing the access details of the course website or the pdf files of our course material is prohibited. Video files cannot be downloaded, but they can be watched in the same way as on Netflix.

For terms and conditions, see:
https://www.highstat.com/index.php/component/hikashop/checkout/termsandconditions/step-3/pos-6/tmpl-component