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Highland Statistics Ltd

Online course with on-demand video and live Zoom meetings: Introduction to regression models with spatial correlation using R-INLA

This online course contains 4 modules representing a total of approximately 50-60 hours of work. Each module consists of multiple video files with short theory presentations, followed by exercises using real data sets, and video files discussing the solutions. All video files are on-demand and can be watched online, as often as you want, at any time of the day, within a 6 month period.

A discussion board allows for daily interaction between instructors and participants. The course also contains a series of (approximately) 2-hour live web meetings in which we summarise some of the theory and exercises. Attending these live web meetings is optional. We will run the web meetings in different time zones. You can also use a Discussion board to ask any questions on the course material. The course fee includes a 1-hour face-to-face video chat with the instructors. You can use this video chat to ask questions about your own data.

A detailed outline of the course is provided below. All exercises consist of a data set, R solution code, and a video discussing the R solution file.

 

Part I: Limitations of frequentist approaches

Module 1A: Introduction and revision.

  • A video with a general introduction and a discussion of dependency.
  • Revision exercise for multiple linear regression.
  • A short video explaining basic matrix algebra.

Module 1B: Dependency using frequentist techniques.

  • A video showing how to add temporal or spatial correlation to a linear regression model in a frequentist setting.
  • A video revising linear mixed-effects models and an exercise in a frequentist setting.

Zoom summary session 1: A 2-hour online meeting will be scheduled. We will summarise modules 1A and 1B.

 

Part II: INLA and the application of linear (mixed-effects) models and GLM in INLA

Module 2A: Introduction Bayesian statistics and INLA.

  • Two video presentations in which we explain the basics of Bayesian statistics and the role of informative and diffuse priors.
  • A video presentation explaining the basic principles of INLA.

Module 2B: Models without spatial correlation in R-INLA.

  • Exercise showing how to execute a linear regression model in R-INLA.
  • Exercise showing how to execute a linear mixed-effects model in R-INLA.
  • Exercise showing how to execute a Poisson GLM in R-INLA.
  • Short theory presentation on the residual covariance matrix.

Zoom summary session 2: A 2-hour online meeting will be scheduled. We will summarise modules 2A and 2B.

 

Part III: The application of regression models, GLMs  and GLMMs with spatial correlation in R-INLA

Module 3: Models with spatial correlation in INLA.

  • Theory presentation on how to include spatial correlation in R-INLA.
  • One exercise showing how to execute a linear regression model with spatial correlation in R-INLA.
  • One exercise showing how to execute a Poisson GLM with spatial correlation in R-INLA.
  • One exercise showing how to execute a negative binomial GLM with spatial correlation in R-INLA.
  • One exercise showing how to add spatial correlation to a Bernoulli GLM.
  • One exercise showing how to add spatial correlation to a gamma GLM.

Zoom summary session 3: A 2-hour online meeting will be scheduled. We will summarise module 3.

 

Part IV: Barrier models and the analysis of areal data in R-INLA

Module 4A: Dealing with natural barriers (e.g. an island for fisheries data).

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

Module 4B: Regression models and GLMs with spatial correlation for areal data.

  • Video file with 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.

Zoom summary session 4: A 2-hour online meeting will be scheduled. We will summarise modules 4A and 4B.

 

Free 1-hour face-to-face video meeting: The course fee includes a 1-hour face-to-face video meeting with one or both instructors. The meeting needs to take place within 3 months after the last live zoom meeting. You can discuss your own data but the statistical topics need to be within the content of the course. The 1-hour needs to be consumed in one session and will take place at a mutually convenient time.

Web meetings: Web meetings are hosted on zoom.us. Click here for recommended internet speed (see the text under 'Recommended bandwidth for Webinar Attendees'). We will record the meetings and make them available on the course website.

Discussion Board: You can use the Discussion Board to ask any questions related to the course material. 

Pre-required knowledge: Good knowledge of R, data exploration, linear regression and GLM (Poisson, negative binomial, Bernoulli). Working knowledge of mixed-effects models. 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.