Introduction to GLM with spatial, and spatial-temporal correlation using R-INLA

Onsite course:

Introduction to GLM with spatial, and spatial-temporal correlation using R-INLA

 

 

This online course consists of 5 modules representing a total of approximately 40 hours of work. 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.

During the course, several case studies are presented in which the statistical theory is integrated with applied analyses in a clear and understandable manner. 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.

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.

We reserve the right to change the exercises. Pdf files of all theory material will be provided. All exercises consist of data sets and annotated R scripts. Access to the course website is for 6 months. The module 1- 5 material does not contain on-demand video.

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