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

Onsite course :

 

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

 

We begin with an introduction to Bayesian statistics and how to execute a linear regression model in R-INLA. We then continue by discussing how to include spatial dependency in linear regression and generalized linear models (GLMs). We also provide an introduction to generalized additive models (GAMs) and show how to execute such models in R-INLA. Additionally, we apply GAMs with spatial dependency. Finally, we will show how to execute GLMs and GAMs with spatial-temporal dependency.


During the course, several case studies are presented, integrating statistical theory with applied analyses in a clear and understandable manner. Throughout the course, we will use the R-INLA package in R. This is a non-technical course; provided you have the required knowledge, it is easy to follow.

Course content

Monday:

  • General Introduction.
  • Brief Introduction to Bayesian Analysis. Conjugate priors. Diffuse versus informative priors
  • Theory presentation on R-INLA
  • Exercise on executing a linear regression model in R-INLA

Tuesday:

  • Short theory presentation on adding dependency to a regression model
  • Theory presentation on adding spatial correlation to a regression model in R-INLA
  • Exercise on adding spatial correlation to a linear regression model

Wednesday

  • Exercise on executing a Poisson GLM in R-INLA
  • Exercise on adding spatial correlation to a Poisson GLM
  • Exercise on adding spatial correlation to a negative binomial GLM

Thursday:

  • Theory presentation on GAM in R-INLA
  • Exercise on executing a Gaussian GAM in R-INLA
  • Exercise on adding spatial correlation to a Gaussian GAM

Friday:

  • Catching up
  • Theory presentation on adding spatial-temporal correlation in R-INLA
  • Exercise on adding spatial-temporal correlation to a negative binomial GAM
  • We reserve the right to change the exercises. Pdf files of all theory material will be provided. All exercises consists of data sets and annotated R scripts. Access to the course website is for 6 months. The Monday-Friday material does not contain on-demand video.

Pre-required knowledge

  • Participants should be familiar with data exploration, linear regression and basic GLMs (i.e. Poisson and negative binomial GLM) in R. The course does contain short revisions.

1 hour face-to-face

  • The course includes a 1-hour face-to-face video chat with the instructors (to be used after the course). You are invited to apply the statistical techniques discussed during the course on your own data and if you encounter any problems, you can ask questions during the 1-hour face-to-face chat.

Discussion Board

  • A discussion board (access for 6 months) allows for interaction on course content between instructors and participants.
    In this course, we will only use the Gaussian, Poisson and negative binomial distributions.

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