Course format:
- Self-study course.
- On-demand access to all video content online within a 12-month period.
- Daily interaction on the Discussion Board for detailed questions.
- Live chat for quick queries.
- Course fee includes a 1-hour video chat with instructors for personalized questions and data assistance.
Course content
We will start with a short revision of multiple linear regression, followed by a basic introduction to Bayesian analysis, and we show how to execute a linear regression model in R-INLA.
In the second module, we will explain how to deal with zero-inflated count data and zero-inflated continuous data using zero-inflated Poisson, zero-inflated negative binomial, and zero-inflated Gamma GLMs. We also explain hurdle models.
In the third module, we will introduce generalised additive models (GAM) to model non-linear relationships. We show how to execute these in mgcv and also in R-INLA.
In the fourth part of the course, we will revise linear mixed-effects models and implement these in R-INLA. We also apply generalised linear mixed-effects models (GLMM) and generalised additive mixed-effects model (GAMM) in R-INLA.
In the fifth part of the course, we will apply zero-inflated GAMs and GAMMs (and GLMMs) on various spatial correlated data sets.
In module 6, we apply GAM, GAMM, and GLMM on spatial-temporal correlated data. We also deal with natural barriers for the spatial correlation (e.g. benthic species that live on a coral reef around an island). We will use barrier models; these ensure that spatial correlation seeps around a barrier (in this case an island).
All exercises are executed in R-INLA.
Detailed outline
Module 1: Revision and introduction to R-INLA
- We start with a revision of data exploration and linear regression, followed by an introduction to Bayesian statistics and R-INLA.
- An exercise revising multiple linear regression (frequentist approach).
- A short video explaining basic matrix algebra.
- A video presentation with a short introduction to Bayesian statistics and the role of priors.
- A video presentation explaining the basic principles of INLA.
- One exercise showing how to execute a linear regression model in R-INLA (Bayesian approach).
Module 2: Introduction to zero-inflated models
- A video presentation on the Poisson, negative binomial and Bernoulli distributions.
- A video presentation with a short revision of Poisson, negative binomial and Bernoulli GLM.
- One exercise showing how to execute a Poisson GLM in R-INLA.
- One exercise showing how to execute a negative binomial GLM in R-INLA.
- One exercise showing how to execute a Bernoulli GLM in R-INLA.
- A video presentation explaining models for zero-inflated count data (ZIP, ZINB, ZAP and ZANB models) and continuous data.
- Three exercises on the analysis of zero-inflated count data and continuous data using R-INLA.
Module 3: Generalised additive models in R-INLA
- A video with a theory presentation on generalised additive models (GAM).
- One exercise showing how to execute a GAM with a Gaussian distribution in R-INLA.
- One exercise showing the application of a Poisson GAM in R-INLA.
- One exercise showing the application of a negative binomial GAM in R-INLA.
- One exercise showing the application of a Bernoulli GAM in R-INLA.
Module 4: GAMM with interactions
- A video presentation with a short revision of linear mixed-effects models.
- One exercise showing how to execute a linear mixed-effects model in R-INLA.
- One exercise showing how to execute a GLMM in R-INLA.
- One exercise showing how to execute a generalised additive mixed-effects model (GAMM) in R-INLA.
- A video presentation showing how to implement an interaction term between a smoother and a categorical covariate in a GAMM.
- One exercise showing how to execute a GAMM with an interaction between a smoother and a categorical covariate in R-INLA.
Module 5: GAM and GAMM (and GLMM) applied to spatial correlated data
- A short video explaining the essential steps of adding spatial correlation to a linear regression model.
- One exercise showing how to apply a linear regression model with spatial correlation in R-INLA.
- Four exercises on zero-inflated GAMs, GAMMs and GLMMs with spatial correlation in R-INLA.
Module 6: GAM and GAMM (and GLMM) applied to spatial-temporal correlated data. Barrier models
- Three exercises on the application of zero-inflated GAMs (and GAMMs and GLMMs) to spatial-temporal correlated data in R-INLA.
- Theory presentation on barrier models.
One exercise showing how to apply a GAM/GLM with spatial correlation and a barrier.
Course material
Pdf files of all presentations are provided. These files are based on various chapters from:
- Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. Volume II: GAM and Zero-Inflated Models (2018). Zuur, Ieno. ISBN: 9780957174146.
This book is exclusively available from www.highstat.com. This book is not included in the course fee. The course can be followed without purchasing this book.
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.