Advanced R users from all professional groups.
During this two-day course with practical sessions, we aim to give an introduction to the R-INLA package. The course starts with a general overview of the possibilities of INLA for applied research and for model development, and it proceeds with examples of generalized linear models with several random effects.
First, we will discuss the innovative ideas making INLA fast; why most likelihoods are near-Gaussian in the posterior, how to represent random effects with sparse matrices, and more.
The main part of the course is practically oriented. Using examples we will explain how to fit different models in practice using R-INLA, and how to interpret the results. The models we fit include, for example, Gaussian, Binomial and Poisson likelihoods, auto-regressive, random walk and varying-intercept models. We will discuss the choice of priors, and how to compare models.
Another important topic is spatial (geographical) modeling, where we show how to construct spatial models with the stochastic partial differential equation (SPDE) approach. In ecology, these models result in habitat maps and abundance maps, and in disease mapping they result in continuous risk maps and in risk classification maps. We discuss point patterns in detail as they fit naturally in the framework: by combining spatial modeling and the Poisson likelihood.
All examples will be run live, also by the participants, which illustrates INLA's computational speed.
For all Zurich R Courses participants should bring their own laptops to the course and will be informed by email in advance which packages they need to install.
29.-30. Oktober 2018