Linear Mixed Effects Models and GLMM with R-INLA


This course has three parts:


Part one begins with a brief revision of multiple linear regression, followed by an introduction to Bayesian analysis and how to execute regression models in R-INLA. Linear mixed effects models to analyze nested data are then explained, followed by a series of mixed modeling exercises in R-INLA. Nested data means multiple observations from the same animal, site, area, nest, patient, hospital, vessel, lake, hive, transect, etc.


In the second part of the course, GLMMs are applied on count data, binary data (e.g. absence/presence of a disease), proportional data (e.g. % coverage) and continuous data (e.g. biomass or distance) using the Poisson, negative binomial, Bernoulli, binomial, beta and gamma distributions.


In the third part of the course we show how R-INLA can be used to execute GLMs with temporal dependency for the analysis of univariate and multivariate time series.


Campus Schoonmeersen, Building C - PC GSCHC.2.008 (2nd Floor) 02/12/2019 09:00 - 06/12/2019 12:008
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