brinla

Examples using INLA for Bayesian Regression Models

You will need to install the brinla R package to run the examples described here.

These examples do not correspond exactly with found in the book. Some come from earlier drafts, some are not found in the book and some are experimental.

Worked Examples

Introduction - a simple example concerning Hubble’s law.
Linear regression - Multiple regression example using some Chicago Insurance data
One-way ANOVA - just one random effect
Ridge regression - Ridge regression with meat spectroscopy data
Gaussian Process regression - GP regression on fossil data
Confidence bands for smoothness - GP regression to determine uncertainty regarding smoothness
Non-stationary smoothing - GP regression with variable smoothing
Generalized Extreme Values - fitting maximum annual river flows
Define your own prior - using a half Cauchy prior for the SD of a random effect.
Linear regression with bounded parameters - French presidential election example with slope parameters bounded in [0,1]

Linear Mixed Model Examples

See https://github.com/julianfaraway/rexamples for updated versions of these examples. The estimation procedure in INLA has changed since the output in the examples below was produced resulting in some noticeable changes. I keep these here for comparison purposes

These come from my Extending Linear Models with R book. I demonstrate these methods for each of the examples in the text. You’ll need to read the text for more background on datasets and the interpretations or you can just look at the help pages for the datasets. I’ve focussed attention on the process for fitting the model and summaries. There’s lots more you can do so these analyses are far from complete.

Single Random Effect - the pulp data
One Fixed and One Random Effect - the penicillin data
Split Plots - the irrigation data
Nested Effects - the eggs data
Crossed Effects - the abrasion data
Multilevel Models - the jsp data
Longitudinal Models - the psid data
Repeated Measures - the vision data
Multiple Response Models - the jsp data

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