SCS 2017: Longitudinal and Nested Data
At their best, graphics are instruments for reasoning. — Edward Tufte
Where there is no uncertainty, there cannot be truth. — Richard Feynman
This is the home page for the SCS short course on Modeling and Analysis of Longitudinal and Nested Data offered Wednesday evenings from 6pm to 9pm from March 1 to March 29, 2017 in York Lanes 213.
- Piazza class forum
- Course Description
- R Links
- Longitudinal and Nested References and Links (post new references and comments to Piazza)
|Day||Files and Links|
Ideas in Regression: Why Models Matter
Getting Started with R and R Studio
Linear Models for Nested Data with Normal Error
Linear Models for Longitudinal Data with Normal Error
Linear Models for Longitudinal Data with Normal Error (cont'd)
Non-Linear Models for Longitudinal Data with Normal Error
Introduction to Bayesian Approaches: MCMC and HMC
Installing Rstan for HMC with STAN
See the documentation site for Stan and download the pdf manual. It will look forbidding at first but it's remarkably comprehensive and easy to use once you learn to navigate in it.
Introduction to Stan
- Download the current manual from http://mc-stan.org/.
- Daniel Lee (2014) Hamiltonian Monte Carlo within Stan
- Daniel Lee (2014) Hierarchical Models in Stan
- Various models for TBI.
- Correcting the bias in the estimate of compositional/contextual effects with small cluster size. This is a simple example illustrating how Stan can be used to specify models with latent predictor variables.
This is an evolving topic. For a while WAIC (Watanabe-Akaike Information Criterion) was considered the state of the art for Bayesian models but perhaps this is being overtaken by the 'loo' package that works specifically with Stan. There are good readable article on it.