Generalised additive mixed models analysis via gammSlice
- Publication Type:
- Journal Article
- Australian and New Zealand Journal of Statistics, 2018, 60 (3), pp. 279 - 300
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2018 Australian Statistical Publishing Association Inc. Published by John Wiley & Sons Australia Pty Ltd. We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.
Please use this identifier to cite or link to this item: