| boral-package {boral} | R Documentation |
Bayesian Ordination and Regression AnaLysis (boral)
Description
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
Details
| Package: | boral |
| Type: | Package |
| Version: | 0.6 |
| Date: | 2014-12-12 |
| License: | GPL-2 |
Author(s)
Francis K.C. Hui [aut, cre], Wade Blanchard [aut]
Maintainer: Francis K.C. Hui <fhui28@gmail.com>
References
Hui et al. (2014). Model-based approaches to unconstrained ordination. Methods in Ecology and Evolution, 6, 399-411.
Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing. March (pp. 20-22).
Skrondal, A., and Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. CRC Press.
Warton et al. (2015). So Many Variables: Joint Modeling in Community Ecology. Trends in Ecology and Evolution, 30, 766-779.
Yi W. et al. (2013).
mvabund: statistical methods for analysing multivariate abundance data. R package version 3.8.4.
Examples
## Please see main boral function for examples.