a list containing the current state and the history of the Markov-Chain, with components
flaga 0/1 number specifying whether the previous Metropolis-Hastings step resulted in a changed state or not.
big.lista matrix containing the history of the Markov-Chain. Each row represents a unique model (combination of variables and outliers). The first column is the set of variables in the model (in binary form), the second column is the set of outliers in the model (in binary form), the third column is the log-posterior for the model (up to a constant) and the fourth column is the number of times that model has been visited.
M0.vara logical vector specifying the variables in the current model.
M0.outa logical vector specifying the outliers in the current model.
M0.1a number representing the variables in the current model in binary form.
M0.2a number represnting the outliers in the current model in binary form.
This function implements a single Metropolis-Hastings step, choosing a proposal model, calculating the Bayes Factor between the current model and proposal model, and updating the current model to the proposal model if the step results in an update.
References
Bayesian Model Averaging for Linear Regression Models
Adrian E. Raftery, David Madigan, and Jennifer A. Hoeting (1997).
Journal of the American Statistical Association, 92, 179-191.
A Method for Simultaneous Variable and Transformation Selection in Linear Regression
Jennifer Hoeting, Adrian E. Raftery and David Madigan (2002).
Journal of Computational and Graphical Statistics 11 (485-507)
A Method for Simultaneous Variable Selection and Outlier Identification in Linear Regression
Jennifer Hoeting, Adrian E. Raftery and David Madigan (1996).
Computational Statistics and Data Analysis, 22, 251-270
Earlier versions of these papers are available via the World Wide Web using the url:
http://www.stat.colostate.edu/~jah/papers/