Helper function to MC3.REG
Helper function to MC3.REG that chooses the proposal model for a Metropolis-Hastings step.
MC3.REG.choose(M0.var, M0.out)
M0.var |
a logical vector specifying the variables in the current model. |
M0.out |
a logical vector specifying the outliers in the current model. |
A list representing the proposal model, with components
var |
a logical vector specifying the variables in the proposal model. |
out |
a logical vector specifying the outliers in the proposal model. |
The implementation here differs from the Splus implentation. The Splus implementation uses global variables to contain the state of the current model and the history of the Markov-Chain. This implentation passes the current state and history to the function and then returns the updated state.
Jennifer Hoeting jah@AT@stat.colostate.edu with the assistance of Gary Gadbury. Translation from Splus to R by Ian Painter ian.painter@AT@gmail.com.
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: https://www.stat.colostate.edu/~jah/papers/
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