MC3.REG.choose {BMA}

Helper function to MC3.REG

Description

Helper function to MC3.REG that chooses the proposal model for a Metropolis-Hastings step.

Usage

MC3.REG.choose(M0.var, M0.out)

Arguments

M0.var

a logical vector specifying the variables in the current model.

M0.out

a logical vector specifying the outliers in the current model.

Value

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.

Note

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.

Author(s)

Jennifer Hoeting [e-mail] with the assistance of Gary Gadbury. Translation from Splus to R by Ian Painter [e-mail].

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: https://www.stat.colostate.edu/~jah/papers/

See Also

MC3.REG, For.MC3.REG, MC3.REG.logpost