Helper function to MC3.REG that calculates the posterior model probability (up to a constant).
MC3.REG.logpost(Y, X, model.vect, p, i, K, nu, lambda, phi)The log-posterior distribution for the model (up to a constant).
the vector of scaled responses.
the matrix of scaled covariates.
logical vector indicating which variables are to be included in the model
number of variables in model.vect
vector of possible outliers
a hyperparameter indicating the outlier inflation factor
regression hyperparameter. Default value is 2.58 if r2 for the full model is less than 0.9 or 0.2 if r2 for the full model is greater than 0.9.
regression hyperparameter. Default value is 0.28 if r2 for the full model is less than 0.9 or 0.1684 if r2 for the full model is greater than 0.9.
regression hyperparameter. Default value is 2.85 if r2 for the full model is less than 0.9 or 9.2 if r2 for the full model is greater than 0.9.
Jennifer Hoeting jennifer.hoeting@gmail.com with the assistance of Gary Gadbury. Translation from Splus to R by Ian Painter ian.painter@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/
MC3.REG, For.MC3.REG, MC3.REG.choose