BayesVarSel (version 1.7.0)

PBvs: Bayesian Variable Selection for linear regression models using parallel computing.

Description

PBvs is a parallelized version of Bvs.

Usage

PBvs(formula, fixed.cov=c("Intercept"), data, prior.betas = "Robust", prior.models = "ScottBerger", n.keep = 10, n.nodes = 2,  priorprobs=NULL, time.test=TRUE)

Arguments

formula

Formula defining the most complex regression model in the analysis. See details.

fixed.cov

A character vector with the names of the covariates that will be considered as fixed (no variable selection over these). This argument provides an implicit definition of the simplest model considered. Default is "Intercept". Use NULL if selection should be performed over all the variables defined by formula

data

data frame containing the data.

prior.betas

Prior distribution for regression parameters within each model. Possible choices include "Robust", "Liangetal", "gZellner", "ZellnerSiow" and "FLS" (see details)

prior.models

Prior distribution over the model space. Possible choices are "Constant" and "ScottBerger" and "User" (see details)

n.keep

How many of the most probable models are to be kept?

n.nodes

Number of nodes to be used in the computation

priorprobs

A p+1 dimensional vector defining the prior probabilities Pr(M_i) (should be used in the case where prior.models="User"; see details.)

time.test

If TRUE a preliminary test to estimate computational time is performed.

Value

returns an object of class Bvs with the following elements: with the following elements:

Details

This function takes advantage of the library parallel to distribute the models in the model space throughout the number of nodes available. Its intended use is for moderately large model spaces (p>=20).

A detailed description of the arguments can be found in the details section in Bvs.

References

Bayarri, M.J., Berger, J.O., Forte, A. and Garcia-Donato, G. (2012) Criteria for Bayesian Model choice with Application to Variable Selection. The Annals of Statistics. 40: 1550-1557

Fernandez, C., Ley, E. and Steel, M.F.J. (2001) Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100, 381-427. Liang, F., Paulo, R., Molina, G., Clyde, M. and Berger, J.O. (2008) Mixtures of g-priors for Bayesian Variable Selection. Journal of the American Statistical Association. 103:410-423. Zellner, A. and Siow, A. (1980) Posterior Odds Ratio for Selected Regression Hypotheses. In Bayesian Statistics 1 (J.M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.) 585-603. Valencia: University Press. Zellner, A. and Siow, A. (1984). Basic Issues in Econometrics. Chicago: University of Chicago Press. Zellner, A. (1986) On Assessing Prior Distributions and Bayesian Regression Analysis with g-prior Distributions. In Bayesian Inference and Decision techniques: Essays in Honor of Bruno de Finetti (A. Zellner, ed.) 389-399. Edward Elgar Publishing Limited.

See Also

plotBvs for different descriptive plots of the results, BMAcoeff for obtaining model averaged simulations of regression coefficients and predictBvs for predictions.

GibbsBvs which implements a heuristic approximation to the problem based on Gibbs sampling.

Examples

## Not run: 
# #Analysis of Crime Data
# #load data
# 
# data(UScrime)
# 
# #Default arguments are Robust prior for the regression parameters
# #and constant prior over the model space
# #Here we keep the 1000 most probable models a posteriori:
# #The computation over the model space is distributed over two
# #cores:
# crime.Bvs<- PBvs(formula="y~.", data=UScrime, n.keep=1000, 
# n.nodes=2)
# 
# #A look at the results:
# crime.Bvs
# 
# summary(crime.Bvs)
# 
# #An image plot with the joint inlcusion 
# #probabilities:
# plotBvs(crime.Bvs, option="joint")## End(Not run)