MCMCpack (version 1.7-0)

BayesFactor: Create an object of class BayesFactor from MCMCpack output

Description

This function creates an object of class BayesFactor from MCMCpack output.

Usage

BayesFactor(...)
is.BayesFactor(BF)

Value

An object of class BayesFactor. A BayesFactor

object has four attributes. They are: BF.mat an \(M \times M\) matrix in which element \(i,j\) contains the Bayes factor for model \(i\) relative to model \(j\);

BF.log.mat an \(M \times M\) matrix in which element

\(i,j\) contains the natural log of the Bayes factor for model

\(i\) relative to model \(j\); BF.logmarglike an

\(M\) vector containing the log marginal likelihoods for models 1 through \(M\); and BF.call an \(M\) element list containing the calls used to fit models 1 through \(M\).

Arguments

...

MCMCpack output objects. These have to be of class mcmc and have a logmarglike attribute. In what follows, we let M denote the total number of models to be compared.

BF

An object to be checked for membership in class

BayesFactor.

See Also

MCMCregress

Examples

if (FALSE) {
data(birthwt)

model1 <- MCMCregress(bwt~age+lwt+as.factor(race) + smoke + ht,
                     data=birthwt, b0=c(2700, 0, 0, -500, -500,
                                        -500, -500),
                     B0=c(1e-6, .01, .01, 1.6e-5, 1.6e-5, 1.6e-5,
                          1.6e-5), c0=10, d0=4500000,
                     marginal.likelihood="Chib95", mcmc=10000)

model2 <- MCMCregress(bwt~age+lwt+as.factor(race) + smoke,
                     data=birthwt, b0=c(2700, 0, 0, -500, -500,
                                        -500),
                     B0=c(1e-6, .01, .01, 1.6e-5, 1.6e-5, 1.6e-5),
                     c0=10, d0=4500000,
                     marginal.likelihood="Chib95", mcmc=10000)

model3 <- MCMCregress(bwt~as.factor(race) + smoke + ht,
                     data=birthwt, b0=c(2700, -500, -500,
                                        -500, -500),
                     B0=c(1e-6, 1.6e-5, 1.6e-5, 1.6e-5,
                          1.6e-5), c0=10, d0=4500000,
                     marginal.likelihood="Chib95", mcmc=10000)

BF <- BayesFactor(model1, model2, model3)
print(BF)

}