MCMCpack (version 1.4-4)

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)

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.

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 \(M\).

See Also

MCMCregress

Examples

# NOT RUN {
# }
# NOT RUN {
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)

# }
# NOT RUN {
# }