PostProbMod: Calculate Posterior Probability of Model

In MCMCpack: Markov Chain Monte Carlo (MCMC) Package

Calculate Posterior Probability of Model

Description

This function takes an object of class BayesFactor and calculates the posterior probability that each model under study is correct given that one of the models under study is correct.

Usage

PostProbMod(BF, prior.probs = 1)

Arguments

BF	An object of class BayesFactor.
prior.probs	The prior probabilities that each model is correct. Can be either a scalar or array. Must be positive. If the sum of the prior probabilities is not equal to 1 prior.probs will be normalized so that it does sum to unity.

Value

An array holding the posterior probabilities that each model under study is correct given that one of the models under study is correct.

See Also

MCMCregress

Examples

## Not run: 
data(birthwt)

post1 <- 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)

post2 <- 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)

post3 <- 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(post1, post2, post3)
mod.probs <- PostProbMod(BF)
print(mod.probs)

## End(Not run)

MCMCpack documentation built on Sept. 11, 2024, 8:13 p.m.