BAS (version 1.4.7)

diagnostics: BAS MCMC diagnostic plot.

Description

Function to help assess convergence of MCMC sampling for bas ojects.

Usage

diagnostics(obj, type = c("pip", "model"), ...)

Arguments

obj

an object created by bas.lm or bas.glm

type

type of diagnostic plot. If "pip" the marginal inclusion probabilities are used, while if "model", plot posterior model probabilities

...

additional graphics parameters to be passed to plot

Value

a plot with of the marginal inclusion probabilities (pip) estimated by MCMC and renormalized marginal likelihoods times prior probabilities or model probabilities.

Details

BAS calculates posterior model probabilites in two ways when method="MCMC". The first is using the relative Monte Carlo frequencies of sampled models. The second is to renormalize the marginal likelihood times prior probabilities over the sampled models. If the markov chain has converged, these two quantities should be the same and fall on a 1-1 line. If not, running longer may be required. If the chain has not converged, the Monte Carlo frequencise may have have less bias, although may exhibit more variability.

See Also

Other bas methods: BAS, bas.lm, coef.bas, confint.coef.bas, confint.pred.bas, fitted.bas, force.heredity.bas, image.bas, predict.basglm, predict.bas, summary.bas, update.bas

Examples

# NOT RUN {
library(MASS)
data(UScrime)
UScrime[,-2] = log(UScrime[,-2])
crime.ZS =  bas.lm(y ~ .,
                   data=UScrime,
                   prior="ZS-null",
                   modelprior=uniform(),
                   method = "MCMC",
                   MCMC.iter = 1000)   # short run for the example
diagnostics(crime.ZS)

# }