Plots the posterior model probabilites based on 1) marginal likelihoods and 2) MCMC frequencies for the best models in a 'bma' object and details the sampler's convergence by their correlation
A call to bms with a MCMC sampler (e.g. bms(datafls,mcmc="bd",nmodel=100) uses a Metropolis-Hastings algorithm to sample through the model space: the frequency of how often models are drawn converges to the distribution of their posterior marginal likelihoods.
While sampling, each 'bma' object stores the best models encountered by its sampling chain with their marginal likelihood and their MCMC frequencies.
plotConv compares the MCMC frequencies to marginal likelihoods, and thus visualizes how well the sampler has converged.
See Also
pmp.bma for posterior model probabilites based on the two concepts, bms for creating objects of class 'bma'