Dear Friends,
    According to Gelman et al (2003), "...Bayesian P-values are defined as
the probability that the replicated data could be more extreme than the
observed data, as measured by the test quantity p=pr[T(y_rep,tetha) >=
T(y,tetha)|y]..." where p=Bayesian P-value, T=test statistics, y_rep=data
from replicated experiment, y=data from original experiment, tetha=the
function of interest. My question is, How do I calculate p (the bayesian
P-value) in R from the chain I obtained from the Gibbs sampler? I have a
matrix 'samp' [10,000x86] where I stored the result of each of the 10,000
iterations of the 86 variables of interest.
Thanks for your help.
Jorge

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