accept_rate: Compute Acceptance Rates for Metropolis-Hastings and...
In conting: Bayesian Analysis of Contingency Tables

Description Usage Arguments Details Value Author(s) References See Also Examples

This function computes the acceptance rates of the Metropolis-Hastings and reversible jump algorithms from the MCMC output of bcct and bict objects.

1	accept_rate(object)

object

An object of class "bcct" or "bict".

Acceptance rates can be used to assess the performance of MCMC methods (in particular the peformance of the reversible jump method, Brooks et al, 2003).

This function will return an object of class "acceptrate" which is a list with the following components.

`rj_ar`	Acceptance rate (as a %) of the reversible jump algorithm.
`mh_ar`	Acceptance rate (as a %) of the Metropolis-Hastings algorithm.

Antony M. Overstall A.M.Overstall@soton.ac.uk.

Brooks, S.P., Giudici, P., & Roberts, G.O. (2003) Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions. Journal of the Royal Statistical Society, Series B, 65 (1), 3–55.

print.acceptrate, bcct, bict.

set.seed(1) ## set a seed for reproducibility
data(AOH)
test1<-bcct(formula=y~(alc+hyp+obe)^3,data=AOH,n.sample=500,prior="UIP")
## Create a bcct object for the AOH dataset for a very small number of
## iterations  (500).
accept_rate(test1)
## Calculate accept rates. Will get:

#Acceptance rate of reversible jump proposals =  32.5581 % 
#Acceptance rate of Metropolis-Hastings proposals =  76.8595 %