Accept Rate {evdbayes} | R Documentation |
Compute suited proposal standard deviations for the MCMC algorithm.
ar.choice(init, prior, lh = c("none","gev","gpd","pp","os"), ..., psd, ar = rep(.4, npar), n = 1000, tol = rep(.05, npar))
init |
a numeric vector for the starting value of the MCMC algorithm. |
prior |
A prior model. See function prior.prob ,
prior.quant , prior.norm and
prior.loglognorm . |
lh |
The likelihood function. Should be one of ``none'', ``gev'', ``gpd'', ``pp'' and ``os''. |
... |
Optional arguments to be passed to the
posterior function. |
psd |
The initials proposal standard deviations. |
ar |
Optional. The objective accept rates - default is
rep(.4, npar) . |
n |
Optional. The length of the simulated Markov Chains. |
tol |
Optional. The tolerance for the convergence test. |
The suited proposal standard deviations (psd) are computed through trial and error processes. Proposal standard deviations are fundamental to ensure good mixing properties for the Markov Chains.
For this purpose, there exits a thumb rule: ``In small dimensions, aim at an average acceptance rate of 50. In large dimensions, at an average acceptance rate of 25. (Gelman et al., 1995)''.
For numerical conveniences, the trial and error process is more accurate with small initial starting psd.
Return a list with two arguments. ``psd'': the suited proposal standard deviations and ``ar'': the accept rates related to these proposal standard deviations.
Mathieu Ribatet
Gelman, A. and Roberts, G. and Gilks, W. (1995) Efficient Metropolis Jumping Rules. Oxford University Press.
data(rainfall) prrain <- prior.quant(shape = c(38.9, 7.1, 47), scale = c(1.5, 6.3, 2.6)) n <- 10000; t0 <- c(43.2, 7.64, 0.32); s <- ar.choice(init = t0, prior = prrain, lh = "pp", data = rainfall, thresh = 40, noy = 54, psd = rep(0.01, 3)) ##Alec Stephenson choose suited psd = c(2, .2, .07) which is really ##close to values computed by ``ar.choice''