mcMAM {numOSL} | R Documentation |
Optimization of the minimum (maximum) age model (using a Markov chain Monte Carlo method)
Description
Sampling from the joint-likelihood function of the minimum (maximum) age model using a Markov chain Monte Carlo (MCMC) method.
Usage
mcMAM(EDdata, ncomp = -1, addsigma = 0, iflog = TRUE,
nsim = 50000, inis = list(), control.args = list())
Arguments
EDdata |
matrix(required): a two-column matrix (i.e., equivalent dose values and |
ncomp |
integer(with default): number of components, |
addsigma |
numeric(with default): additional uncertainty, i.e., the sigmab value |
iflog |
logical(with default): transform equivalent dose values to log-scale or not |
nsim |
integer(with default): deseried number of iterations |
inis |
list(with default): initial state of parameters. |
control.args |
list(with default): arguments used by the Slice Sampling algorithm, see function mcFMM for details |
Value
Return an invisible list of S3 class object "mcAgeModels"
. See mcFMM for details.
References
Galbraith RF, Roberts RG, Laslett GM, Yoshida H, Olley JM, 1999. Optical dating of single grains of quartz from Jinmium rock shelter, northern Australia. Part I: experimental design and statistical models. Archaeometry, 41(2): 339-364.
Neal RM, 2003. "Slice sampling" (with discussion). Annals of Statistics, 31(3): 705-767. Software is freely available at https://glizen.com/radfordneal/slice.software.html.
Peng J, Dong ZB, Han FQ, 2016. Application of slice sampling method for optimizing OSL age models used for equivalent dose determination. Progress in Geography, 35(1): 78-88. (In Chinese).
See Also
mcFMM; reportMC; RadialPlotter; EDdata; optimSAM; sensSAM
Examples
# Not run.
# data(EDdata)
# Construct a MCMC chain for MAM3.
# obj<-mcMAM(EDdata$al3,ncomp=-1,addsigma=0.1,nsim=5000)
# reportMC(obj,burn=1e3,thin=2)
#
# The convergence of the simulations may be diagnosed with
# the Gelman and Rubin's convergence diagnostic.
# library(coda) # Only if package "coda" has been installed.
# args<-list(nstart=50)
# inis1<-list(p=0.01,gamma=26,mu=104,sigma=0.01)
# inis2<-list(p=0.99,gamma=100,mu=104,sigma=4.99)
# obj1<-mcMAM(EDdata$al3,ncomp=-2,nsim=3000,inis=inis1,control.args=args)
# obj2<-mcMAM(EDdata$al3,ncomp=-2,nsim=3000,inis=inis2,control.args=args)
# chain1<-mcmc(obj1$chains)
# chain2<-mcmc(obj2$chains)
# chains<-mcmc.list(chain1,chain2)
# gelman.plot(chains)