mcMAM {numOSL} | R Documentation |
Sampling from the joint-likelihood function of the minimum age model using a Markov chain Monte Carlo (MCMC) method .
mcMAM(EDdata, ncomp = -1, addsigma = 0, iflog = TRUE, nsim = 50000, inis = list(), control.args = list())
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 |
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 |
Return an invisible list of S3 class object "mcAgeModels"
. See mcFMM for details.
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 http://www.cs.utoronto.ca/~radford/slice.software.html.
mcFMM; reportSAM; RadialPlotter; EDdata
# Not run. # data(EDdata) # Construct a MCMC chain for MAM3. # obj<-mcMAM(EDdata$al3,ncomp=-1,addsigma=0.1,nsim=5000) # reportSAM(obj,burn=1e3,thin=2) # # The convergence of the simulations may be diagnosed with # the Gelman and Rubin's convergence diagnostic. # library(coda) # Only if package "coda" has been installed. # args<-list(nstart=50) # inis1<-list(p=0.01,gamma=26,mu=104,sigma=0.01) # inis2<-list(p=0.99,gamma=100,mu=104,sigma=4.99) # obj1<-mcMAM(EDdata$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)