numOSL (version 1.5)

mcMAM: Estimating parameters of minimum age models (3- or 4-parameter) with a Markov chain Monte Carlo method

Description

Constructing Markov chains for the parameters of interest in minimum age models.

Usage

mcMAM(EDdata, ncomp = -1, addsigma = 0, iflog = TRUE, 
      nsim = 50000, inis = list(), control.args = list())

Arguments

EDdata

data.frame(required): equivalent doses and associated errors (a total of two columns), minus equivalent dose values can also be analyzed

ncomp

numeric(with default): the number of components used for optimizing, -1 for the MAM3, -2 for the MAM4

addsigma

numeric(with default): the added spread to the relative (or absolute) standard error of equivalent dose

iflog

logical(with default): use log-scale or not

nsim

numeric(with default): the total number of simulations (desired row number of the chains)

inis

list(with default): initials of the chains, such as inis=list(p=0.2,gamma=20,sigma=0.2) for a 3-parameter minimum age model

control.args

list(with default): some further arguments (w, m, nstart) used in a Slice Sampling method, see mcFMM for details

Value

• Return an invisible list of class "mcAgeModels" (has the same members as that returned by function mcFMM), which can be operated with function reportSAM.

Details

see mcFMM for details.

References

Galbraith, R.F., Roberts, R.G., Laslett, G.M., Yoshida, H. & Olley, J.M., 1999. Optical dating of single grains of quartz from Jinmium rock shelter, northern Australia. Part I: experimental design and statistical models. Archaeometry, 41 (2), pp. 339-364.

Neal, R.M., 2003. "Slice sampling" (with discussion). Annals of Statistics, 31 (3), pp. 705-767.

See Also

mcFMM; reportSAM; RadialPlotter

Examples

data(EDdata)
  # Construct MCMC chains for a MAM3.
  obj<-mcMAM(EDdata$al3,ncomp=-1,addsigma=0.1,nsim=1e4)
  reportSAM(obj,burn=1e3)
  #
  # The convergence of the simulations may be diagnosed with 
  # the Gelman and Rubin's convergence diagnostic.
  # library(coda)   # Only if package "coda" has been installed.
  # args<-list(nstart=50)
  # inis1<-list(p=0.01,gamma=26,mu=104,sigma=0.01)
  # inis2<-list(p=0.99,gamma=100,mu=104,sigma=4.99)
  # obj1<-mcMAM(EDdata$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)