tmcmc {mixstock}R Documentation

Mixed stock analysis by Markov Chain Monte Carlo

Description

Runs a Gibbs sampler MCMC starting with 95% contribution from each source, then combines the chains

Usage

tmcmc(data, n.iter=20000, rseed=1001, n.burnin=floor(n.iter/2),
n.thin=max(1, floor(n.chains * (n.iter - n.burnin)/1000)),
verbose=FALSE, fprior=NULL,
contrib.only=TRUE, rptiter=-1,
outfile=NULL, lang="C",a=NULL,gr=FALSE)
gibbs(sourcesamp, mixsamp, a = 1, startiter, maxiter, startfval = NULL, 
    n.thin = 1, fprior = NULL, rptiter = -1) 

Arguments

data Data: a mixstock.data object
n.iter Total length of each chain
n.burnin Number of burn-in iterations
n.thin thinning rate. Must be a positive integer. Set 'n.thin' > 1 to save memory and computation time if 'n.iter' is large. Default is 'max(1, floor(n.chains * (n.iter-n.burnin) / 1000))' which will only thin if there are at least 2000 simulations.
rseed Random-number seed
verbose Produce lots of output
fprior Bayesian prior for source contributions
contrib.only To save memory, store only information about contributions from each source and not about the estimated marker frequencies in each source
rptiter How often to issue a progress report. Negative numbers mean no reports
outfile file to use for output
lang Run the chain in C or R (for debugging/testing purposes only)?
a prior strength parameter
gr calculate Gelman-Rubin convergence statistic?
sourcesamp matrix of marker samples from sources
mixsamp vector of marker samples from mixed stock
startiter starting iteration
maxiter max. number of iterations
startfval starting source contributions

Value

Returns an object of type mixstock.est

Author(s)

Ben Bolker

References

Masuda and Pella

Examples

  data(bolten98)
  b98c <- markfreq.condense(as.mixstock.data(bolten98))
  t1 <- tmcmc(b98c); t1

[Package mixstock version 0.9 Index]