tmcmc: Mixed stock analysis by Markov Chain Monte Carlo

In mixstock: functions for mixed stock analysis

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

mixstock documentation built on May 2, 2019, 6:48 p.m.