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 0.9.2 Index]