runsims: Run mixed stock simulations

Description Usage Arguments Value Examples

View source: R/mixstock.R

Description

Run multiple simulations of a mixed stock systems with specified marker frequencies and source contributions, running multiple estimations (bootstrap samples or MCMC chains) for each simulation

Usage

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runsims(sim.n=10, mc.n=10, totsamp=200, which="all",
true.freq=matrix(c(0.65, 0.31, 0.01, 0.01, 0.01, 0.01,
               0.31,0.65,0.01,0.01,0.01,0.01),ncol=2),
       true.contrib=c(0.9, 0.1), est="MCMC", verbose=FALSE,
fuzz=0.001, nboot=1000, bootrpt=20, minmarks=3)

Arguments

sim.n

Number of simulations to run

mc.n

Number of bootstrap/MCMC chains to run for each simulation

totsamp

total sample size (to be distributed half in mixed stock and half, evenly, among sources)

which

which markers to use: "common", "rare", or "all"

true.freq

matrix of true marker frequencies in the sources (column=source, row=marker)

true.contrib

vector of true contributions of sources to the mixed stock

est

estimation method: "MCMC" (Markov Chain Monte Carlo), "cml" (conditional max. likelihood), or "uml" (unconditional max. likelihood)

verbose

produce lots of debugging output?

fuzz

"fuzz" parameters for (e.g.) keeping estimated values away from 0/1

nboot

Number of bootstrap samples/length of MCMC chain

bootrpt

Frequency for reporting on the progress of bootstrap code

minmarks

Minimum number of markers to allow for a simulation

Value

Array of the results from all simulations and bootstrap samples

Examples

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## mild kluge to drop unneeded dimensions
  runsims(sim.n=1,mc.n=1,nboot=100,est="cml")[,,]
  

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