mixstock.boot: Bootstrap samples of mixed stock analysis data
In mixstock: functions for mixed stock analysis

Description Usage Arguments Details Value Author(s) Examples

Create a bootstrap (multinomial) sample from a given set of marker data (source and mixed population genotypes), either parametric or non-parametric

1 2	mixstock.boot(x, param=FALSE, condense=TRUE,save.freq=FALSE, param.match="mean")

`x`	"Standard" mixstock data, a list with `mixsamp` (vector of samples in the mixed population) and `sourcesamp` (matrix of samples in the sources, rows=markers, columns=sources)
`param`	parametric bootstrapping or not?
`condense`	use `markfreq.condense` on the results?
`save.freq`	save frequencies?
`param.match`	match mean or mode of distribution when parametric bootstrapping?

Nonparametric bootstrapping just resamples the observed data from the mixed population and from each source with replacement; this is equivalent to taking a multinomial sample with the probabilities equal to the observed sample frequencies. Parametric bootstrapping takes the observed samples and resamples the probabilities themselves from a Dirichlet distribution, then takes a multinomial sample.

A bootstrapped data set, in the same format as the input data: i.e.,

`mixsamp`	samples in mixed population
`sourcesamp`	samples in sources

...

Ben Bolker

true.freq <- matrix(c(0.65,0.33,0.01,0.01,
                       0.33,0.65,0.01,0.01),ncol=2)
true.contrib <- c(0.9,0.1)
x <- simmixstock0(true.freq,true.contrib,50,100,1004)
nboot <- 1000
boot.results.par <- matrix(NA,ncol=12,nrow=nboot)
boot.results.npar <- matrix(NA,ncol=12,nrow=nboot)
for (i in 1:nboot) {
  x.par  <- mixstock.boot(x,param=TRUE,condense=FALSE)
  x.npar <- mixstock.boot(x,condense=FALSE)
  boot.results.par[i,] <- c(x.par$sourcesamp,x.par$mixsamp)
  boot.results.npar[i,] <- c(x.npar$sourcesamp,x.npar$mixsamp)
}
summary(boot.results.par[,7:8])
summary(boot.results.npar[,7:8])
par(mfrow=c(1,2))
hist(boot.results.par[,7])
hist(boot.results.npar[,7])