chao_bootstrap: Produce bootstrap samples from each subpopulation of a genind...
In mmod: Modern Measures of Population Differentiation

Description Usage Arguments Details Value References See Also Examples

This function produces bootstrap samples from a genind object, with each subpopulation resampled according to its size. Because there are many statistics that you may wish to calculte from these samples, this function returns a list of genind objects representing bootsrap samples that can then be futher processed (see examples).

1	chao_bootstrap(x, nreps = 1000)

`x`	genind object (from package adegenet)
`nreps`	numeric number of bootstrap replicates to perform (default 1000)

You should note, this is a standard (frequentist) approach to quantifying uncertainty - effectively asking "if the population was exactly like our sample, and we repeatedly took samples like this from it, how much would those samples vary?" The confidence intervals don't include uncertainty produced from any biases in the way you collected your data. Additionally, this boostrapping procedure displays a slight upward bias for some datasets. If you plan or reporting a confidence interval for your statistic, it is probably a good idea to subtract the difference between the point estimate of the statistic and the mean of the boostrap distribution from the extremes of the interval (as demonstrated in the expample below)

A list of genind objects

Chao, A. et al. (2008). A Two-Stage probabilistic approach to Multiple-Community similarity indices. Biometrics, 64:1178-1186

Other resample: jacknife_populations, summarise_bootstrap

## Not run:   
data(nancycats)
obs.D <- D_Jost(nancycats)
bs <- chao_bootstrap(nancycats)
bs_D <- summarise_bootstrap(bs, D_Jost)
bias <- bs.D$summary.global.het[1] - obs.D$global.het
bs.D$summary.global.het - bias

## End(Not run)