nonparboot-methods: Nonparametric bootstrapping in unmarked
In ianfiske/unmarked: Models for Data from Unmarked Animals

Description Details Methods Examples

Call nonparboot on an unmarkedFit to obtain non-parametric bootstrap samples. These can then be used by vcov in order to get bootstrap estimates of standard errors.

Calling nonparboot on an unmarkedFit returns the original unmarkedFit, with the bootstrap samples added on. Then subsequent calls to vcov with the argument method="nonparboot" will use these bootstrap samples. Additionally, standard errors of derived estimates from either linearComb or backTransform can be instructed to use bootstrap samples by providing the argument method = "nonparboot".

signature(object = "unmarkedFit"): Obtain nonparametric bootstrap samples for a general unmarkedFit.
signature(object = "unmarkedFitColExt"): Obtain nonparametric bootstrap samples for colext fits.
signature(object = "unmarkedFitDS"): Obtain nonparametric bootstrap samples for a distsamp fits.
signature(object = "unmarkedFitMPois"): Obtain nonparametric bootstrap samples for a distsamp fits.
signature(object = "unmarkedFitOccu"): Obtain nonparametric bootstrap samples for a occu fits.
signature(object = "unmarkedFitOccuRN"): Obtain nonparametric bootstrap samples for a occuRN fits.
signature(object = "unmarkedFitPCount"): Obtain nonparametric bootstrap samples for a pcount fits.

data(ovendata)
ovenFrame <- unmarkedFrameMPois(ovendata.list$data,
siteCovs=as.data.frame(scale(ovendata.list$covariates[,-1])), type = "removal")
(fm <- multinomPois(~ 1 ~ ufp + trba, ovenFrame))
fm <- nonparboot(fm, B = 20) # should use larger B in real life.
vcov(fm, method = "hessian")
vcov(fm, method = "nonparboot")
avg.abundance <- backTransform(linearComb(fm, type = "state", coefficients = c(1, 0, 0)))

## Bootstrap sample information propagates through to derived quantities.
vcov(avg.abundance, method = "hessian")
vcov(avg.abundance, method = "nonparboot")
SE(avg.abundance, method = "nonparboot")