R: Nonparametric bootstrapping in unmarked

nonparboot-methods {unmarked} | R Documentation

## Nonparametric bootstrapping in unmarked

### Description

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.

### Details

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"`.

For `occu` and `occuRN` both sites and occassions are re-sampled. For all other fitting functions, only sites are re-sampled.

### Methods

`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 = "unmarkedFitOccuPEN")`

  Obtain nonparametric bootstrap samples for an occuPEN fit.

`signature(object = "unmarkedFitOccuPEN_CV")`

  Obtain nonparametric bootstrap samples for occuPEN_CV fit.

`signature(object = "unmarkedFitOccuRN")`

  Obtain nonparametric bootstrap samples for a occuRN fits.

`signature(object = "unmarkedFitPCount")`

  Obtain nonparametric bootstrap samples for a pcount fits.

### Examples

    data(ovendata)
    ovenFrame <- unmarkedFrameMPois(ovendata.list$data,
    siteCovs=as.data.frame(scale(ovendata.list$covariates[,-1])), type = "removal")
    (fm <- multinomPois(~ 1 ~ ufc + 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")

[Package unmarked version 1.0.0 Index]