nonparboot-methods: Nonparametric bootstrapping in unmarked

Description Details Methods Examples

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

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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")

Example output

Loading required package: reshape
Loading required package: lattice
Loading required package: parallel
Loading required package: Rcpp

Call:
multinomPois(formula = ~1 ~ ufc + trba, data = ovenFrame)

Abundance:
            Estimate    SE      z P(>|z|)
(Intercept)    0.102 0.119  0.864   0.388
ufc            0.100 0.126  0.794   0.427
trba          -0.171 0.135 -1.262   0.207

Detection:
 Estimate    SE    z P(>|z|)
    0.288 0.233 1.24   0.217

AIC: 326.1387 
              lambda(Int)   lambda(ufc) lambda(trba)        p(Int)
lambda(Int)   0.014052472 -1.228179e-03 2.364962e-03 -4.323007e-03
lambda(ufc)  -0.001228179  1.594197e-02 8.057614e-03  3.653607e-12
lambda(trba)  0.002364962  8.057614e-03 1.831812e-02  4.763192e-13
p(Int)       -0.004323007  3.653606e-12 4.763191e-13  5.415986e-02
              lambda(Int)  lambda(ufc) lambda(trba)       p(Int)
lambda(Int)   0.026495725 -0.003251525  0.004810396 -0.005644345
lambda(ufc)  -0.003251525  0.013235439  0.007029157 -0.004364993
lambda(trba)  0.004810396  0.007029157  0.012199172 -0.005716476
p(Int)       -0.005644345 -0.004364993 -0.005716476  0.050863583
           [,1]
[1,] 0.01724521
           [,1]
[1,] 0.03251559
[1] 0.1803208

unmarked documentation built on May 27, 2021, 5:07 p.m.