bootstrap.p.with.Et: Detection probability bootstrap with availability process...

In david-borchers/hmltm: Line transect estimation with hidden Markov availability models

Detection probability bootstrap with availability process times.

Description

Nonparametric bootstrap of detection data with estimation of detection probabilities. If fixed.avail=FALSE, does parametric resampling of mean times available and unavailable for every resample of detection data, else treats these mean times as fixed.

Usage

bootstrap.p.with.Et(
  dat,
  pars,
  hfun,
  models,
  survey.pars,
  hmm.pars,
  control.fit,
  control.opt,
  fixed.avail = FALSE,
  B = 999
)

Arguments

dat	detection data frame constructed by removing all rows with no detections from a data frame of the sort passed to est.hmltm.
pars	starting parameter values, as for est.hmltm.
hfun	detection hazard function name; same as argument FUN of est.hmltm.
models	detection hazard covariate models, as for est.hmltm.
survey.pars	survey parameters, as for est.hmltm.
hmm.pars	availability hmm parameters, as for est.hmltm. Must have elements $Et and $Sigma.Et
control.fit	list controlling fit, as for est.hmltm.
control.opt	list controlling function optim, as for est.hmltm.
fixed.avail	if TRUE, hmm.pars is treated as fixed, else element $Et is parametrically resampled.
B	number of bootstrap replicates.

Details

The rows of data frame dat are resampled with replacement to create new data frames with as many detections as were in dat. If fixed.avail=TRUE, then a pair of new mean times available and unavailable ($Ets) are generated for each resampled data frame, by resampling parametrically from a logNormal distribution with mean hmm.pars$Et and variance-covariance matrix hmm.pars$Sigma.Et.

Function fit.hmltm is called to estimate detection probabilities and related things for every bootstrap resample.

Value

A list with the following elements:

• callist: input reflection: everything passed to the function, bundled into a list

• bs: a list containing (a) a Bxn matrix $phats in which each row is the estimated detection probabilities for each of the n bootstrapped detections, (b) a Bxn matrix $pars in which each row is the estimated detection hazard parameters, (c) the following vectors of length B with estimates from each bootstrap: $p0 (mean estimated p(0) over all detections), $phat (mean estimated detection probability over all detections), and (d) a Bx2 matrix $b.Et in which each row is the mean times unavailable and available.

david-borchers/hmltm documentation built on Oct. 29, 2023, 9:07 p.m.