Performs density and abundance estimation for one bootstrap iteration.
oneBsIter(
object,
area,
propUnitSurveyed,
pb,
plot.bs,
plotCovValues,
warn = FALSE,
asymptoticSE = FALSE
)A data frame containing density and abundance and other relevant statistics for one iteration of the bootstrap.
An Rdistance model frame or fitted distance function,
normally produced by a call to dfuncEstim.
A scalar containing the total area of inference. Usually, this is
study area size. If area is NULL (the default),
area will be set to 1 square unit of the output units and density estimates
will be produced.
If area is not NULL, it must have measurement units
assigned by the units package.
The units on area must be convertible
to squared output units. Units
on area must be two-dimensional.
For example, if output units are "foo",
units on area must be convertible to "foo^2" by the units
package. Units of "km^2", "cm^2", "ha", "m^2", "acre", "mi^2", and several
others are acceptable.
A scalar or vector of real numbers between 0 and 1.
The proportion of the default sampling unit that
was surveyed. If both sides of line transects were observed,
propUnitSurveyed
= 1. If only a single side of line transects were observed, set
propUnitSurveyed = 0.5. For point transects, this should be set to
the proportion of each circle that was observed. Length must either be
1 or the total number of transects in x.
A progress bar created with progress::progress_bar$new().
Logical. Whether to plot bootstrap estimate of detection function.
A plot must already exist because this uses lines.
Data frame containing values of covariates to plot.
Ignored if plot.bs is FALSE.
A logical scalar specifying whether to issue
an R warning if the estimation did not converge or if one
or more parameter estimates are at their boundaries.
For estimation, warn should generally be left at
its default value of TRUE. When computing bootstrap
confidence intervals, setting warn = FALSE
turns off annoying warnings when an iteration does
not converge. Regardless of warn, after
completion all messages about
convergence and boundary conditions are printed
by print.dfunc, print.abund, and
plot.dfunc.
Logical variable for whether to calculate
asymptotic standard errors. The default (TRUE) estimates an
asymptotic variance-covariance matrix for parameters based on the
likelihood's Hessian (2nd derivative). If maximization
has been performed by Nlminb or HookesJeeves, the asymptotic
Hessian is estimated using numeric second deriviatives
of the likelihood at the maximum likelihood solution. If
maximization was performed by Optim, the last Hessian of
the maximization is returned
by Optim and used
(see varcovarEstim and secondDeriv).
Asymptotic standard errors will not be estimated if
asymptoticSE = FALSE. If not estimated,
bootstrap iterations will run faster because the numeric Hessian,
which is discarded during bootstrapping,
will not be calculated every iteration.
bootstrap; abundEstim