oneBsIter: Calculations for one bootstrap iteration

In Rdistance: Density and Abundance from Distance-Sampling Surveys

Calculations for one bootstrap iteration

Description

Performs density and abundance estimation for one bootstrap iteration.

Usage

oneBsIter(
  object,
  area,
  propUnitSurveyed,
  pb,
  plot.bs,
  plotCovValues,
  warn = FALSE,
  asymptoticSE = FALSE
)

Arguments

object	An Rdistance model frame or fitted distance function, normally produced by a call to dfuncEstim.
area	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.
propUnitSurveyed	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.
pb	A progress bar created with progress::progress_bar$new().
plot.bs	Logical. Whether to plot bootstrap estimate of detection function. A plot must already exist because this uses lines.
plotCovValues	Data frame containing values of covariates to plot. Ignored if plot.bs is FALSE.
warn	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.
asymptoticSE	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.

Value

A data frame containing density and abundance and other relevant statistics for one iteration of the bootstrap.

See Also

bootstrap; abundEstim

Rdistance documentation built on Jan. 10, 2026, 1:07 a.m.