MoosePopR_DomStrat_bootrep: Generate a bootstrap replicate of data for call to...

In SightabilityModel: Wildlife Sightability Modeling

Generate a bootstrap replicate of data for call to MoosePopR_DomStrat()

Description

This function takes the data from a classical/domain stratification and generates a bootstrap replicate suitable for analysis using MoosePopR_DomStrat(). A sightability model is allowed which "adjusts" the input data for sightability. This can also be used for SightabilityPopR() models by forcing block areas to 1 and the total block area in stratum to the number of blocks to mimic a mean-per-unit estimator. See the vignette for examples of usage.

Usage

MoosePopR_DomStrat_bootrep(
  stratum.data,
  selected.unit.data,
  waypoint.data,
  density = NULL,
  abundance = NULL,
  numerator = NULL,
  denominator = NULL,
  sight.model = NULL,
  sight.beta = NULL,
  sight.beta.cov = NULL,
  stratum.var = "Stratum",
  domain.var = "Domain",
  stratum.total.blocks.var = "Total.Blocks",
  stratum.total.area.var = "Total.Area",
  block.id.var = "Block.ID",
  block.area.var = "Block.Area",
  conf.level = 0.9,
  survey.lonely.psu = "fail",
  check.args = TRUE
)

Arguments

stratum.data	A data frame containing for each combination of stratum and domain, the stratum id (see stratum.var), the domain id (see domain.var), the total number of blocks in the stratum (see stratum.total.blocks.var) and the total area of the stratum (see stratum.total.area.var)
selected.unit.data	A data frame containing information on the selected survey units. Required variables are the stratum (see stratum.var), domain (see domain.var), block.id (see block.id.var), and the area of the block (see block.area.var).
waypoint.data	A data frame containing counts of moose in each group along with a variable identifying the stratum (see stratum.var), domain (see domain.var) and block (see block.id.var). Additional variables can be included such as covariates for the sightability function (not currently used in MoosePopR)
density, abundance, numerator, denominator	Right-handed formula identifying the variable(s) in the waypoint data frame for which the density, abundance, or ratio (numerator/denominator) are to be estimated.
sight.model	A formula that identifies the model used to estimate sightability. For example observed ~ VegCoverClass would indicate that sightability is a function of the VegCoverClass variable in the survey data. The left hand variable is arbitrary. The right hand variables must be present in the survey.data data frame.
sight.beta	The vector of estimated coefficients for the logistic regression sightability model.
sight.beta.cov	The covariance matrix of sight.beta
stratum.var	Name of the variable in the data frames that identifies the classical stratum
domain.var	Name of the variable in the data frames that identifies the domain.
stratum.total.blocks.var	Name of the variable in the stratum.data data frame that contains the total number of blocks in the stratum.
stratum.total.area.var	Name of the variable in the stratum.data data.frame that contains the total stratum area.
block.id.var	Name of the variable in the data frames that identifies the block.id (the sampling unit)
block.area.var	Name of the variable in data frames that contains the area of the blocks (area of sampling unit)
conf.level	Confidence level used to create confidence intervals.
survey.lonely.psu	How to deal with lonely PSU within strata. See surveyoptions in the survey package.
check.args	Should arguments be checked. Turn off for extensive bootstrapping to save time.

Value

A list containing the input data (input.data), the bootstrap replicate (boot.data), and a data frame (boot.res) with the estimated density, or abundance or ratio along with its estimated standard error and large-sample normal-based confidence interval. The density/abundance/ratio over all strata is also given on the last line of the data.frame.

Author(s)

Schwarz, C. J. [e-mail].

References

To Be Added.

Examples

 
##---- See the vignettes for examples on how to use this function

SightabilityModel documentation built on Aug. 20, 2023, 1:08 a.m.