SightabilityPopR_DomStrat: Classical and Domain Stratification using SightabilityPopR()

View source: R/SightabilityPopR_DomStrat.R

SightabilityPopR_DomStratR Documentation

Classical and Domain Stratification using SightabilityPopR()

Description

This function allows for classical or domain stratification when using SightabilityPopR(). Caution **SE are NOT adjusted for measurements on multiple domains on the same sampling unit. Bootstrapping may be required**. Consult the vignette for more details.

SightabilityPopR_DomStrat() adjusts for sightability < 100%.

Usage

SightabilityPopR_DomStrat(
  stratum.data,
  selected.unit.data,
  waypoint.data,
  density = NULL,
  abundance = NULL,
  numerator = NULL,
  denominator = NULL,
  sight.formula = ~1,
  sight.beta = 10,
  sight.beta.cov = matrix(0, nrow = 1, ncol = 1),
  sight.logCI = TRUE,
  sight.var.method = c("Wong", "SS")[1],
  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
)

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.formula

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

sight.logCI

Should confidence intervals for the sightability adjusted estimates be computed using a normal-based confidence interval on log(abundance)

sight.var.method

What method should be used to estimate the variances after adjusting for sightability.

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.

Value

A data frame containing for each stratum and for all combinations of strata and domains (identified as stratum id .OVERALL), the density, or abundance or ratio estimate along with its estimated standard error and large-sample normal-based confidence interval.

Author(s)

Schwarz, C. J. cschwarz.stat.sfu.ca@gmail.com.

References

To Be Added.

Examples

 
##---- See the vignettes for examples on how to run this analysis.


SightabilityModel documentation built on March 7, 2023, 6:15 p.m.