calcAC: Calculate the area correction value(s) with confidence intervals

Description

Use a fitted carcass density distribution and data describing the search area to calculate area correction values and confidence intervals using a parametric bootstrap approach.

Usage

calcAC(
  distribution,
  paramVec,
  varcovVec = NULL,
  proportionSearchDF,
  distanceCol,
  proportionCol,
  additionalCol = NULL,
  nBoot = NULL,
  truncBounds = NULL,
  ciLevel = 0.9,
  randomSeed = NULL,
  ...
)

Value

windAC object

Arguments

distribution: Character indicating the distribution, passed to getDistanceProbability.
paramVec: Numeric vector for the parameters associated with distribution. Assumed to be in the same order as the function indicated by distribution.
varcovVec: Numeric vector for the variances and covariances for paramVec, default is NULL, see details.
proportionSearchDF: Data frame with at least two columns: distance of the outer edge of an annulus from turbine and proportion of area searched within each annulus.
distanceCol: Character indicating the column name for the distance from turbine
proportionCol: Character indicating the column name for the proportion of area searched.
additionalCol: Character vector, default is NULL, indicating additional columns of how the area correction value should be calculated, see details and examples.
nBoot: Integer, indicating the number of parametric bootstrap replicates to use. Default is NULL, and not confidence intervals are produced.
truncBounds: Numeric, indicating bounds for the area correction calculation, see details. Default is NULL, and the bounds are set to c(0,Inf).
ciLevel: Numeric, default is 0.9, desired confidence level for the bootstrap confidence interval.
randomSeed: Numeric value of random seed, default is NULL.
...: Additional arguments passed to getDistanceProbability and rmvnorm.

Details

The function getDistanceProbability is used to calculate the probability (fraction of carcasses) in the intervals between distances in proportionSearchDF.

The truncBounds argument defaults to zero as a lower bound and infinity as the upper bound. If a single value is provided, it is assumed as the upper bound with zero as the lower bound. If two or more values are provided, the max(truncBounds) is the upper bound and min(truncBounds) is the lower bound.

If varcovVec is NULL, then parametric bootstrapping is impossible and a confidence interval is not estimated. The varcovVec should be in such an order that correctly fills the lower triangle including the diagonal. The first column is filled, then the second, and so on. This forms the variance-covariance matrix for the parameters.

If nBoot is greater than zero, a parametric bootstrap is done. Bootstrap parameters are generated using the rmvnorm function.

If the additionalCol argument is not NULL, separate area corrections are estimated for each unique value within the column.

Examples

Run this code


## proportion of area searched data
data(proportionAreaSearched)

## no parametric bootstrap
noBootstrap <- calcAC(distribution = 'gamma',
                      paramVec = c(2.483323, 0.02495139),
                      varcovVec = NULL,
                      proportionSearchDF = proportionAreaSearched,
                      distanceCol = 'distanceFromTurbine',
                      proportionCol = 'proportionAreaSearched',
                      additionalCol = 'plotType')

## with a parametric bootstrap

withBootstrap <- calcAC(distribution = 'gamma',
                        paramVec = c(2.483323, 0.02495139),
                        varcovVec = c(0.041189428, 0.0008825275, 2.118081e-05),
                        proportionSearchDF = proportionAreaSearched,
                        distanceCol = 'distanceFromTurbine',
                        proportionCol = 'proportionAreaSearched',
                        additionalCol = 'plotType',
                        nBoot = 10)