EDR: EDR - Effective Detection Radius (EDR) for point transects

In Rdistance: Density and Abundance from Distance-Sampling Surveys

EDR - Effective Detection Radius (EDR) for point transects

Description

Computes Effective Detection Radius (EDR) for estimated detection functions on point transects. See ESW is for line transects.

Usage

EDR(object, newdata = NULL)

Arguments

object

An Rdistance model frame or fitted distance function, normally produced by a call to dfuncEstim.

newdata

A data frame containing new values of the covariates at which to evaluate the distance functions. If newdata is NULL, distance functions are evaluated at values of the observed covariates and results in one prediction per distance or transect (see parameter type). If newdata is not NULL and the model does not contains covariates, this routine returns one prediction for each row in newdata, but columns and values in newdata are ignored.

Details

Effective Detection Radius is the integral under the detection function times distance.

Value

If newdata is present, the returned value is a vector of effective sampling distances for values of the covariates in newdata with length equal to the number of rows in newdata. If newdata is NULL, the returned value is a vector of effective sampling distances associated with covariate values in object and has the same number of detected groups. The returned vector has measurement units, i.e., object$outputUnits.

Numeric Integration

Rdistance uses Simpson's composite 1/3 rule to numerically integrate under distance functions. The number of points evaluated during numerical integration is controlled by options(Rdistance_intEvalPts) (default 101). Option 'Rdistance_intEvalPts' must be odd because Simpson's rule requires an even number of intervals (hence, odd number of points). Lower values of 'Rdistance_intEvalPts' increase calculation speeds; but, decrease accuracy. 'Rdistance_intEvalPts' must be >= 5. A warning is thrown if 'Rdistance_intEvalPts' < 29. Empirical tests by the author suggest 'Rdistance_intEvalPts' values >= 30 are accurate to several decimal points and that all 'Rdistance_intEvalPts' >= 101 produce identical results in all but pathological cases.

See Also

dfuncEstim, ESW, effectiveDistance

Examples

# Load example thrasher data (point transect survey type)
data(thrasherDf)

# Fit half-normal detection function
dfunc <- thrasherDf |> dfuncEstim(formula=dist~bare)

# Compute effective detection radius (EDR)
EDR(dfunc) # vector length 192
effectiveDistance(dfunc) # same
EDR(dfunc, newdata = data.frame(bare=30)) # vector length 1
  

Rdistance documentation built on April 12, 2025, 1:12 a.m.