EDR: Effective Detection Radius (EDR) for point transects

In Rdistance: Density and Abundance from Distance-Sampling Surveys

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 associated with covariate values in newdata. Length of return in this case is 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. Length of return in this case is the number of detected groups. The returned vector has measurement units, i.e., object$outputUnits.

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 Jan. 10, 2026, 1:07 a.m.