# R/effectiveDistance.R In Rdistance: Distance-Sampling Analyses for Density and Abundance Estimation

```#' @title Calculates the effective sampling distance for
#' estimated detection functions
#'
#' @description Computes Effective Strip Width (ESW) for line-transect detection
#'   functions, or the analogous Effective Detection Radius (EDR) for point-transect
#'   detection functions.
#'
#' @param obj An estimated detection function object.  An estimated detection
#'   function object has class 'dfunc', and is usually produced by a call to
#'   \code{dfuncEstim}. The estimated detection function may optionally contain
#'   a \eqn{g(0)} component.  If no \eqn{g(0)} component is found, \eqn{g(0)} =
#'   1 is assumed.
#'
#' @param newdata A data frame containing new values of the covariates at which
#'   ESW's or EDR's are sought. If NULL or missing and
#'   \code{obj} contains covariates, the
#'   covariates stored in
#'   \code{obj} are used. See \bold{Value} section.
#'
#'
#' @return If \code{newdata} is not missing or NULL and
#' covariates are present in \code{obj}, returned value is
#' a vector with length equal to the number of rows in \code{newdata}.
#' If \code{newdata} is missing or NULL and covariates are present
#' in \code{obj}, returned value is a vector with length equal to
#' the number of detections in \code{obj\$dist}. In either of the
#' above cases, elements in the returned vector are
#' the effective sampling distances for the corresponding set of
#' covariates.
#'
#' If \code{obj} does not contain covariates, \code{newdata} is ignored and
#' a scalar equal to the (constant) effective sampling distance for all
#' detections is returned.
#'
#' @keywords modeling
#' @export

effectiveDistance <- function(obj, newdata = NULL){

# call ESW for line transects and EDR for point transects

if (obj\$pointSurvey) {
EDR(obj, newdata)
} else {
ESW(obj, newdata)
}

}
```

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Rdistance documentation built on May 2, 2019, 3:49 a.m.