Computes effective strip width (ESW) for estimated detection functions from line transect data
An estimated detection function object. An estimated detection
function object has class 'dfunc', and is usually produced by a call to
A data frame containing new values of
the covariates at which ESW's are sought. If NULL or missing and
Effective strip width (ESW) of a distance function is its
integral. That is, ESW is the area under the distance function from its
left-truncation limit (
obj$w.lo) to its right-truncation limit
Under perfect detection, area under the detection function is the entire
the strip transect (from
Under perfect detection, density is the number sighted targets
divided by area surveyed, where area surveyed is
total length of transects.
When detection is not perfect, less than the total half-width is effectively covered. Buckland et al. (1993) show that the denominator of the density estimator in this case involves total length of surveyed transects times area under the detection function (i.e., this integral). By analogy with the perfect detection case, this integral can be viewed as the transect half-width that observers effectively cover. In other words, a survey with imperfect detection and ESW equal to X effectively covers the same area as a study with perfect detection out to a distance of X.
The trapezoid rule is used to numerically integrate under the distance
trapezoids are used in the approximation to speed calculations. In some
rare cases, two hundred trapezoids may not be enough. In these cases, the
code for this function can be
sink-ed to a file, inspected in a text
editor, modified to bump the number of trapezoids, and
newdata is not missing and not NULL and
covariates are present in
obj, returned value is
a vector with length equal to the number of rows in
newdata is missing or NULL and covariates are present
obj, returned value is a vector with length equal to
the number of detections in
obj$dist. In either of the
above cases, elements in the returned vector are
the effective strip widths for the corresponding set of
obj does not contain covariates,
newdata is ignored and
a scalar equal to the (constant) effective strip width for all
detections is returned.
Trent McDonald, WEST Inc., firstname.lastname@example.org
Buckland, S.T., Anderson, D.R., Burnham, K.P. and Laake, J.L. 1993. Distance Sampling: Estimating Abundance of Biological Populations. Chapman and Hall, London.
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# Load example sparrow data (line transect survey type) data(sparrowDetectionData) # Fit half-normal detection function dfunc <- dfuncEstim(formula=dist~1, detectionData=sparrowDetectionData, likelihood="halfnorm", w.hi=100, pointSurvey=FALSE) # Compute effective strip width (ESW) ESW(dfunc) # ESW only applies to line transect surveys # EDR is the point transect equivalent # The effectiveDistance function tests whether the dfunc was # fit to line or point data, and returns either ESW or EDR accordingly effectiveDistance(dfunc)
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