Description Usage Arguments Details Value Author(s)
View source: R/predict.dsmodel.R
Predict detection probabilities (or effective strip widths/effective areas of detection) from a fitted distance sampling model using either the original data (i.e. "fitted" values) or using new data.
1 2 3 4 5 6 7 8 9 
object 

newdata 
new 
compute 
if 
esw 
if 
se.fit 
should standard errors on the predicted probabilities of detection (or ESW if 
... 
for S3 consistency 
For line transects, the effective strip halfwidth (esw=TRUE
) is the integral of the fitted detection function over either 0 to W or the specified int.range
. The predicted detection probability is the average probability which is simply the integral divided by the distance range. For point transect models, esw=TRUE
calculates the effective area of detection (commonly referred to as "nu", this is the integral of 2/width^2 * r * g(r)
.
Fitted detection probabilities are stored in the model
object and these are returned unless compute=TRUE
or newdata
is specified. compute=TRUE
is used to estimate numerical derivatives for use in delta method approximations to the variance.
Note that the ordering of the returned results when no new data is supplied (the "fitted" values) will not necessarily be the same as the data supplied to ddf
, the data (and hence results from predict
) will be sorted by object ID (object
).
a list with a single element: fitted
, a vector of average detection probabilities or esw values for each observation in the original data ornewdata
. If se.fit=TRUE
there is an additional element $se.fit
, which contains the standard errors of the probabilities of detection or ESW.
David L Miller
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