Mark-Recapture Distance Sampling (MRDS) Analysis of Removal Observer Configuration with Full Independence
# S3 method for rem.fi
ddf(
dsmodel = NULL,
mrmodel,
data,
method,
meta.data = list(),
control = list(),
call = ""
)result: an rem.fi model object
not used
mark-recapture model specification
analysis dataframe
analysis method; only needed if this function called from
ddf.io
list containing settings controlling data structure
list containing settings controlling model fitting
original function call used to call ddf
Jeff Laake
The mark-recapture data derived from an removal observer distance sampling survey can only derive conditional detection functions (p_j(y)) for both observers (j=1) because technically it assumes that detection probability does not vary by occasion (observer in this case). It is a conditional detection function because detection probability for observer 1 is conditional on the observations seen by either of the observers. Thus, p_1(y) is estimated by p_1|2(y).
If detections by the observers are independent (full independence) then p_1(y)=p_1|2(y) and for the union, full independence means that p(y)=p_1(y) + p_2(y) - p_1(y)*p_2(y) for each distance y. In fitting the detection functions the likelihood from Laake and Borchers (2004) are used. That analysis does not require the usual distance sampling assumption that perpendicular distances are uniformly distributed based on line placement that is random relative to animal distribution. However, that assumption is used in computing predicted detection probability which is averaged based on a uniform distribution (see eq 6.11 of Laake and Borchers 2004).
For a complete description of each of the calling arguments, see
ddf. The argument model in this function is the same
as mrmodel in ddf. The argument dataname is the name
of the dataframe specified by the argument data in ddf. The
arguments control,meta.data,and method are defined the
same as in ddf.
Laake, J.L. and D.L. Borchers. 2004. Methods for incomplete detection at distance zero. In: Advanced Distance Sampling, eds. S.T. Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.
ddf.io,rem.glm