Mark-Recapture Distance Sampling (MRDS) Analysis of Removal Observer Configuration and Point Independence
## S3 method for class 'rem' ddf(dsmodel, mrmodel, data, meta.data = list(), control = list(), call = "")
distance sampling model specification; model list with key function and scale formula if any
mark-recapture model specification; model list with formula and link
list containing settings controlling data structure
list containing settings controlling model fitting
original function call used to call
MRDS analysis based on point independence involves two separate and
independent analyses of the mark-recapture data and the distance sampling
data. For the removal observer configuration, the mark-recapture data are
analysed with a call to
ddf.rem.fi (see Laake and Borchers
2004) to fit conditional distance sampling detection functions to estimate
p(0), detection probability at distance zero for the primary observer based
on independence at zero (eq 6.22 in Laake and Borchers 2004). Independently,
the distance data, the observations from the primary observer, are used to
fit a conventional distance sampling (CDS) (likelihood eq 6.6) or
multi-covariate distance sampling (MCDS) (likelihood eq 6.14) model for the
detection function, g(y), such that g(0)=1. The detection function for the
primary observer is then created as p(y)=p(0)*g(y) (eq 6.28 of Laake and
Borchers 2004) from which predictions are made.
ddf.rem is not called
directly by the user and is called from
For a complete description of each of the calling arguments, see
ddf. The argument
data is the dataframe specified by
ddf. The arguments
meta.data are defined the same as
result: an rem model object which is composed of rem.fi and ds model objects
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.
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