ddf.io.fi: Mark-Recapture Distance Sampling (MRDS) IO - FI

View source: R/ddf.io.fi.R

ddf.io.fiR Documentation

Mark-Recapture Distance Sampling (MRDS) IO - FI

Description

Mark-Recapture Analysis of Independent Observer Configuration with Full Independence

Usage

## S3 method for class 'io.fi'
ddf(model, data, meta.data = list(), control = list(), call = "", method)

Arguments

model

mark-recapture model specification

data

analysis dataframe

meta.data

list containing settings controlling data structure

control

list containing settings controlling model fitting

call

original function call used to call ddf

method

analysis method; only needed if this function called from ddf.io

Details

The mark-recapture data derived from an independent observer distance sampling survey can be used to derive conditional detection functions (p_j(y)) for both observers (j=1,2). They are conditional detection functions because detection probability for observer j is based on seeing or not seeing observations made by observer 3-j. 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),p_2(y)=p_2|1(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 given by eq 6.8 and 6.16 in Laake and Borchers (2004) is 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.

Value

result: an io.fi model object

Author(s)

Jeff Laake

References

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.

See Also

ddf.io,summary.io.fi,coef.io.fi, plot.io.fi,gof.io.fi,io.glm


mrds documentation built on March 18, 2022, 5:26 p.m.