# ddf.rem.fi: Mark-Recapture Distance Sampling (MRDS) Removal - FI In mrds: Mark-Recapture Distance Sampling

 ddf.rem.fi R Documentation

## Mark-Recapture Distance Sampling (MRDS) Removal - FI

### Description

Mark-Recapture Distance Sampling (MRDS) Analysis of Removal Observer Configuration with Full Independence

### Usage

``````## S3 method for class 'rem.fi'
ddf(
dsmodel = NULL,
mrmodel,
data,
method,
meta.data = list(),
control = list(),
call = ""
)
``````

### Arguments

 `dsmodel` not used `mrmodel` mark-recapture model specification `data` analysis dataframe `method` analysis method; only needed if this function called from `ddf.io` `meta.data` list containing settings controlling data structure `control` list containing settings controlling model fitting `call` original function call used to call `ddf`

### Details

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`.

### Value

result: an rem.fi model object

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.

`ddf.io`,`rem.glm`