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mrds

ddf.io

Mark-Recapture Distance Sampling (MRDS) IO - PI

Description

Mark-Recapture Distance Sampling (MRDS) Analysis of Independent Observer Configuration and Point Independence

Usage

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

Arguments

`dsmodel`	distance sampling model specification; model list with key function and scale formula if any
`mrmodel`	mark-recapture model specification; model list with formula and link
`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`

Details

MRDS analysis based on point independence involves two separate and independent analyses of the mark-recapture data and the distance sampling data. For the independent observer configuration, the mark-recapture data are analysed with a call to ddf.io.fi (see likelihood eq 6.8 and 6.16 in Laake and Borchers 2004) to fit conditional distance sampling detection functions to estimate p(0), detection probability at distance zero for the independent observer team based on independence at zero (eq 6.22 in Laake and Borchers 2004). Independently, the distance data, the union of the observations from the independent observers, 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 observer team is then created as p(y)=p(0)*g(y) (eq 6.28 of Laake and Borchers 2004) from which predictions are made. ddf.io is not called directly by the user and is called from ddf with method="io".

For a complete description of each of the calling arguments, see ddf. The argument dataname is the name of the dataframe specified by the argument data in ddf. The arguments dsmodel, mrmodel, control and meta.data are defined the same as in ddf.

Value

result: an io model object which is composed of io.fi and ds model objects

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.