capthist {secr} | R Documentation

## Spatial Capture History Object

### Description

A `capthist` object encapsulates all data needed by `secr.fit`, except for the optional habitat mask.

### Details

An object of class `capthist` holds spatial capture histories, detector (trap) locations, individual covariates and other data needed for a spatially explicit capture-recapture analysis with `secr.fit`.

For 'single' and 'multi' detectors, `capthist` is a matrix with one row per animal and one column per occasion (i.e. dim(capthist) = c(nc, noccasions)); each element is either zero (no detection) or a detector number. For other detectors ('proximity', 'count', 'signal' etc.), `capthist` is an array of values and dim(capthist) = c(nc, noccasions, ntraps); values maybe binary ({–1, 0, 1}), integer or real depending on the detector type.

Deaths during the experiment are represented as negative values.

Ancillary data are retained as attributes of a `capthist` object as follows:

traps
  object of class `traps` (required)
session
  session identifier (required)
covariates
  dataframe of individual covariates (optional)
cutval
  threshold of signal strength for detection ('signal' only)
detectedXY
  dataframe of coordinates for location within polygon ('polygon' only)

The parts of a capthist object can be assembled with the function `make.capthist`. Use `sim.capthist` for Monte Carlo simulation (simple models only). Methods are provided to display and manipulate `capthist` objects (print, summary, plot, rbind, subset, reduce) and to extract and replace attributes (covariates, traps, xy).

A multi-session `capthist` object is a list in which each component is a `capthist` for a single session. The list maybe derived directly from multi-session input in Density format, or by combining existing `capthist` objects with `MS.capthist`.

### Author(s)

Murray Efford murray.efford@otago.ac.nz

### References

Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.

Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density estimation by spatially explicit capture-recapture: likelihood-based methods. In: D. L. Thomson, E. G. Cooch and M. J. Conroy (eds) Modeling Demographic Processes in Marked Populations. Springer, New York. Pp. 255–269.

### See Also

`traps`, `secr.fit`, `make.capthist`, `sim.capthist`, `subset.capthist`, `rbind.capthist`, `MS.capthist`, `reduce.capthist`, `mask`

[Package secr version 1.3.0 Index]