A capthist object encapsulates all data needed by
secr.fit, except for the optional habitat mask.
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.
A capthist is primarily an array of values with dim(capthist) = c(nc,
noccasions, ntraps) where nc is the number of detected individuals.
Values maybe binary ({--1, 0, 1}) or integer 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)
signalframe --- signal strength values etc., one row per detection (`signal' only)
detectedXY --- dataframe of coordinates for location within polygon (`polygon'-like detectors only)
xylist --- coordinates of telemetered animals
Tu --- detectors x occasions matrix of sightings of unmarked animals
Tm --- detectors x occasions matrix of sightings of marked but unidentified animals
Tn --- detectors x occasions matrix of sightings with unknown mark status
read.capthist is adequate for most data input. Alternatively, 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.
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.
traps, secr.fit,
read.capthist, make.capthist,
sim.capthist,
subset.capthist, rbind.capthist,
MS.capthist,
reduce.capthist, mask