capthist object encapsulates all data needed by
secr.fit, except for the optional habitat mask.
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}) 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 (required)
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