| traps {secr} | R Documentation |
An object of class traps encapsulates a set of detector (trap)
locations and related data. A method of the same name extracts or
replaces the traps attribute of a capthist object.
traps(object, ...) traps(object) <- value
object |
a capthist object. |
value |
traps object to replace previous. |
... |
other arguments (not used). |
An object of class traps holds detector (trap) locations as a
data frame of x-y coordinates. Trap identifiers are used as row names.
The required attribute 'detector' records the type of detector
('single', 'multi' or 'proximity').
Other possible attributes of a traps object are trap-specific
covariates (covariates) and a matrix of binary (0/1) codes
indicating whether each detector was used on each occasion
(usage). If usage is specified, at least one detector must be
'used' on each occasion.
Generic methods are provided to select rows (subset.traps), combine two or more arrays (rbind.traps), shift an array (shift.traps), and to rotate an array (rotate.traps). The attributes usage and covariates may be extracted or replaced using generic methods of the same name.
Murray Efford murray.efford@otago.ac.nz
Efford, M. G. (2007) Density 4.1: software for spatially explicit capture–recapture. Department of Zoology, University of Otago, Dunedin, New Zealand. http://www.otago.ac.nz/density
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.
make.grid, read.traps, plot.traps, secr.fit
demotraps <- make.grid(nx = 8, ny = 6, spacing = 30)
demotraps ## uses print method for traps
summary (demotraps)
plot (demotraps, border = 50, label = TRUE, offset = 8,
gridlines=FALSE)
## generate an arbitrary covariate 'randcov'
covariates (demotraps) <- data.frame(randcov = rnorm(48))
## overplot detectors that have high covariate values
temptr <- subset(demotraps, covariates(demotraps)$randcov > 0.5)
plot (temptr, add = TRUE,
detpar = list (pch = 16, col = 'green', cex = 2))