| traps {secr} | R Documentation |
Detector Array
Description
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.
Usage
traps(object, ...)
traps(object) <- value
Arguments
object |
a |
value |
|
... |
other arguments (not used). |
Details
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" etc.; see detector for
more).
Other possible attributes of a traps object are:
spacing | mean distance to nearest detector |
spacex | |
spacey | |
covariates | dataframe of trap-specific covariates |
clusterID | identifier of the cluster to which each detector belongs |
clustertrap | sequence number of each trap within its cluster |
usage | a traps x occasions matrix of effort (may be binary 0/1) |
markocc | integer vector distinguishing marking occasions (1) from sighting occasions (0) |
newtrap | vector recording aggregation of detectors by
reduce.traps |
If usage is specified, at least one detector must be ‘used’ (usage non-zero) on each occasion.
Various array geometries may be constructed with functions such as
make.grid and make.circle, and these may be
combined or placed randomly with trap.builder.
Note
Generic methods are provided to select rows
(subset.traps), combine two or more arrays
(rbind.traps), aggregate detectors
(reduce.traps), shift an array
(shift.traps), or rotate an array
(rotate.traps).
The attributes usage and covariates may be extracted or
replaced using generic methods of the same name.
References
Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://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.
See Also
make.grid, read.traps,
rbind.traps, reduce.traps,
plot.traps, secr.fit,
spacing, detector,
covariates, trap.builder,
as.mask
Examples
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))