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 capthist object.
value	traps object to replace previous.
...	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))  

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