traps: 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.

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. 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.

Examples

Run this code

# NOT RUN {
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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