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').

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.

### Note

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.

### Author(s)

Murray Efford murray.efford@otago.ac.nz

### References

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.

### See Also

`make.grid`, `read.traps`, `plot.traps`, `secr.fit`

### 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))

[Package secr version 1.3.0 Index]