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))

[Package secr version 3.2.1 Index]