Rsurface {secr} | R Documentation

## Smoothed Resource Surface

### Description

Creates a smoothed resource surface from a covariate of a mask. Smoothing entails summing the value in each pixel weighted by a detection kernel centred on the focal pixel. The detection kernel represents home-range utilization with spatial scale sigma. The resulting surface is equivalent to the denominator used by Royle et al. (2013) to normalize site-specific detection.

### Usage

    Rsurface(mask, sigma, usecov = NULL, alpha2 = 1, detectfn = 'HHN', z = 1,
     inverse = FALSE, scale = TRUE)

### Arguments

`mask` | secr habitat mask object (single-session)
`sigma` | numeric spatial scale of home range model
`alpha2` | numeric coefficient of spatial covariate
`usecov` | character name of resource covariate
`detectfn` | integer or character code for detection function
`z` | numeric shape parameter of home range model
`inverse` | logical; if TRUE the reciprocal of smoothed resource is returned
`scale` | logical; not used

### Details

`detectfn` may be uniform (‘UN’) or one of the cumulative-hazard functions (‘HHN’, ‘HHR’, ‘HEX’, ‘HAN’, ‘HCG’) (or integer codes 4, 14:18; see detectfn).

The default ‘HHN’ corresponds to a halfnormal function on the hazard scale, or a bivariate circular normal home range.

If `usecov` is not named then it takes the value 1.0 for all points on the mask and zero otherwise.

The Rsurface can be used implicitly to normalize detection probability when fitting a model with detector-specific covariate equal to `usecov` (see details, but the process is intricate and not fully documented).

### Value

An object with class c(‘Rsurface’, ‘mask’, ‘data.frame’) and covariate ‘Resource’ (other covariates are retained from the input mask). The attribute ‘scale’ is 1.0 if `scale = FALSE`; otherwise it is the average of the resource over the masked area.

### Note

Consider a focal pixel s and another point in the habitat mask x, with distance d = |x-s|. Weights are given by a kernel f(d). Typically the kernel will be halfnormal f(d) = exp(-d^2/(2 σ^2)) (detectfn = ‘HHN’) or exponential exp(-d/σ) (detectfn = ‘HEX’) (see detectfn for other possibilities).

If z(x) represents the covariate value at point x, the summed resource availability at s is given by

R(s) = ∑ f(d) exp(α2 z(x)).

This corresponds to the denominator of eqn 4 in Royle et al. (2013).

By default, the numerical values reported by `Rsurface` are not raw R values. If `scale = TRUE`, values are standardized by dividing by the mean: R'(s) = R(s) / (∑ R(s) / n) where n is the number of pixels. Values of R'(s) are centred on 1.0.

If `inverse = TRUE`, the numeric values are 1 / R'(s) or 1 / R(s) as determined by `scale`.

### References

Royle, J. A., Chandler, R. B., Sun, C. C. and Fuller, A. K. (2013) Integrating resource selection information with spatial capture–recapture. Methods in Ecology and Evolution 4, 520–530.

### See Also

`mask`, `plot.Rsurface`, `spotHeight`, details

### Examples

    ## create binary covariate (0 outside habitat)
    msk <- make.mask(traps(possumCH), buffer = 800)
    covariates(msk) <- data.frame(z = as.numeric(pointsInPolygon
        (msk,possumarea)))

    ## derive and plot "resource availability"
    Rs <- Rsurface(msk, sigma = 100, usecov = 'z')
    plot(Rs, plottype = 'contour', col = topo.colors(10))
    lines(possumarea)

    ## Not run:

    spotHeight(Rs, dec = 2)

    ## End(Not run)

[Package secr version 3.2.1 Index]