kselect {adehabitat} | R Documentation

## K-Select Analysis: a Method to Analyse the Habitat Selection by Animals

### Description

Performs a multivariate analysis of ecological data (K-select analysis).

### Usage

    kselect(dudi, factor, weight, scannf = TRUE, nf = 2, ewa = FALSE)
    print.kselect(x, ...)
    kplot.kselect(object, xax = 1, yax = 2, csub = 2, possub = c("topleft",
                  "bottomleft", "bottomright", "topright"),
                  addval = TRUE, cpoint = 1, csize = 1, clegend = 2, ...)
    hist.kselect(x, xax = 1, mar = c(0, 0, 0, 0), ampl = 1,
                 col.out = gray(0.75), col.in = gray(0.75), ncell = TRUE,
                 denout = NULL, denin = NULL, lwdout = 1, lwdin = 1,
                 maxy = 1, csub = 2, possub =
                 c("bottomleft", "topleft", "bottomright", "topright"),
                 ncla = 15, ...)
    plot.kselect(x, xax = 1, yax = 2, ...)

### Arguments

`dudi` | an object of class `dudi`
`factor` | a factor with the same length as `nrow(dudi$tab)`
`weight` | a numeric vector of integer values giving the weight associated to the rows of `dudi$tab`.
`scannf` | logical. Whether the eigenvalues bar plot should be displayed
`nf` | if `scannf = FALSE`, an integer indicating the number of kept axes
`ewa` | logical. If `TRUE`, uniform weights are given to all animals in the analysis. If `FALSE`, animal weights are given by the proportion of relocations of each animal (i.e. an animal with 10 relocations has a weight 10 times lower than an animal with 100 relocations)
`x` | an object of class `kselect`
`object` | an object of class `kselect`
`xax` | the column number for the x-axis
`yax` | the column number for the y-axis
`addval` | logical. If `TRUE`, the frequency of the relocations per animal is displayed (see examples)
`cpoint` | the size of the points (if 0, the points where no relocations are found are not displayed)
`mar` | the margin parameter (see `help(par)`).
`ampl` | the amplification factor (i.e. `ylim = c(-1 , 1) / ampl`)
`col.out` | character string. The color of the upper histogram
`col.in` | character string. The color of the lower histogram
`ncell` | logical. If `TRUE`, the histogram shows the distribution of the cells of the raster map where at least one relocation is found. If `FALSE`, the histogram shows the distribution of the relocations
`denout` | the density of shading lines for the upper histogram, in lines per inch (see `help(hist)` for further informations)
`denin` | the density of shading lines for the lower histogram, in lines per inch
`lwdout` | the line width for the upper histogram
`lwdin` | the line width for the lower histogram
`maxy` | the maximum Y coordinate (since the histogram draws frequencies, default value of `maxy` is 1)
`csub` | the character size for the legend, used with `par("cex")*csub`
`csize` | the size coefficient for the points
`clegend` | the character size for the legend used by `par("cex")*clegend`
`possub` | a character string indicating the sub-title position `("topleft", "topright", "bottomleft", "bottomright")`
`ncla` | the number of classes of the histograms
`...` | additional arguments to be passed to the generic function `hist`, `print` or, in the case of `plot.kselect`, `s.distri`

### Value

`kselect` returns a list of the class `kselect` and `dudi` (see `dudi`).

### Author(s)

Clément Calenge calenge@biomserv.univ-lyon1.fr

### References

Calenge, C., Dufour, A.B. and Maillard, D. (2005) K-select analysis: a new method to analyse habitat selection in radio-tracking studies. Ecological modelling, 186, 143–153.

### See Also

`sahrlocs2kselect` for conversion of objects class `sahrlocs` to objects suitable for a K-select analysis, `s.distri`, and `dudi` for class `dudi`.

### Examples

    ## Not run:
    ## Loads the data
    data(puechabon)
    sahr <- puechabon$sahr

    ## prepares the data for the kselect analysis
    x <- sahrlocs2kselect(sahr)
    tab <- x$tab

    ## Example of analysis with two variables: the slope and the elevation.
    ## Have a look at the use and availability of the two variables
    ## for the 4 animals
    tab <- tab[,((names(tab) == "Slope")|(names(tab) == "Elevation"))]
    tab <- scale(tab)
    tmp <- split.data.frame(tab, x$factor)
    wg <- split(x$weight, x$factor)
    opar <- par(mfrow = n2mfrow(nlevels(x$factor)))
    for (i in names(tmp))
      s.distri(scale(tmp[[i]]), wg[[i]])
    par(opar)

    ## We call a new graphic window
    x11()
    ## A K-select analysis
    acp <- dudi.pca(tab, scannf = FALSE, nf = 2)
    kn <- kselect(acp, x$factor, x$weight,
     scannf = FALSE, nf = 2)

    # use of the generic function scatter
    scatter(kn)

    # Displays the first factorial plane
    kplot(kn)
    kplot(kn, cellipse = 0, cpoint = 0)
    kplot(kn, addval = FALSE, cstar = 0)

    # this factorial plane can be compared with
    # the other graph to see the rotation proposed by
    # the analysis
    graphics.off()

    # Displays the first factorial axis
    hist(kn)

    # Displays the second factorial axis
    hist(kn, xax = 2)

    # Summary of the analysis
    plot(kn)
    ## End(Not run)

[Package adehabitat version 1.4 Index]