| kselect {adehabitat} | R Documentation |
Performs a multivariate analysis of ecological data (K-select analysis).
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, ...)
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 |
kselect returns a list of the class kselect and
dudi (see dudi).
Clément Calenge calenge@biomserv.univ-lyon1.fr
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.
sahrlocs2kselect for
conversion of objects class sahrlocs to objects suitable for a
K-select analysis, s.distri, and
dudi for class dudi.
## 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)