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. (submitted). K-select analysis, a new method to analyse habitat selection in radio-tracking studies.
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)