Description Usage Arguments Details Author(s) References See Also Examples
Performs the scatter diagrams of objects of class enfa
.
1 2 3 4 5 |
x |
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
pts |
logical. Whether the points should be drawn. If
|
nc |
whether or not the niche center should be displayed |
percent |
100 minus the proportion of outliers to be excluded from the computation of the minimum convex polygons |
clabel |
a character size for the columns |
side |
if |
Adensity |
the density of shading lines, in lines per inch, for the
available pixels polygon. See |
Udensity |
the density of shading lines, in lines per inch, for the
used pixels polygon. See |
Aangle |
the slope of shading lines, given as an angle in degrees (counter-clockwise), for the available pixels polygon |
Uangle |
the slope of shading lines, given as an angle in degrees (counter-clockwise), for the used pixels polygon |
Aborder |
the color for drawing the border of the available pixels
polygon. See |
Uborder |
the color for drawing the border of the used pixels polygon.
See |
Acol |
the color for filling the available pixels polygon.
if |
Ucol |
the color for filling the used pixels polygon.
if |
Alty |
the line type for the available pixels polygon, as in |
Ulty |
the line type for the used pixels polygon, as in |
Abg |
if |
Ubg |
if |
Ainch |
if |
Uinch |
if |
... |
further arguments passed to or from other methods |
scatter.enfa
displays a factorial map of pixels, as well as the
projection of the vectors of the canonical basis multiplied by a
constant of rescaling.
The kept axes for the plot are specified in a corner.
Mathieu Basille basille@ase-research.org
Basille, M., Calenge, C., Marboutin, E., Andersen, R. & Gaillard, J.M. (2008) Assessing habitat selection using multivariate statistics: Some refinements of the ecological-niche factor analysis. Ecological Modelling, 211, 233–240.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | data(lynxjura)
map <- lynxjura$map
## We keep only "wild" indices.
tmp <- lynxjura$locs[,4]!="D"
locs <- lynxjura$locs[tmp, c("X","Y")]
## We perform a square root transformation
## of the variable to normalize it
map[,4] <- sqrt(map[,4])
## We perform the ENFA
tmp <- data2enfa(map, locs[tmp, c("X","Y")])
(enfa1 <- enfa(dudi.pca(tmp$tab, scannf=FALSE),
tmp$pr, scannf = FALSE))
scatter(enfa1)
|
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