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 xaxis 
yax 
the column number for the yaxis 
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 (counterclockwise), for the available pixels polygon 
Uangle 
the slope of shading lines, given as an angle in degrees (counterclockwise), 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@aseresearch.org
Basille, M., Calenge, C., Marboutin, E., Andersen, R. & Gaillard, J.M. (2008) Assessing habitat selection using multivariate statistics: Some refinements of the ecologicalniche factor analysis. Ecological Modelling, 211, 233–240.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  data(lynxjura)
map < lynxjura$map
## We keep only "wild" indices.
locs < lynxjura$locs
locs < locs[slot(locs, "data")[,2]!="D",]
hist(map, type = "l")
## The variable artif is far from symetric
## We perform a square root transformation
## of this variable
## We therefore normalize the variable 'artif'
slot(map,"data")[,4] < sqrt(slot(map,"data")[,4])
hist(map, type = "l")
## We prepare the data for the ENFA
tab < slot(map, "data")
pr < slot(count.points(locs, map), "data")[,1]
## We then perform the PCA before the ENFA
pc < dudi.pca(tab, scannf = FALSE)
## We perform the ENFA
(enfa1 < enfa(pc, pr, scannf = FALSE))
scatter(enfa1)

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