Hi Mark,
I think you should check a bit your data. I have the feeling that there
are several "outliers". You can see it easily with:
plot(tot, col=tot$Ind)
Maybe the presence of these outliers induce these strange results,
even if I do not know exactly what is behind this UDOI measure.
Try for example:
overlap<-kerneloverlap(tot[,2], meth="UDOI",percent=50)
Results are really different (for the core area).
Cheers,
Nicolas.
Le 23/07/2013 16:29, marc fernandez a écrit :
Hi,
I am having some issues in applying the home range overlap metrics
with the adehabitatHR package. I tried to use the UDOI method and the
BA. While the BA seems to work quite well,the values from the UDOI are
very strange UDOI, ranging from 300 to 0.3. I wonder if it could be
solved including the grid size? Or maybe it is related with the
selection of the smoothing parameter? Can anyone help me?
Here you have the code, and I also attach a reduced sample of the data.
> tot<-read.csv("totall.csv", header=TRUE, sep=";")
> coordinates(tot) <- ~Easting+Northing
> summary(tot)
Object of class SpatialPointsDataFrame
Coordinates:
min max
Easting 268511 576049.4
Northing 4209927 4264506.2
Is projected: NA
proj4string : [NA]
Number of points: 1210
Data attributes:
date Ind Sex
8/31/05 : 16 s5a : 77 F:647
10/2/07 : 15 s9a : 75 M:563
7/27/04 : 15 s6b : 74
7/11/07 : 13 s8j : 63
8/9/05 : 13 s7b : 62
10/13/07: 12 m2c : 59
(Other) :1126 (Other):800
> overlap<-kerneloverlap(tot[,2], meth="UDOI")
Thanks a lot!
Cheers,
MArc
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Comportement et Écologie de la Faune Sauvage (C.E.F.S.)
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