On Thu, Dec 10, 2009 at 9:23 AM, Dan Putler <dan.put...@sauder.ubc.ca>wrote:
> Hi Alexandre,
>
> Greetings from yesterday evening.
>
> I think I know what you want, and I've been working on the same problem
> in a very different substantive context. To make sure I understand the
> question, you are basically stating that if the gap between "nearest"
> birds of the same species exceeds 1000 meters, then the two birds cannot
> be considered to be in the same cluster. Returning to your example of
> birds along a straight line, assume the birds run from A to B to C, and
> A and B are 700 meters apart, while B and C are 1001 meters apart, then
> it is the case that A and B are in a cluster, but C is not a member of
> that cluster.
>
Another approach (can be automated without user interaction):
1) create a distance matrix for the individuals (if I remember correctly
dist())
2) iterate through all individuals and for this focal individual:
a) identify all individuals closer to the focal animal then 1000m
(neighbours_focal)
b) go through all neighbours_focal and identify all individuals closer
then 1000m neighbours_focal_2
c) exclude all neighbours_focal from neighbours_focal_2 and go back to b
for all neighbours_focal_2
d) repeat b + c until the number of neighbours_focal_2 is zero, i.e. no
individual is closer to one in the cluster then 1000m
e) all these individuals are your cluster_1
2) exclude all individuals in cluster_1
repeat s and 3 until no individuals are left
Hope this helps,
Rainer
>
> Assuming what I said is correct, one thing you can do is create a
> relative neighbor graph of your points, and then drop any edges from the
> graph that exceed 1000 meters. This will create a set up unconnected
> sub-graphs, and the points within each sub-graph constitutes a cluster.
> I've written a function (using the spdens package) that removes the long
> edges from the relative neighbor graph, there by creating the set of
> clusters. I've pasted my function below (at some point I will create an
> R package that includes this function, but it is likely a couple of
> months away).
>
> The function follows:
>
> # dropLongLinks takes an nb (neighbor list) object, the original
> coordinates,
> # and a maximum distance, then removes links from the nb object for
> links that
> # exceed the maximum distance.
>
> # Programer: Dan Putler
> # Created: 08Feb09
> # Modified: 08Feb09
>
> dropLongLinks <- function(nb, coords, max.dist) {
> require(spdep)
> if(!is.matrix(coords)) coords <- as.matrix(coords)
> dists <- nbdists(nb, coords)
> nb.new <-
> lapply(1:length(nb), function(i) nb[[i]] <- nb[[i]][dists[[i]] <=
> max.dist])
> class(nb.new) <- "nb"
> return(nb.new)
> }
>
> On Thu, 2009-12-10 at 07:50 +0100, Alexandre VILLERS wrote:
> > Good morning (and sorry for the cross posting on the R sig ecology),
> >
> > I'm still working on the spatial distribution of an aggregated bird
> species. I would like to test different between birds distances to create
> clusters and thus identify groups of birds (leks). It would be "visually"
> quite easy to do it but I would like to repeat this clustering objectively
> for many years in order to characterize some biological processes. So I
> would like for example to group all the birds who share neighbours in less
> than 1000 meters in the same cluster. Thus, 3 birds lying on a straight line
> 700 meters from each other would belong to the same cluster (hope this is
> clear).
> >
> > I can get groups of points whose distances are below a certain threshold
> with dnearneigh() from package "spdep" but this would still require to write
> an iterative function to group points.
> > Is there an existing function that can do the whole trick? I know many
> packages are available for clustering with R but I haven't found one that I
> can parametrize in a such way yet.
> >
> > Any link will be appreciated.
> > Thanks
> >
> >
> > Alex
> >
> > P.S.: to get this visually
> >
> > x<-c(0,700,1400, 3000)
> > y<-c(0,0,0,0)
> > plot(y~x, col=c("red","red", "red", "black"), pch=c(16,16,16,16)) # red
> points belong to the same cluster while the black doesn't
> >
> >
> --
> Dan Putler
> Sauder School of Business
> University of British Columbia
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo@stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
--
NEW GERMAN FAX NUMBER!!!
Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation Biology,
UCT), Dipl. Phys. (Germany)
Centre of Excellence for Invasion Biology
Natural Sciences Building
Office Suite 2039
Stellenbosch University
Main Campus, Merriman Avenue
Stellenbosch
South Africa
Cell: +27 - (0)83 9479 042
Fax: +27 - (0)86 516 2782
Fax: +49 - (0)321 2125 2244
email: rai...@krugs.de
Skype: RMkrug
Google: r.m.k...@gmail.com
[[alternative HTML version deleted]]
_______________________________________________
R-sig-Geo mailing list
R-sig-Geo@stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
