[R-sig-Geo] creating cluster with between-points arbitrary distances

[Alexandre VILLERS]

Dear Dan,

That's exactly that ! Thank you very much.
Rainer, thank you for the other approach. I had thought of something 
like that, and even if such loop is not too much complicated to write, 
before putting my hands into it, I wanted to make sure there was not an 
existing function hanging somewhere.

Best regards


Alex

Dan Putler a écrit :
> 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.
>
> 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
>>
>>
>>     

-- 
Alexandre Villers
PhD Candidate
AgriPop
Centre d'Etudes Biologiques de Chizé-CNRS UPR1934
79360 Beauvoir sur Niort

Phone +33 (0)5 49 09 96 13
Fax   +33 (0)5 49 09 65 26




__________ Information from ESET Mail Security, version of virus signature database 4674 (20091209) __________

The message was checked by ESET Mail Security.
http://www.eset.com