clement.calenge at oncfs.gouv.fr
Wed Feb 11 11:16:21 CET 2009
> The result is a non-symmetric distance matrix, which eventually can be
> aggregated to have, say, average overlaps between sexes, seasons, etc..
> In practice, all that is done with a slightly modified version of Clément's
> kerneloverlap. the modification consists in commenting put the first rows
> that call kernelUD, and making function kerneloverlap work on already
> calculated UDs.
> This raises another point, i.e. a suggestion to Clément: since often the 40x40
> cells approach gives undesirable results, we prefer to prepare as a
> preliminary step, a raster (of course using exclusively ESRI software, duh!)
> with cells of suitable size, that encompassess an extent slightly wider than
> the study area, an then calculate once all the UDs.
Note that controlling the grid can be done with kerneloverlap: this
functions accepts all the arguments of kernelUD. For example, using the
## With 200x200 grid
grid=200, meth="VI", conditional=TRUE)
## With a map available in R
map <- getkasc(puechabon$kasc,1)
grid=map, meth="VI", conditional=TRUE)
> This 'single point of homerange truth'  guarantees a consistency between
> subsequant calculations, i.e. 'getvoumes + polygon extraction', compana,
> kerneloverlapd etc.
> This also saves lots of time, since UD calculation, moreover with a 'fine'
> grid, takes some time: UD processing can be done on a fast number crunching
> machine , and the result can be saved (as part of a sahrlocs structure)
> and retrieved on a less performant host to do other postprocessing.
Yes, you are right: kernel UDs are often computed as a basis for further
analysis, including kernel overlap, but not only... It would be more
sensible to estimate UD once, and then using them for other analyses. I
planned to upload a new version of adehabitat to CRAN before the end of
the week, and I will include, on the help page of kerneloverlap, a new
function accepting a "khrud" object to perform this computation.
Many thanks for the suggestion,
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