On Mon, Aug 2, 2010 at 12:40 PM, Corrie Curtice <[email protected]> wrote:
> 2010/7/28 Damiano G. Preatoni <[email protected]>:
>> In un messaggio del Wednesday 28 July 2010, Corrie Curtice ha scritto:
>>> On Wed, Jul 28, 2010 at 2:35 AM, Anne Ghisla <[email protected]> wrote:
>>> > On Tue, 2010-07-27 at 15:27 -0400, Corrie Curtice wrote:
>>> >> Hello,
>>> >>
>>> >> I am trying to export the home range for a single individual animal.
>>> >> I'm getting the error  "Error in re[[i]] : subscript out of bounds" --
>>> >> I looked on the archive and found my own posting of this question for
>>> >> the 100% isopleth. :)  So now I'm wondering, why does the error occur
>>> >> at lower isopleth levels? Ideally I would like the 95% KHRE to show
>>> >> the "home range" of each animal, and this works with all other
>>> >> individuals.  The answer to my last posting noted that the isopleth
>>> >> went beyond the grid limits.   I'm not passing in an underlying grid.
>>> >> Here's my call:
>>> >>
>>> >>     ud <- kernelUD(xy, grid=100, h="href")
>>> >>     kvtmp <- getverticeshr(ud, lev = 95)
>>> >>
>>> >> Levels up to 85 work fine.  Is there anything I can do to fix this, if
>>> >> not what is the proper way to report the result for this animal? If
>>> >> you need more code or output I can send.
>>> >
>>> > Hi Corrie,
>>> >
>>> > it sounds like the issue reported here:
>>> >
>>> > https://trac.faunalia.it/animove/ticket/13
>>> >
>>> > does your dataset show anisotropy? The one attached to the ticket is a
>>> > narrow cloud of points along a horizontal line. The default grid created
>>> > by kernelUD is likely not able to encompass the whole UD. If the case, I
>>> > would try creating a wider grid and providing it to kernelUD.
>>> >
>>> > hope this helps!
>>>
>>> Yes, that's exactly it.  I tried increasing the value passed into the
>>> grid parameter -- is this right?  Is there some way to determine what
>>> the correct value would be?
>>
>> There is no "correct" value for the grid parameter.
>> It it a known "feature" of adehabitat.
>>
>> That is, passing "grid=<some integer value>" means that kernel calculations
>> (see e.g. Worton classical text) start with superimposing your point 
>> locations
>> a mesh with <integer value> x <integer value> _cells_. Worton's default is
>> 40x40.
>>
>> Problem is that is more meaningful having the possibility to supply the _cell
>> size_  insteda of the _number of cells_.
>>
>> This means that if your points cloud covers a _wide_ extent, say, half a
>> state, you will have in your case a 100x100 cells mesh, and a single cell 
>> side
>> will be some tenths of kilometers!
>>
>> To overcome this problem, one should use "grid=<a kasc object>" (look into
>> kernelUD source...).
>>
>> I normally do like this:
>> - using my favorite GIS package, I create a raster with the cell size I want,
>> making it so that covers my study area. This way I'll have for instance a
>> raster with 100 m cells, with a size of such and such rows and columns (I say
>> 'such and such' since we're not interested in how many squares we have, but 
>> in
>> how long is a square side!).
>> - I export that raster in ASCII GRID format.
>> - back into R I use read.asc function and create a kasc object that then I
>> will use as my "reference grid" in home range calculations, like this:
>>
>>  my.reference.grid <- read.asc('refgrid.asc')
>>  ud <- kernelUD(xy, grid=my.reference.grid, h="href")
>
> Thanks Damiano, this is helpful information. I have a follow up question.
>
> It sounds like from what you say, that the smaller cell size is
> better. ie: 100m vs 1000m or greater. I've read quite a bit of
> background lit, but until I try this on my own I don't fully
> understand all the implications. I am not able to find much that
> discusses the impact of cell size during kernel calculation.  More
> focus is on the smoothing parameter.
>
> I created a grid of 100m cells, and re-ran the kernelUD for one set of
> animals in my study (at one island).  The UDs in this home range are a
> bit smaller and more detailed than the ones created with 100x100 grid,
> pretty close but I do like it a little better.
>
> So, is there a process to decide on the best cell size, related to the
> data? Would I be safe to pick 100m for each of my sites and
> individuals?  These are marine mammals that range over wide areas,
> some as far as almost 300km, but mostly w/in 100km.


I retract that comment: the UDs created are exactly the same -- I was
looking at a wrong image when comparing the first time, apologies.
Thanks for the guidance.
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