If you are indeed aggregating (resampling is in my view a related but
different procedure) then you can also use the raster::modal function as
in, e.g.,

aggregate(x, 10, fun=modal)


On Tue, Feb 5, 2013 at 12:52 PM, Edzer Pebesma <
edzer.pebesma@uni-muenster.de> wrote:

> Jonathan, perhaps use
>
> function(x) which.max(table(x))
>
> as the aggregate function?
>
>
> On 02/05/2013 09:28 PM, Jonathan Greenberg wrote:
>
>> R-sig-geo'ers:
>>
>> I'm trying to figure out an efficient way to upsample (from higher rez to
>> lower rez) a *categorical* raster using a function where the output pixel
>> value is the ID of the class that would have had the highest cover within
>> the output, given the resampling.  This is NOT the same as a nearest
>> neighbor function. Right now, the best I can think of is:
>> 1) Generate a set of binary masks, one per class, of the input image,
>> where
>> for class X, the image X will be 0 if class X is not present, and 1 if it
>> is.
>> 2) Run aggregate() with a function mean() on these images.
>> 3) Pick the max value across the aggregated, lower rez images, which
>> should
>> be the class with the highest cover.
>>
>> Is there any easier way to do this?
>>
>> --j
>>
>>
>>
> --
> Edzer Pebesma
> Institute for Geoinformatics (ifgi), University of Münster
> Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
> 8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
> http://www.52north.org/**geostatistics<http://www.52north.org/geostatistics>
> e.pebesma@wwu.de
>
>
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