Hi Mitchell,

It's a good suggestion, and I had thought of this, but one concern is that
the model fit at the end of the terminal nodes would need to be a
generalized linear model (the predicted outcomes are factors).  Given that
the models need to be fit automatically without human guidance, I'm not
keen on using a GLM with the types predictor variables I have...

mobForest is a take-off on mob, but my initial testing indicated that it
took a VERY long time to fit the models.

Thanks!
Craig



On Thu, Aug 7, 2014 at 8:10 PM, Mitchell Maltenfort <mmalten@gmail.com>
wrote:

> I don't know this particular package well, but I believe "party" contains
> something called "mob" which creates a regression tree terminating in
> different models at each node.  Could that be adapted to your project?
>
>
> On Thursday, August 7, 2014, Craig Aumann <craigaumann@gmail.com> wrote:
>
>> I'm struggling with the best way to apply different predictive models over
>> different geographical areas of a raster stack.
>>
>> The context of the problem is that different predictive models are
>> developed within different polygonal regions of the overall study area.
>>  Each model needs to be used to predict an outcome for just the geographic
>> area for which it was developed.  Every pixel has one and only one
>> predictive model, but the model changes across different regions of the
>> landscape.  The models come from a "random forest" fit.
>>
>> The problem is that the rasterstack is rather large both in terms of
>> number
>> of pixels and also the number of layers which the predictive model needs
>> to
>> use.  If the problem were smaller, there are a number of things I could
>> "get away with" in terms of how I would do this, but given the problem
>> size, I need a more cunning solution.
>>
>> Ideally, I would like to only call predict from the package Raster just
>> once, and have the predict function call the right model based on the
>> geographical location of the pixel.  However, not clear that this is
>> possible with the Raster Package, or if it is possible how to implement it
>> efficiently.
>>
>> Any ideas or suggestions greatly appreciated.
>>
>> Cheers!
>> Craig
>>
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>>
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>
>
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