[R-sig-Geo] Random forest classification combined with spatial linear model prediction

[Marcelino de la Cruz Rot]

Hi,
I'm not completely sure if this is what you are asking for but just in case:

https://blasbenito.github.io/spatialRF/

Cheers, Marcelino


El 21/02/2025 a las 9:46, Edzer Pebesma escribió:
> I'm not entirely sure what you mean by spatial linear model, but it 
> may be worth looking into 
> https://cran.r-project.org/web/packages/RandomForestsGLS/index.html
>
> The paper referenced in there, 
> https://doi.org/10.1080/01621459.2021.1950003 , is worth reading!
>
> On 20/02/2025 22:50, Manuel Spínola wrote:
>> Thank you Ben,
>>
>> I know tidysdm, and it’s a great package, but I don’t think it 
>> accounts for
>> spatial structure in the way I expect—though I could be wrong. It 
>> does use
>> spatial_block_cv, which likely considers the spatial component.
>>
>> It is my understanding that sprflm from the spmodel package applies 
>> kriging
>> to the residuals.
>>
>>
>> Manuel
>>
>>
>>
>>
>> El jue, 20 feb 2025 a las 15:20, Ben Tupper (<btupper using bigelow.org>)
>> escribió:
>>
>>> Hi,
>>>
>>> I'm not sure I fully understand what you are asking for, but are
>>> describing something like tidysdm
>>> <https://evolecolgroup.github.io/tidysdm/>?
>>>
>>> Cheers,
>>> Ben
>>>
>>> On Thu, Feb 20, 2025 at 3:16 PM Manuel Spínola <mspinola10 using gmail.com>
>>> wrote:
>>>
>>>> Dear list members,
>>>>
>>>> Is there any R package that combines random forest classification and
>>>> spatial linear model prediction?
>>>>
>>>> The spmodel can fit this type of model but only for random forest
>>>> regression according to the help document.
>>>>
>>>> My goal is to work with species distribution modelling with random 
>>>> forest
>>>> but includes the spatial structure of the data.
>>>>
>>>> Manuel
>>>>
>>>>
>>>> -- 
>>>> *Manuel Spínola, Ph.D.*
>>>> Instituto Internacional en Conservación y Manejo de Vida Silvestre
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>>>>>
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>>>>
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>>
>

-- 
Marcelino de la Cruz Rot
Coordinador funcional de Biología
Instituto de Investigación en Cambio Global (IICG-URJC)
Dpto. Biología y Geología, Física y Química Inorgánica
Universidad Rey Juan Carlos (URJC)
C. Tulipán, s/n, 28933 Móstoles, Madrid