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Good morning,

I'm working on some spatio temporal data concerning PM 2.5.

I want to apply a version of random forest which explicitly accounts for spatial dependence in the observations, as introduced in "Random Forest for spatially dependent data" https://www.tandfonline.com/doi/abs/10.1080/01621459.2021.1950003#:~:text=Spatial%20linear%20mixed%2Dmodels%2C%20consisting,the%20covariate%20effect%20is%20nonlinear.

Here is the R package: https://www.google.com/url?sa=t&source=web&rct=j&url=https://cran.r-project.org/web/packages/RandomForestsGLS/vignettes/RandomForestsGLS_user_guide.pdf&ved=2ahUKEwjF6JiRhf73AhUQyxoKHbkBB-8QFnoECAUQAQ&usg=AOvVaw31g4t0m-Uoz1Wy-2ysDvla

The point is that I want to account for spatio temporal dependence, not just spatial dependence.

Theoretically if I could provide in input to the function the estimation of the spatio temporal covariance matrix Q (which will be a NT x NT matrix) then the fitting could run as in the original version of the alghoritm.

I have no idea about how to modify the function RFGLS_estimate to be able to provide in input the covariance matrix Q to be used to grow the trees.

Any suggestion?

Thank you very much in advance

ANDREA ZUCCHI
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    It looks like you're new to SO; welcome to the community! If you want great answers quickly, it's best to make your question reproducible. This includes sample code you've attempted, listing non-base R packages, any errors/warnings received, sample data, like the output from `dput(head(dataObject))`, and what type of output you are expecting. Check it out: [making R questions reproducible](https://stackoverflow.com/q/5963269). – Kat May 27 '22 at 03:41

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