Comments (1)
Sorry for the delay! There's not an inherent solution built in, but you could use x and y as variables. There are more advanced ways to constrain possible variable interactions in dbarts, but I don't really know them yet, and haven't built them in here. But the short answer is that like most other ML methods for SDMs (e.g., BRT or MaxEnt), it's not explicitly spatial
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Related Issues (20)
- "velox" was removed from CRAN HOT 2
- bart.flex doesn't allow prior adjusting
- Categorical variables not recognized HOT 3
- Dropped variable problem HOT 1
- velox dependency HOT 3
- Error in varim.diag "can't find `dropnames`" HOT 2
- cutoff value? HOT 2
- Conflicted "select" function between raster and dplyr. HOT 3
- Requiremed Number of Covariates HOT 1
- R version 4.0.5
- plot.mcmc does not work with masked raster layers HOT 3
- Error from `bart.step()` related to `select()` call HOT 2
- predict() gives completely flat prediction at 0.5 HOT 1
- Saved RI model objects may not retain saved trees HOT 4
- categorical variables mishandled by `varimp`, `variable.step`, `bart.step` HOT 4
- open thread: small vignette HOT 1
- Allow data frame y.data instead of vector
- Conservative partial plot extent
- add warning partial() doesn't work with rbart objects (yet) HOT 2
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