If you extract the information from your rasters into a dataframe (one column per raster), can you use the predict.randomForest function in the randomForest package?
If yes, you can then input your predictions back into a raster of the same dimensions as the environmental variables. I don't have any experience with random forest model, but that's what I do with my Boosted Regression Tree predictions. Nicole K.S. Barker Ph.D Student / Étudiante au Doctorat Laval University / Université Laval Ducks Unlimited Canada / Canards Illimités Canada LinkedIn: www.linkedin.com/in/nksbarker Phone: 647-470-3207 On Tue, Mar 5, 2013 at 5:21 AM, Li Wen <[email protected]> wrote: > Dear list member > > > > I have fitted a Random Forest model for species distribution, and want to > use it to project the model to a defined landscape (i.e. forecasting for > all > grids in an area) . The landscape has all the environment covariates > (rasters) and cover a large region (over 1000*1000 grids). I wonder if > there > is package in R to do this. Thank you for your help in advance. > > > > Cheers > > Li > > > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-ecology mailing list > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]]
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