Dear list members, I am using the fit.gstatModel from the GSIF package.
I obtained 2 different values for variance explained using randomForest. One is for the model and the other for the prediction. What is the difference among them and what is more important to report? omm <- fit.gstatModel(meuse, om~dist+ffreq, meuse.grid, method="randomForest") > omm@regModel Call: randomForest(formula = formulaString, data = rmatrix.s, importance = TRUE, na.action = na.omit) Type of random forest: regression Number of trees: 500 No. of variables tried at each split: 1 Mean of squared residuals: 5.952434 % Var explained: 49.16 om.rk <- predict(omm, meuse.grid) > show(om.rk) Variable : om Minium value : 1 Maximum value : 17 Size : 153 Total area : 4964800 Total area (units) : square-m Resolution (x) : 40 Resolution (y) : 40 Resolution (units) : m Vgm model : Exp Nugget (residual) : 2.78 Sill (residual) : 8.36 Range (residual) : 6100 RMSE (validation) : 1.672 Var explained : 76.1% Effective bytes : 1215 Compression method : gzip -- *Manuel Spínola, Ph.D.* Instituto Internacional en Conservación y Manejo de Vida Silvestre Universidad Nacional Apartado 1350-3000 Heredia COSTA RICA mspin...@una.cr <mspin...@una.ac.cr> mspinol...@gmail.com Teléfono: (506) 8706 - 4662 Personal website: Lobito de río <https://sites.google.com/site/lobitoderio/> Institutional website: ICOMVIS <http://www.icomvis.una.ac.cr/> [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo