Gab, Make sure you have variables for each training.
training <- data.frame(Training_2006, AST_L1B_1, AST_L1B_2, AST_L1B_3N) If you can't do that, then you don't have as many training observations than you have predictive informations. Make sure to create a line for each set of predictive pixels corresponding to a training pixel. That should then work in svm(). Once you have a satisfying model, take the rest of your pixels (where you have no training) and make a prediction using the model. Hope this helps, Etienne 2012/2/14 gab <gis...@libero.it> > Dear R Community- > > I am a new user of R. I am using R with GRASS GIS. > I would apply svm "on" raster data in GRASS. > Basically I have a raster with "areas training" and other three raster > (each > represents a band of ASTER satellite image). > My goal is to classify, according to training areas, the 3 raster. > Trying to replicate the guides found on the net, I did the following: > # load raster > Training<-readRAST6("Training") > AST_L1B_1<-readRAST6("AST_L1B_1") > AST_L1B_2<-readRAST6("AST_L1B_2") > AST_L1B_3N<-readRAST6("AST_L1B_3N") > #and then > model_ASTER <- > svm(Training_2006,AST_L1B_1,AST_L1B_2,AST_L1B_3N,type='C',kernel='linear') > #but > Errore in data.frame(y, x) : > arguments imply differing number of rows: 1857076, 1488 > > Thanks for any help > > > > > -- > View this message in context: > http://r.789695.n4.nabble.com/svm-with-GRASS-GIS-tp4388006p4388006.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.