Max, Thanks. Yes what you said is exactly I am looking for, i.e. the first tree fits using data from sites A&B, then predicts on C (and so on).
Does that means if I : 1. pass this list as index into trainControl > tmpSiteList [[1]] [1] 1 2 3 4 5 6 7 [[2]] [1] 1 2 3 8 9 10 [[3]] [1] 4 5 6 7 8 9 10 AND 2. use other "methods" in the trainControl() then I would get the RF to be built and tested in the above way? I had tried other "methods" in the trainControl (had tried root, cv), but seems in the final built RF, the "rf.obj$finalModel$inbag" still does not match those in the "index"...my understanding of "rf.obj$finalModel$inbag" is that it should show which row of sample that had gone into training of that particular tree, which in essence should match the "index" argument that we had passed into "trainControl"...may be my understanding of what this "rf.obj$finalModel$inbag" would show is wrong? I had not look into the estimates yet, what I am looking is just to make sure in each of the tree iteration, the "training sites" data does go into the training, and the "hold out sites" data would be used for testing in that tree iteration. Welcome any thoughts/ideas. Again really appreciates your patience and help on this. Regards, Coll -- View this message in context: http://r.789695.n4.nabble.com/Random-Forest-Strata-tp2295731p2305269.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.
