Hi list, I'm trying to do a randomForest classification in a MODIS NDVI time series. So far I've been able to generate the randomForest and get the Grass NDVI images inside grass as a SpatialGridDataFrame. Then, following some notes from Markus Neteler [1], I converted the SpatialGridDataFrame to a DataFrame and sucessfully applied the randomForest classifier. The problem is that now I'm struglling to transform the DataFrame back to a grass image. What is giving me a headach is that the images contains lots of null values (I need to have a MASK in place) so, each NDVI image has 1023701 cells but the DataFrame has only 264647 values since the conversion from SpatialGrid to DataFrame skips the nulls.
So, the question is, how to convert my DataFrame back to a Grass image? Cheers Daniel [1] http://mpa.fbk.eu/markus/shortcourse/notes7.html PS - For completion sake... Using Grass 6.4.0RC6 in Ubuntu 9.10 R 2.11 commands used in R: # open images ndvi <- readRAST6(<list of 23 ndvi images>) # convert to dataframe ndvi.df <- as.data.frame(ndvi) class <- predict(RFmodel, ndvi.df) # class contains 264647 classified pixels - how to get them back to an image? _______________________________________________ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user