I am using an algorigm to split my data set into two random sections repeatedly and constuct a model using rpart() on one, test on the other and average out the results.
One of my variables is a factor(crop) where each crop type has a code. Some crop types occur infrequently or singly. when the data set is randomly split, it may be that the first data set has a crop type which is not present in the second and so using predict() I get the error: Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = attr(object, : factor 'factor(c2001)' has new level(s) 13, 24, 35 where c2001 is the crop. I would like the predict function to ignore these records. is there a command which will allow this as part of the predict() function? With those with a small number of records (eg. 3-4), I would hope some of the models would have the right balance to allow a prediction to be made. -- View this message in context: http://r.789695.n4.nabble.com/predict-an-rpart-model-how-to-ignore-missing-levels-in-a-factor-tp3049218p3049218.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.
