It doesn't: You just get an error if there are NAs in the data; e.g., R> rf1 = randomForest(iris[1:4], iris[[5]]) R> predict(rf1, newdata=data.frame(Sepal.Length=1, Sepal.Width=2, Petal.Length=3, Petal.Width=NA)) Error in predict.randomForest(rf1, newdata = data.frame(Sepal.Length = 1, : missing values in newdata Andy
-----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Jennifer Corcoran Sent: Saturday, May 05, 2012 5:17 PM To: r-help@r-project.org Subject: [R] No Data in randomForest predict I would like to ask a general question about the randomForest predict function and how it handles No Data values. I understand that you can omit No Data values while developing the randomForest object, but how does it handle No Data in the prediction phase? I would like the output to be NA if any (not just all) of the input data have an NA value. It is not clear to me if this is the default or if I need to add an argument in the predict function. [[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. Notice: This e-mail message, together with any attachme...{{dropped:11}} ______________________________________________ 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.
