Hi, You can set na.action=na.roughfix which fills NAs with the mean or mode of the missing variable.
Other option is to impute missing values using rfImpute, then run randomForest on the complete data set. Weidong Gu On Sat, Feb 25, 2012 at 6:24 PM, kevin123 <kevincorry...@gmail.com> wrote: > I am using the package Random Forrest to test and train a model, > I aim to predict (LengthOfStay.days),: > >> library(randomForest) >> model <- randomForest( LengthOfStay.days~.,data = training, > + importance=TRUE, > + keep.forest=TRUE > + ) > > > *This is a small portion of the data frame: * > > *data(training)* > > LengthOfStay.days CharlsonIndex.numeric DSFS.months > 1 0 0.0 8.5 > 6 0 0.0 3.5 > 7 0 0.0 0.5 > 8 0 0.0 0.5 > 9 0 0.0 1.5 > 11 0 1.5 NaN > > > > *Error message* > > Error in na.fail.default(list(LengthOfStay.days = c(0, 0, 0, 0, 0, 0, : > missing values in object, > > I would greatly appreciate any help > > Thanks > > Kevin > > > -- > View this message in context: > http://r.789695.n4.nabble.com/How-to-deal-with-missing-values-when-using-Random-Forrest-tp4421254p4421254.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. ______________________________________________ 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.
