How to build random forests in R with missing (NA)

Question

I would like to fit a random forest model, but when I call

library(randomForest)
cars$speed[1] <- NA # to simulate missing value
model <- randomForest(speed ~., data=cars)

I get the following error

Error in na.fail.default(list(speed = c(NA, 4, 7, 7, 8, 9, 10, 10, 10,  : 
   missing values in object

Answer 1

My initial reaction to this question was that it didn't show much research effort, since "everyone" knows that random forests don't handle missing values in predictors. But upon checking ?randomForest I must confess that it could be much more explicit about this.

(Although, Breiman's PDF linked to in the documentation does explicitly say that missing values are simply not handled at all.)

The only obvious clue in the official documentation that I could see was that the default value for the na.action parameter is na.fail, which might be too cryptic for new users.

In any case, if your predictors have missing values, you have (basically) two choices:

1. Use a different tool (rpart handles missing values nicely.)
2. Impute the missing values

Not surprisingly, the randomForest package has a function for doing just this, rfImpute. The documentation at ?rfImpute runs through a basic example of its use.

If only a small number of cases have missing values, you might also try setting na.action = na.omit to simply drop those cases.

And of course, this answer is a bit of a guess that your problem really is simply having missing values.

Answer 2

If there is possibility that missing values are informative then you can inpute missing values and add additional binary variables (with new.vars<-is.na(your_dataset) ) and check if it lowers error, if new.var is too large set to add it to your_dataset then you could use it alone, pick significiant variables with varImpPlot and add them to your_dataset, you could also try to add single variable to your_dataset which counts number of NA's new.var <- rowSums(new.vars)

This is not off-topick answer, if missing variables are informative accounting for them could correct for increase of model error due to inperfect imputation procedure alone.

Missing values are informative then they arise due to non-random causes, its expecially common in social experiments settings.