[R] any r package can handle factor levels not in the test set

[William Dunlap]

I think it would be nice if predict methods returned NA in appropriate
spots instead of aborting when a categorical predictor contains levels not
found in the training set.  It should not be that hard to implement, as the
'xlevels' component of the model is already being used to put factor levels
into the order found in the training set.  Until that happens you can do
this by hand as in the following example:

> training <- data.frame(Cat=rep(c("One","Two","Three"),3), Dog = 1:9,
Response=100+2^(1:9))
> newdata <- data.frame(Cat=c("Two","Apocalypse","Three"), Dog=2)
> model <- lm(data=training, Response ~ Cat + log(Dog))
> predict(model, newdata=newdata)
Error in model.frame.default(Terms, newdata, na.action = na.action, xlev =
object$xlevels) :
  factor Cat has new levels Apocalypse
> predict(model, newdata=newdata[-2,])
        1         3
 85.50099 148.56609
> # Use model$xlevels to replace unknown levels with NA's
> newdata$Cat <- factor(newdata$Cat, levels=model$xlevels$Cat)
> predict(model, newdata=newdata)
        1         2         3
 85.50099        NA 148.56609

(I don't think that predict.lm should be trying anything fancy to give a
non-NA
value at the Apocalypse.  That would be the job for another model fitting
function,
like rpart.)


Bill Dunlap
TIBCO Software
wdunlap tibco.com

On Tue, Jan 13, 2015 at 8:59 AM, HelponR <suncertain at gmail.com> wrote:

> Thanks for your reply. But I cannot control the data.
> I am dealing with real world stream data. It is very normal that the test
> data(when you apply model to do prediction) have new values that are not
> seen in training data.
> If I code myself, I would give a random guess or just an intercept for such
> situation. But it seems most R package returns an error and exit.
>
> On Mon, Jan 12, 2015 at 6:08 PM, Richard M. Heiberger <rmh at temple.edu>
> wrote:
>
> > You need to define the levels of the training set to include all
> > levels that you might see.
> > Something like this
> >
> > > A <- factor(letters[1:5])
> > > B <- factor(letters[c(1,3,5,7,9)])
> > > A
> > [1] a b c d e
> > Levels: a b c d e
> > > B
> > [1] a c e g i
> > Levels: a c e g i
> > > training <- factor(A, levels=unique(c(levels(A), levels(B))))
> > > training
> > [1] a b c d e
> > Levels: a b c d e g i
> > >
> >
> > In the future please "provide commented, minimal, self-contained,
> > reproducible code."
> >
> > On Mon, Jan 12, 2015 at 9:00 PM, HelponR <suncertain at gmail.com> wrote:
> > > It looks like gbm, glm all has this issue
> > >
> > > I wonder if any R package is immune of this?
> > >
> > > In reality, it is very normal that test data has data unseen in
> training
> > > data. It looks like I have to give up R?
> > >
> > > Thanks!
> > >
> > >         [[alternative HTML version deleted]]
> > >
> > > ______________________________________________
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> > http://www.R-project.org/posting-guide.html
> > > and provide commented, minimal, self-contained, reproducible code.
> >
>
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>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
>

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