On 8/22/07, Polly He <[EMAIL PROTECTED]> wrote: > I'm trying to fit a naive Bayes model and predict on a new data set using > the functions naivebayes and predict (package = e1071). > > R version 2.5.1 on a Linux machine > > My data set looks like this. "class" is the response and k1 - k3 are the > independent variables. All of them are factors. The response has 52 levels > and k1 - k3 have 2-6 levels. I have about 9,300 independent variables but > omit the long list here for simple demonstration. There are no missing > values in the observations. > > class k1 k2 k3 > 1 0 0 1 > 8 0 0 0 > > # model fitting, I also tried setting laplace=0 but didn't help > nbmodel <- naiveBayes(class~., data=train, laplace=1) > > # predict > nb.fit <- predict(nbmodel, x.test[,-1]) > > First I had no trouble fitting the model. R also returned the predictions > for some of my large data sets. But for some data sets, R can fit the model > (no error message, nb.model$tables look ok). When I invoked the predict > function, it kept giving me the following message: > > # my data set has 1 response variable and 9318 independent variables > Error in FUN(1:9319[[4L]], ...) : subscript out of bounds [...]
In my experience, some predict methods have trouble when newdata does not have all levels of a factor. This seems to be the case with predict.naiveBayes: example(naiveBayes) predict(model, subset(HouseVotes84, V1 == "n")) gives Error in object$tables[[v]] : subscript out of bounds One workaround is to predict for a "bigger" data set and retain a subset of the predictions. Hope this helps, Stephen -- Rochester, Minn. USA ______________________________________________ R-help@stat.math.ethz.ch 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.
