How to preProcess features when some of them are factors?
My question is related to this one regarding categorical data (factors in R terms) when using the Caret package. I understand from the linked post that if you use the "formula interface", some features can be factors and the training will work fine. My question is how can I scale the data with the preProcess() function? If I try and do it on a data frame with some columns as factors, I get this error message:
Error in preProcess.default(etitanic, method = c("center", "scale")) :
all columns of x must be numeric
See here some sample code:
library(earth)
data(etitanic)
a <- preProcess(etitanic, method=c("center", "scale"))
b <- predict(etitanic, a)
Thank you.
Answer
It is really the same issue as the post you link to. preProcess works only on numeric data and you have:
> str(etitanic)
'data.frame': 1046 obs. of 6 variables:
$ pclass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ...
$ survived: int 1 1 0 0 0 1 1 0 1 0 ...
$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...
$ age : num 29 0.917 2 30 25 ...
$ sibsp : int 0 1 1 1 1 0 1 0 2 0 ...
$ parch : int 0 2 2 2 2 0 0 0 0 0 ...
You can't center and scale pclass or sex as-is so they need to be converted to dummy variables. You can use model.matrix or caret's dummyVars to do this:
> new <- model.matrix(survived ~ . - 1, data = etitanic)
> colnames(new)
[1] "pclass1st" "pclass2nd" "pclass3rd" "sexmale" "age"
[6] "sibsp" "parch"
The -1 gets rid of the intercept. Now you can run preProcess on this object.
btw making preProcess ignore non-numeric data is on my "to do" list but it might cause errors for people not paying attention.
Max