Damian Krstajic wrote:
Hello,
We have a system which creates thousands of regression/classification models
and in cases where we have only one input variable NaiveBayes throws an error.
Maybe I am mistaken and I shouldn't expect to have a model with only one input
variable.
We use R version 2.6.0 (2007-10-03). We use caret (v4.1.19), but have tested
similar code with klaR (v.0.5.8), because caret relies on NaiveBayes
implementation from klaR. I get different error messages from caret than from
klaR so I will provide the code for caret usage and klaR usage.
Here is the code which uses the iris dataset.
library(klaR);
Loading required package: MASS
X<-iris["Sepal.Length"];
Y<-iris["Species"];
mnX<-as.matrix (X);
mnY<-as.matrix (Y);
cY<-factor(mnY);
d <- data.frame (cbind(mnX,cY));
In the line above you coerce cY to numeric (in a matrix) again.
Rather use
d <- data.frame(mnX=mnX[,1], cY=cY)
and proceed.
I will add some more error checking into NaiveBayes().
Best wishes,
Uwe Ligges
m<-NaiveBayes(cY~mnX, data=d);
predict(m);
Error in as.vector(x, mode) : invalid argument 'mode'
library(caret);
Loading required package: lattice
mCaret<-train(mnX,cY,method="nb",trControl = trainControl(method = "cv", number
= 10));
Loading required package: class
Fitting: usekernel=TRUE
Fitting: usekernel=FALSE
predicted <- predict(mCaret, newdata=mnX);
Error in 1:nrow(newdata) : NA/NaN argument
We use caret to call NaiveBayes and we don't have any error messages in cases
where the number of input variables is greater than 1.
Cheers
DK
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