Dear all,
I tried a simple naive Bayes classification on an artificial dataset, but I
have troubles getting the predict function to work with the type="class"
specification. With type= "raw", it works perfectly, but with type="class" I
get following error :
Error in as.vector(x, mode) : invalid 'mode' argument
Data : mixture.train is a training set with 100 points originating from 2
multivariate gaussian distributions (class 0 and class 1), with X1 and X2 as
coordinates in a 2-dimensional space. Mixture.test is a grid going from -15
to +15 in both dimensions. Stupid data, but it's just to test.
Code :
Sigma <- matrix(c(10,3,3,2),2,2)
mixture.train <- cbind(mvrnorm(n=50, c(0, 2), Sigma),rep(0,50))
mixture.train <- as.data.frame(rbind(mixture.train,cbind(mvrnorm(n=50, c(2,
0), Sigma),rep(1,50))))
names(mixture.train) <-c("X1","X2","Class")
X1 <- rep(seq(-15,15,by=1),31)
X2 <- rep(seq(-15,15,by=1),each = 31)
mixture.test <- data.frame(X1,X2)
Bayes.res <- naiveBayes(Class ~ X1 + X2, data=mixture.train)
pred.bayes <-predict(Bayes.res, cbind(mixture.test$X1,
mixture.test$X2),type="class")
Tried it also with pred.bayes <-predict(Bayes.res,
mixture.test,type="class"), but that gives the same effect. Is this a bug or
am I missing something?
Kind regards
Joris Meys
University Ghent
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