Greetings,
I am having trouble calculating artificial neural network
misclassification errors using errorest() from the ipred package.
I have had no problems estimating the values with randomForest()
or svm(), but can't seem to get it to work with nnet(). I believe
this is due to the output of the predict.nnet() function within
cv.factor(). Below is a quick example of the problem I'm
experiencing. Any ideas on how to get around it or will it simply
not work with nnet()?
library(MASS)
library(nnet)
library(ipred)
data(iris3)
set.seed(191)
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
+ species = factor(c(rep("s",50), rep("c", 50), rep("v",
50))))
errorest(species ~., data = ird, subset = samp, model = nnet,
size = 2, rang =0.1, decay = 5e-4, maxit = 200)
# weights: 19
initial value 73.864441
.
.
.
final value 0.339114
converged
Error in cv.factor(y, formula, data, model = model, predict =
predict, :
predict does not return factor values
Thanks,
Dave
______________________________________
Dave Armitage
Wildlife Ecology and Conservation
University of Florida
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