modifying predict.nnet() to function with errorest()
Dave, You should look at the train() function in teh caret package. Max
On Mon, Nov 2, 2009 at 6:01 PM, Armitage, Dave <dave.armitage at ufl.edu> wrote:
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 ______________________________________________ R-help at r-project.org 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.
Max