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.armit...@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
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Max
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