R help - Nov 2009 - modifying predict.nnet() to function with errorest()

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

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
>
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>


-- 

Max