Hi all,

I have considered neural network to classify the health status of the cow.
I found a very neatly written R codes for classification method in
here<http://home.strw.leidenuniv.nl/~jarle/IAC/RRoutines/classification-example.R>
.


It would be very helpful if you can answer some of the questions, that I am
struggling with,

I have set of time series data from different animals, I use data on 10
animals to train the model and 1 animal to test the model at a time.

Below is the simple Rcode , I used before complicating myself. But the
prediction resulted in classifying absolute 0 and 1, even though I have
asked results in integer value(#round()).


   -         In section 6.2 of neural network and related method for
   classification, by B.D.
Ripley<http://www.jstor.org/stable/2346118?origin=JSTOR-pdf>it is
specified as below.


[image: Inline image 9]

I tried to transfer my data in the range [0,1] before feeding them to
‘nnet’. But it does not seemed to improve my result.


   -         I would like to know, on specifying size of the hidden layer
   and decay value.
   -         How important is it specifying ‘linout=T’ in the function? I
   want output in terms probability, so I have not specified it!!!!!!!!
   -         In Rcode mensioned above, I see ‘r$data’, in all the places, I
   just wonder whether training and test data are considered as same!!!


#----------------------------------------------------------------------------------

## training NNET

              Nnet <- nnet(x=data, y=class.ind(dat$trt_window),

                 size=2,rang=0.05,skip=T, decay=0.000001, maxit=500)#



## validation for each lactation

     pred <- predict(Nnet, newdata=y, type = "raw")

#-----------------------------------------------------------------------------------



Your help is very much appreciated,

Thanks for your time advance.

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