[R] nnet question

[Prof Brian Ripley]

Please read the documentation: why is a logistic output equation 
appropriate for example.  The iris3 example does not use the same 
arguments as you have, so you are claiming to know what you are doiung 
here.

You *have* worked though the examples in the book this software supports, 
haven't you?

On Mon, 26 Apr 2004, Erik Johnson wrote:

> I am using R 1.8.0, and am attempting to fit a Neural Network model of a 
> time series (here called Metrics.data).  It consists of one time series 
> variable run on its lag (AR(1)).  Basically, in an OLS model it would 
> look like
> Metrics.data$ewindx ~ Metrics.data$ewindx.lag1
> However, I am trying to run this through a neural network estimation.  
> So far, I have been getting convergence very quickly, and do not believe 
> it too be true.
> Here is the code and output.  Please note that I am using all of the 
> values for training and testing in one matrix, as I do not care about 
> the testing results right now, I only want to capture weights.  Here is 
> the code and output
> 
>  > nnet(metrics.data$ewindxlag1,metrics.data$ewindx,size=2, entropy=FALSE)
> # weights:  7
> initial  value 78858370643.085342
> final  value 78841786515.212158
> converged
> a 1-2-1 network with 7 weights
> options were -
> 
> When I run the iris3 example, the convergence looks much nicer 
> (consisting of more than one iteration).  Am I missing some fundamental 
> understanding of this example?  Thanks for any input.
> 
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
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