[R] nnet question

[Erik Johnson]

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.