Need help on nnet

Am working on neural network.
Below is the coding and the output [...]

summary (uplift.nn)

a 3-3-1 network with 16 weights

options were -

  b->h1  i1->h1  i2->h1  i3->h1
  16.64    6.62  149.93    2.24
  b->h2  i1->h2  i2->h2  i3->h2
 -42.79  -17.40 -507.50   -5.14
  b->h3  i1->h3  i2->h3  i3->h3
   3.45    1.87   18.89    0.61
   b->o   h1->o   h2->o   h3->o
 402.81   41.29  236.76    6.06

Q1: How to interpret the above output

The summary above is the list of internal weights that were learnt during
the neural network training in nnet(). From my point of view I wouldn't
really try to interpret any meaning into those weights, especially if you
have multiple predictor variables.

Q2: My objective is to know the contribution of each independent variable.

You may try something like variable importance approaches (VI) or feature
selection approaches. 

1) In VI you have a training and test set as in normal cross-validation.
You train your network on the training set. You use the trained network
for predicting the test values. The clue in VI then is to pick one
variable at a time, permute its values in the test set only (!) and see
how much the prediction error deviates from the original prediction error
on the unpermuted test set.  Repeat this a lot of times to get a
meaningful output and also be sure to use a lot of cross-validation
permutations. The more the prediction error rises, the more important the
respective variable was/is. This approach includes interactions between
variables.

2) feature selection is essentially an exhaustive approach which tries
every possible subset of your predictors, trains a network and sees what
the prediction error is. The subset which is best (lowest error) is then
chosen in the end. It normally (as a side-effect) also gives you something
like an importance ranking of the variables when using backward or forward
feature selection. But be careful of interactions between variables.

Q3: Which package of neural network provides the AIC or BIC values

You may try training with the multinom() function, as pointed out in
msg09297:
http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg09297.html

I hope I could point out some keywords and places to look at.

Regards,
Georg.

Research Assistant
Otto-von-Guericke-Universit?t Magdeburg
research at georgruss.de
http://research.georgruss.de