On 10/12/10 02:56:13, jothy wrote: > 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@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-Universität Magdeburg resea...@georgruss.de http://research.georgruss.de ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.