Hello, I've recently been using the nnet package to do some basic forecast predictions. I've found the package to be quite useful and I am getting some good results. However, I am in the midst of writing a small paper on the results I am getting and wish to clarify some things about the nnet package that are not made clear in the documentation. In particular I would like to know the following:
1) Is it a standard feed forward network trained using gradient descent (I am assuming this is the case, seems like a no brainer but just to be sure)? 2) What is the sigmoidal function used for the activation/firing of a node in the network? 3) What exactly does the output "value" consist of at each iteration? Is this the value of the Least Mean Square function of the difference between the output layer and the target values or is it something else? 4) Will this package ever be updated to allow for multiple layers instead of just one? (just out of curiousity) I have to present this paper on Friday May 2nd so I would greatly appreciate a timely response. Thanks, -Colin ______________________________________________ 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.
