Purpose of decay parameter in nnet function in R?

Question

I am using nnet function in R to train my neural network. I am not getting what is decay parameter in nnet is? Is this step size to be used in gradient descent mentod or regular

Answer 1

Complementing blahdiblah's answer by looking at the source code I think that parameter weights corresponds to the learning rate of back-propagation (by reading the manual I couldn't understand what it was). Look at the file nnet.c, line 236, inside function fpass :

TotalError += wx * E(Outputs[i], goal[i - FirstOutput]);

here, in a very intuitive nomenclature, E corresponds to the bp error and wx is a parameter passed to the function, which eventually corresponds to the identifier Weights[i].

Also you can be sure that the parameter decay is indeed what it claims to be by going to the lines 317~319 of the same file, inside function VR_dfunc :

for (i = 0; i < Nweights; i++)
    sum1 += Decay[i] * p[i] * p[i];
*fp = TotalError + sum1;

where p corresponds to the connections' weights, which is the exact definition of the weight-decay regularization.