R help - Jan 2009 - A simple perceptron neural network (nnet)

Hello,

I've started to learn about neural networks and the first examples
I've seen are the implementation of an OR logical gate, aswell as the
AND gate. I've implemented it as a perceptron with no hidden layers,
and I've done it this way because so far is the only way I've learned.
The R file with the implementation of the OR gate is:

ppton_or.R
----------------------------------------------------------------------
ppton_or <- function() {
         x = array(c(1,1,1,1,0,0,1,1,0,1,0,1), dim=c(4,3))
         y = c(0,1,1,1)
         w = c(0,0,0)
         b = 1
         n = 1
         k = 0

         while(all(as.integer((x[,] %*% w) >= 0) == y) == FALSE) {
                z = as.integer((x[n,] %*% w) >= 0)
                if(z != y[n]) {
                     w = w+b*(y[n]-z)*x[n,];
                }

                n = n%%4+1
                k = k+1
         }

         print(k)
         print(w)
}
----------------------------------------------------------------------

I've would like to know if it is possible to implement this pretty
basic neural network with the nnet package. I've tried using the
"skip=TRUE" switch with "size=0" and filling x and y with
the training
data but it is not working. Neither do I know how to make it use a
heaviside function as the threshold function. If someone could give me
some hint I'll be pretty grateful.

Thanks,
Eduardo Grajeda.