Evaluates the Hessian (matrix of second derivatives) of the specified
neural network. Normally called via argument Hess=TRUE to nnet or via
vcov.multinom.
nnetHess(net, x, y, weights)object of class nnet as returned by nnet.
training data.
classes for training data.
the (case) weights used in the nnet fit.
square symmetric matrix of the Hessian evaluated at the weights stored in the net.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
# NOT RUN {
# use half the iris data
ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3])
targets <- matrix(c(rep(c(1,0,0),50), rep(c(0,1,0),50), rep(c(0,0,1),50)),
150, 3, byrow=TRUE)
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ir1 <- nnet(ir[samp,], targets[samp,], size=2, rang=0.1, decay=5e-4, maxit=200)
eigen(nnetHess(ir1, ir[samp,], targets[samp,]), TRUE)$values
# }
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