Evaluates the Hessian (matrix of second derivatives) of the specified
neural network. Normally called via argument Hess=TRUE to nnet or via
vcov.multinom.
Usage
nnetHess(net, x, y, weights)
Arguments
net
object of class nnet as returned by nnet.
x
training data.
y
classes for training data.
weights
the (case) weights used in the nnet fit.
Value
square symmetric matrix of the Hessian evaluated at the weights stored
in the net.
References
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.
See Also
nnet, predict.nnet
Examples
# 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