R: Evaluates Hessian for a Neural Network

Evaluates Hessian for a Neural Network

Usage

nnet.Hess(net, x, y)

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.

Description

Evaluates the Hessian (matrix of second derivatives) of the specified neural network. Normally called via argument Hess=TRUE to nnet or via vcov.multinom.

Value

square symmetric matrix of the Hessian evaluated at the weights stored in the net.

Examples

data(iris3)
# 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(nnet.Hess(ir1, ir[samp,], targets[samp,]), T)$values