nnetHess {nnet} | R Documentation |
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)
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.
|
square symmetric matrix of the Hessian evaluated at the weights stored in the net.
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(nnetHess(ir1, ir[samp,], targets[samp,]), TRUE)$values