`confidence.interval`

, a method for objects of class `nn`

,
typically produced by `neuralnet`

. Calculates confidence intervals of
the weights (White, 1989) and the network information criteria NIC (Murata
et al. 1994). All confidence intervals are calculated under the assumption
of a local identification of the given neural network. If this assumption
is violated, the results will not be reasonable. Please make also sure that
the chosen error function equals the negative log-likelihood function,
otherwise the results are not meaningfull, too.

`confidence.interval(x, alpha = 0.05)`

x

neural network

alpha

numerical. Sets the confidence level to (1-alpha).

`confidence.interval`

returns a list containing the following
components:

a list containing the lower confidence bounds of all weights of the neural network differentiated by the repetitions.

a list containing the upper confidence bounds of all weights of the neural network differentiated by the repetitions.

a vector containg the information criteria NIC for every repetition.

White (1989) *Learning in artificial neural networks. A
statistical perspective.* Neural Computation (1), pages 425-464

Murata et al. (1994) *Network information criterion - determining the
number of hidden units for an artificial neural network model.* IEEE
Transactions on Neural Networks 5 (6), pages 865-871

# NOT RUN { data(infert, package="datasets") print(net.infert <- neuralnet(case~parity+induced+spontaneous, infert, err.fct="ce", linear.output=FALSE)) confidence.interval(net.infert) # }