Calculates confidence intervals of the weights
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)
- neural network
- numerical. Sets the confidence level to (1-alpha).
confidence.intervalreturns a list containing the following components:
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
data(infert, package="datasets") print(net.infert <- neuralnet(case~parity+induced+spontaneous, infert, err.fct="ce", linear.output=FALSE)) confidence.interval(net.infert)