confidence.interval {neuralnet} | R Documentation |
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 err.fct 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:
lower.ci |
a list containing the lower confidence bounds of all weights of the neural network differentiated by the repetitions. |
upper.ci |
a list containing the upper confidence bounds of all weights of the neural network differentiated by the repetitions. |
nic |
a vector containg the information criteria NIC for every repetition. |
Stefan Fritsch fritsch@bips.uni-bremen.de
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)