Description Usage Arguments Value Author(s) References See Also Examples
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.
1 | 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, Frauke Guenther guenther@leibniz-bips.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
1 2 3 4 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.