criteria.mocca {fdaMocca} | R Documentation |
AIC, BIC, entropy for a functional clustering model
Description
Function to extract the information criteria AIC and BIC, as well as the average Shannon entropy over all functional objects, for a fitted functional clustering mocca
. The Shannon entropy is computed over the posterior probability distribution of belonging to a specific cluster given the functional object (see Arnqvist and Sjöstedt de Luna, 2019, for further details).
Usage
criteria.mocca(x)
Arguments
x |
fitted model objects of class |
Value
A table with the AIC, BIC and Shannon entropy values of the fitted model.
Author(s)
Per Arnqvist
References
Arnqvist, P., and Sjöstedt de Luna, S. (2019). Model based functional clustering of varved lake sediments. arXiv preprint arXiv:1904.10265.
See Also
Examples
## see examples in mocca()
[Package fdaMocca version 0.1-1 Index]