criteria.mocca

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 <code>mocca</code>. 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).

clustering

covariates

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <doi:10.48550/arXiv.1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

Natalya Pya

fdaMocca

Model-Based Clustering for Functional Data with Covariates

Per Arnqvist

Sara Sjöstedt de Luna

criteria.mocca function

<dl>
 
<dt>x</dt>
<dd>fitted model objects of class <code>mocca</code> as produced by <code>mocca()</code>.</dd>
</dl>

Arguments

Author

AIC, BIC, entropy for a functional clustering model — criteria.mocca

<dl>
 
<dt>x</dt>
<dd>fitted model objects of class <code>mocca</code> as produced by <code>mocca()</code>.</dd>


</dl>

criteria.mocca: AIC, BIC, entropy for a functional clustering model

Description

Usage

Value

Arguments

Author

References

See Also

Examples