logLik.mocca

Function to extract the log-likelihood for a fitted functional clustering <code>mocca</code>
model (fitted by mixture likelihood maximization).
Note: <code>estimate.mocca</code> uses <code>loglik.EMmocca()</code> for calculating the log likelihood at each iterative step.

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

logLik.mocca function

<dl>
 
<dt>object</dt>
<dd>fitted model objects of class <code>mocca</code> as produced by <code>mocca()</code>.</dd><dt>...</dt>
<dd>unused in this case</dd>
 </dl>

Arguments

Author

Log-likelihood for a functional clustering model — logLik.mocca

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

<dt>...</dt>
<dd>unused in this case</dd>
 

</dl>

logLik.mocca: Log-likelihood for a functional clustering model

Description

Usage

Value

Arguments

Author

References

See Also