Gmixt_algo_cpp

Internal C++ algorithm for computing the cost matrix.

Provides two methods for segmentation and joint segmentation/clustering of
bivariate time-series. Originally intended for ecological segmentation
(home-range and behavioural modes) but easily applied on other series,
the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'.
The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT'
(Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>).
This method rely on dynamic programming for efficient segmentation.
The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm.
This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method
(formerly available in 'cghseg' package) to the bivariate case.
The method is fully described in Patin et al (2018) <doi:10.1101/444794>.

Remi Patin

segclust2d

Bivariate Segmentation/Clustering Methods and Tools

Marie-Pierre Etienne

Emilie Lebarbier

Simon Benhamou

Gmixt_algo_cpp function

<dl><dt>zi</dt>
<dd>vector of observations</dd>
<dt>lgi</dt>
<dd>vector of indices</dd>
<dt>P</dt>
<dd>number of class</dd>
<dt>mvec</dt>
<dd>vector of means for each class</dd>
<dt>wk</dt>
<dd>temporary vector for calculations</dd>
<dt>svec</dt>
<dd>vector of standard deviations for each class</dd>
<dt>prop</dt>
<dd>mixture vector</dd></dl>

Arguments

Gmixt_algo_cpp — Gmixt_algo_cpp

<dl>

<dt>zi</dt>
<dd>vector of observations</dd>


<dt>lgi</dt>
<dd>vector of indices</dd>


<dt>P</dt>
<dd>number of class</dd>


<dt>mvec</dt>
<dd>vector of means for each class</dd>


<dt>wk</dt>
<dd>temporary vector for calculations</dd>


<dt>svec</dt>
<dd>vector of standard deviations for each class</dd>


<dt>prop</dt>
<dd>mixture vector</dd>

</dl>

Gmixt_algo_cpp: Gmixt_algo_cpp

Description

Usage

Arguments