Gmixt_simultanee

Return matrix G(i,j), the mixture density 
for segment between points (i+1) to j :
$$G(i,j) = \sum_{p=1}^P \log (\pi_p f(y^{ij};\theta_p))$$
Rq: this density if factorized in order to avoid numerical zeros in
the log

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_simultanee function

<dl><dt>Don</dt>
<dd>the bivariate signal</dd>
<dt>lmin</dt>
<dd>the minimum size for a segment</dd>
<dt>phi</dt>
<dd>the parameters of the mixture</dd></dl>

Arguments

Gmixt_simultanee calculates the cost matrix 
for a segmentation/clustering model — Gmixt_simultanee

<dl>

<dt>Don</dt>
<dd>the bivariate signal</dd>


<dt>lmin</dt>
<dd>the minimum size for a segment</dd>


<dt>phi</dt>
<dd>the parameters of the mixture</dd>

</dl>

Gmixt_simultanee: Gmixt_simultanee calculates the cost matrix for a segmentation/clustering model

Description

Usage

Value

Arguments