chooseseg_lavielle

Choosing optimal number of segment using Marc Lavielle's method. From Emilie
Lebarbier. Method based on identifying breaks in the slope of the contrast.

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

chooseseg_lavielle function

<dl><dt>J</dt>
<dd>likelihood for each number of segment</dd>
<dt>S</dt>
<dd>threshold for choosing the number of segment. See
adehabitatLT::chooseseg</dd>
<dt>...</dt>
<dd>additional arguments</dd></dl>

Arguments

Internal Function for choosing optimal number of segment — chooseseg_lavielle

<dl>

<dt>J</dt>
<dd>likelihood for each number of segment</dd>


<dt>S</dt>
<dd>threshold for choosing the number of segment. See
adehabitatLT::chooseseg</dd>


<dt>...</dt>
<dd>additional arguments</dd>

</dl>

chooseseg_lavielle: Internal Function for choosing optimal number of segment

Description

Usage

Value

Arguments