segclust2d: segclust2d: tools for segmentation of animal GPS movement...
In segclust2d: Bivariate Segmentation/Clustering Methods and Tools
segclust2d R Documentation
segclust2d: tools for segmentation of animal GPS movement data
Description
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.
Details
The segmentation method is a bivariate extension of Lavielle's method
available in adehabitatLT (Lavielle 1999; and 2005). 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) method (formerly available in cghseg package) to the bivariate
case.
The full description of the method is published in Patin et al. (2020).
References:
Lavielle, M. (1999) Detection of multiple changes in a sequence of dependent
variables. Stochastic Processes and their Applications, 83:
79–102.
Lavielle, M. (2005) Using penalized contrasts for the change-point problem.
Report number 5339, Institut national de recherche en informatique et en
automatique.
Patin, R., Etienne, M. P., Lebarbier, E., Chamaill\'e-Jammes, S.,
& Benhamou, S. (2020). Identifying stationary phases in
multivariate time series for highlighting behavioural modes
and home range settlements. Journal of Animal Ecology,
89(1), 44-56.
Picard, F., Robin, S., Lebarbier, E. and Daudin, J.-J. (2007), A
Segmentation/Clustering Model for the Analysis of Array CGH Data.
Biometrics, 63: 758-766. doi:10.1111/j.1541-0420.2006.00729.x
segclust2d documentation built on Aug. 21, 2023, 9:10 a.m.
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| segclust2d | R Documentation |
segclust2d: tools for segmentation of animal GPS movement data
Description
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.
Details
The segmentation method is a bivariate extension of Lavielle's method available in adehabitatLT (Lavielle 1999; and 2005). 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) method (formerly available in cghseg package) to the bivariate case.
The full description of the method is published in Patin et al. (2020).
References:
Lavielle, M. (1999) Detection of multiple changes in a sequence of dependent variables. Stochastic Processes and their Applications, 83: 79–102.
Lavielle, M. (2005) Using penalized contrasts for the change-point problem. Report number 5339, Institut national de recherche en informatique et en automatique.
Patin, R., Etienne, M. P., Lebarbier, E., Chamaill\'e-Jammes, S., & Benhamou, S. (2020). Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements. Journal of Animal Ecology, 89(1), 44-56.
Picard, F., Robin, S., Lebarbier, E. and Daudin, J.-J. (2007), A Segmentation/Clustering Model for the Analysis of Array CGH Data. Biometrics, 63: 758-766. doi:10.1111/j.1541-0420.2006.00729.x
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