Model-based clustering in the discriminative functional subspaces with the funFEM algorithm

Description

The package provides the funFEM algorithm (Bouveyron et al., 2014) which allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Details

Author(s)

Charles Bouveyron

Maintainer: <charles.bouveyron@parisdescartes.fr>

References

C. Bouveyron, E. Côme and J. Jacques, The discriminative functional mixture model for the analysis of bike sharing systems, Preprint HAL n.01024186, University Paris Descartes, 2014.

Examples

# Clustering the well-known "Canadian temperature" data (Ramsay & Silverman)
basis <- create.bspline.basis(c(0, 365), nbasis=21, norder=4)
fdobj <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"Temperature.C"],basis,
        fdnames=list("Day", "Station", "Deg C"))$fd
res = funFEM(fdobj,K=4)

# Visualization of the partition and the group means
par(mfrow=c(1,2))
plot(fdobj,col=res$cls,lwd=2,lty=1)
fdmeans = fdobj; fdmeans$coefs = t(res$prms$my)
plot(fdmeans,col=1:max(res$cls),lwd=2)

Results