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Warping Functional Data in R and C via a Bayesian Multiresolution Approach | Slaets | Journal of Statistical Software
Authors: Leen Slaets, Gerda Claeskens, Bernard W. Silverman
Title: Warping Functional Data in R and C via a Bayesian Multiresolution Approach
Abstract: Phase variation in functional data obscures the true amplitude variation when a typical cross-sectional analysis of these responses would be performed. Time warping or curve registration aims at eliminating the phase variation, typically by applying transformations, the warping functions τn, to the function arguments. We propose a warping method that jointly estimates a decomposition of the warping function in warping components, and amplitude components. For the estimation routine, adaptive MCMC calculations are performed and implemented in C rather than R to increase computational speed. The R-C interface makes the program user-friendly, in that no knowledge of C is required and all input and output will be handled through R. The R package MRwarping contains all needed files.

Page views:: 1579. Submitted: 2011-08-31. Published: 2013-10-22.
Paper: Warping Functional Data in R and C via a Bayesian Multiresolution Approach     Download PDF (Downloads: 1615)
Supplements:
MRwarping_1.0.tar.gz: R source package Download (Downloads: 213; 32KB)
v55i03.R: R example code from the paper Download (Downloads: 196; 7KB)

DOI: 10.18637/jss.v055.i03

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.