This function aligns a collection of functions using the elastic square-root slope (srsf) framework.
multiple_align_functions(
f,
time,
mu,
lambda = 0,
pen = "roughness",
showplot = TRUE,
smooth_data = FALSE,
sparam = 25,
parallel = FALSE,
cores = -1,
omethod = "DP",
MaxItr = 20,
iter = 2000,
verbose = TRUE
)Returns a fdawarp object containing
original functions
aligned functions - matrix (\(N\) x \(M\)) of \(M\) functions with \(N\) samples
aligned SRSFs - similar structure to fn
original SRSF - similar structure to fn
function mean or median - vector of length \(N\)
SRSF mean or median - vector of length \(N\)
warping functions - similar structure to fn
Original Variance of Functions
Amplitude Variance
Phase Variance
Cost Function Value
matrix (\(N\) x \(M\)) of \(M\) functions with \(N\) samples
vector of size \(N\) describing the sample points
vector of size \(N\) that f is aligned to
controls the elasticity (default = 0)
alignment penalty (default="roughness") options are second derivative ("roughness"), geodesic distance from id ("geodesic"), and norm from id ("norm")
shows plots of functions (default = T)
smooth data using box filter (default = F)
number of times to apply box filter (default = 25)
enable parallel mode using foreach and
doParallel package (default=F)
number of cores in parallel (default=-1, means all cores)
optimization method (DP,DP2,RBFGS,dBayes,expBayes)
maximum number of iterations
bayesian number of mcmc samples (default 2000)
verbose printing (default TRUE)
Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J. S., May 2011. Registration of functional data using fisher-rao metric, arXiv:1103.3817v2.
Tucker, J. D., Wu, W., Srivastava, A., Generative Models for Function Data using Phase and Amplitude Separation, Computational Statistics and Data Analysis (2012), 10.1016/j.csda.2012.12.001.