multiple_align_functions: Group-wise function alignment to specified mean

In fdasrvf: Elastic Functional Data Analysis

Group-wise function alignment to specified mean

Description

This function aligns a collection of functions using the elastic square-root slope (srsf) framework.

Usage

multiple_align_functions(
  f,
  time,
  mu,
  lambda = 0,
  pen = "roughness",
  showplot = TRUE,
  smooth_data = FALSE,
  sparam = 25,
  parallel = FALSE,
  omethod = "DP",
  MaxItr = 20,
  iter = 2000
)

Arguments

f	matrix (N x M) of M functions with N samples
time	vector of size N describing the sample points
mu	vector of size N that f is aligned to
lambda	controls the elasticity (default = 0)
pen	alignment penalty (default="roughness") options are second derivative ("roughness"), geodesic distance from id ("geodesic"), and norm from id ("norm")
showplot	shows plots of functions (default = T)
smooth_data	smooth data using box filter (default = F)
sparam	number of times to apply box filter (default = 25)
parallel	enable parallel mode using foreach() and doParallel package (default=F)
omethod	optimization method (DP,DP2,RBFGS,dBayes,expBayes)
MaxItr	maximum number of iterations
iter	bayesian number of mcmc samples (default 2000)

Value

Returns a fdawarp object containing

f0	original functions
fn	aligned functions - matrix (N x M) of M functions with N samples
qn	aligned SRSFs - similar structure to fn
q0	original SRSF - similar structure to fn
fmean	function mean or median - vector of length N
mqn	SRSF mean or median - vector of length N
gam	warping functions - similar structure to fn
orig.var	Original Variance of Functions
amp.var	Amplitude Variance
phase.var	Phase Variance
qun	Cost Function Value

References

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.

fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.