multiple_align_functions {fdasrvf}

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.