| optimum.reparam {fdasrvf} | R Documentation |
This function aligns two SRSF functions using Dynamic Programming
optimum.reparam( Q1, T1, Q2, T2, lambda = 0, method = "DP", w = 0.01, f1o = 0, f2o = 0 )
Q1 |
srsf of function 1 |
T1 |
sample points of function 1 |
Q2 |
srsf of function 2 |
T2 |
sample points of function 2 |
lambda |
controls amount of warping (default = 0) |
method |
controls which optimization method (default="DP") options are Dynamic Programming ("DP"), Coordinate Descent ("DP2"), and Riemannian BFGS ("RBFGS") |
w |
controls LRBFGS (default = 0.01) |
f1o |
initial value of f1, vector or scalar depending on q1, defaults to zero |
f2o |
initial value of f2, vector or scalar depending on q1, defaults to zero |
gam warping function
Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J. S., May 2011. Registration of functional data using fisher-rao metric, arXiv:1103.3817v2 [math.ST].
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.
data("simu_data")
q = f_to_srvf(simu_data$f,simu_data$time)
gam = optimum.reparam(q[,1],simu_data$time,q[,2],simu_data$time)