Landmark-Constrained Elastic Alignment

Align a set of curves to a target curve with landmark constraints. The alignment is performed using segmented dynamic programming that passes through specified landmark positions.

Usage

elastic.align.constrained(
  fdataobj,
  target = NULL,
  landmark.pairs = NULL,
  kind = "peak",
  min.prominence = 0,
  expected.count = 0,
  lambda = 0
)

Arguments

fdataobj

An object of class 'fdata'.

target

Target curve (numeric vector or single-curve fdata). If NULL, uses the cross-sectional mean.

landmark.pairs

A two-column matrix of landmark pairs (target_t, source_t) for explicit constraints. If NULL, auto-detects.

kind

Landmark type for auto-detection: "peak", "valley", "zero", "inflection". Default "peak".

min.prominence

Minimum prominence for auto-detection. Default 0.

expected.count

Expected number of landmarks for auto-detection. Default 0 (all detected).

lambda

Penalty weight on warp deviation from identity. Default 0.

Value

An object of class 'elastic.align' with components:

aligned

fdata of aligned curves

gammas

fdata of warping functions

distances

numeric vector of elastic distances

target

the target curve

fdataobj

the original input

Examples

# \donttest{
t <- seq(0, 1, length.out = 100)
X <- matrix(0, 10, 100)
for (i in 1:10) X[i, ] <- sin(2*pi*(t - i/50))
fd <- fdata(X, argvals = t)
res <- elastic.align.constrained(fd, kind = "peak")
# }