fdars (version 0.3.3)

group.distance: Compute Distance/Similarity Between Groups of Functional Data

Description

Computes various distance and similarity measures between pre-defined groups of functional curves.

Usage

group.distance(
  fdataobj,
  groups,
  method = c("centroid", "hausdorff", "depth", "all"),
  metric = "lp",
  p = 2,
  depth.method = "FM",
  ...
)

Value

An object of class 'group.distance' containing:

centroid

Centroid distance matrix (if method includes centroid)

hausdorff

Hausdorff distance matrix (if method includes hausdorff)

depth

Depth-based similarity matrix (if method includes depth)

groups

Unique group labels

group.sizes

Number of curves per group

method

Methods used

Arguments

fdataobj

An object of class 'fdata'.

groups

A factor or character vector specifying group membership for each curve. Must have length equal to the number of curves.

method

Distance/similarity method:

• "centroid": L2 distance between group mean curves

• "hausdorff": Hausdorff-style distance between groups

• "depth": Depth-based overlap (similarity, not distance)

• "all": Compute all methods

metric

Distance metric for centroid method (default "lp").

p

Power for Lp metric (default 2 for L2).

depth.method

Depth method for depth-based overlap (default "FM").

...

Additional arguments passed to metric functions.

Examples

# Create grouped functional data
set.seed(42)
n <- 30
m <- 50
t_grid <- seq(0, 1, length.out = m)
X <- matrix(0, n, m)
for (i in 1:15) X[i, ] <- sin(2 * pi * t_grid) + rnorm(m, sd = 0.1)
for (i in 16:30) X[i, ] <- cos(2 * pi * t_grid) + rnorm(m, sd = 0.1)
fd <- fdata(X, argvals = t_grid)
groups <- factor(rep(c("A", "B"), each = 15))

# Compute all distance measures
gd <- group.distance(fd, groups, method = "all")
print(gd)