Find Extrema Locations in Functional Data

Find landmark locations for registration

Usage

tf_landmarks_extrema(x, which = "all", threshold = 0.5, boundary_tol = NULL)

detect_landmarks(x, arg_list, which)

cluster_landmarks(features, n, bandwidth, threshold)

build_landmark_matrix(features, clusters, n, bandwidth)

Arguments

x

tf object (already smoothed if needed)

which

character vector: subset of c("max", "min", "zero")

threshold

minimum proportion of curves for a cluster to be retained

boundary_tol

numeric: features within this distance of the domain boundary are dropped (they are redundant with the boundary anchors in landmark registration). Defaults to 2x the grid spacing. Set to 0 to keep all features.

arg_list

list of numeric vectors: per-curve evaluation grids

features

list of per-curve data.frames

n

number of curves

bandwidth

matching distance

clusters

data.frame from cluster_landmarks()

Value

A numeric matrix with one row per function and one column per landmark, sorted left-to-right on the domain. Has attribute "feature_types" (character vector of "max", "min", or "zero" for each column). Contains NA where a curve is missing a landmark.

detect_landmarks: list of n data.frames with columns (position, type)

cluster_landmarks: data.frame with columns: center, type, count, proportion

build_landmark_matrix: n x k matrix with feature_types attribute

Details

Detects local maxima, minima, and/or zero crossings in each function and returns a landmark matrix suitable for tf_register() with method = "landmark". Uses position-based clustering across curves to establish feature correspondence and majority-count filtering to discard unstable landmarks.

• detect_landmarks detects local extrema and zero crossings per curve.

• cluster_landmarks clusters within each feature type separately (max with max, min with min, etc.) to avoid merging adjacent features of different types. Then combines and sorts by position.

• build_landmark_matrix creates a landmark matrix by matching per-curve features to clusters.

See also

tf_register() with method = "landmark"

Other registration functions: tf_align(), tf_estimate_warps(), tf_register(), tf_registration, tf_warp()

Examples

t <- seq(0, 1, length.out = 101)
x <- tfd(t(sapply(c(0.3, 0.5, 0.7), function(s) dnorm(t, s, 0.1))), arg = t)
tf_landmarks_extrema(x, "max")
#> Warning: No stable landmarks detected across curves.
#> ℹ Pre-smoothing with `tf_smooth()` can help suppress spurious features.
#>     
#> [1,]
#> [2,]
#> [3,]
#> attr(,"feature_types")
#> character(0)
tf_landmarks_extrema(x, "both")
#> Warning: No stable landmarks detected across curves.
#> ℹ Pre-smoothing with `tf_smooth()` can help suppress spurious features.
#>     
#> [1,]
#> [2,]
#> [3,]
#> attr(,"feature_types")
#> character(0)