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Elastic Outlier Detection

Source: R/alignment.R
elastic.outlier.detection.Rd

Identify outlying curves using SRVF-based elastic distances and Tukey's fence rule. Outliers are detected in both the amplitude and phase domains.

Usage

elastic.outlier.detection(
  fdataobj,
  lambda = 0,
  alpha = 0.05,
  use.median = FALSE
)

Arguments

fdataobj

An object of class 'fdata'.

lambda

Regularisation parameter controlling warping smoothness (default 0).

alpha

Significance level for the Tukey fence (default 0.05).

use.median

Logical; if TRUE, use the Karcher median instead of the Karcher mean as the central tendency reference (default FALSE).

Value

A list with components:

outlier_indices

integer vector of detected outlier indices

amplitude_outliers

integer vector of amplitude outlier indices

phase_outliers

integer vector of phase outlier indices

amplitude_distances

numeric vector of amplitude distances

phase_distances

numeric vector of phase distances

amplitude_fence

numeric; the amplitude fence threshold

phase_fence

numeric; the phase fence threshold

References

Xie, W., Kurtek, S., Bharath, K., and Sun, Y. (2017). A geometric approach to visualization of variability in functional data. Journal of the American Statistical Association, 112(519):979–993.

See also

elastic.depth

Examples

# \donttest{
set.seed(1)
t <- seq(0, 1, length.out = 50)
X <- matrix(0, 20, 50)
for (i in 1:20) X[i, ] <- sin(2 * pi * t + runif(1, -0.3, 0.3))
# Add an outlier
X[20, ] <- 5 * cos(4 * pi * t)
fd <- fdata(X, argvals = t)
out <- elastic.outlier.detection(fd)
# }