outlier.detection: Outlier Detection

In fdasrvf: Elastic Functional Data Analysis

Outlier Detection

Description

This function calculates outlier's using geodesic distances of the SRVFs from the median

Usage

outlier.detection(q, time, mq, k = 1.5)

Arguments

q	matrix (N x M) of M SRVF functions with N samples
time	vector of size N describing the sample points
mq	median calculated using time_warping()
k	cutoff threshold (default = 1.5)

Value

q_outlier

outlier functions

References

Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J. S., May 2011. Registration of functional data using fisher-rao metric, arXiv:1103.3817v2.

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.

Examples

q_outlier <- outlier.detection(
  q = toy_warp$q0,
  time = toy_data$time,
  mq = toy_warp$mqn,
  k = .1
)

fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.