fdasrvf (version 1.9.4)

outlier.detection: 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 [math.ST].

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

# NOT RUN {
data("toy_data")
data("toy_warp")
q_outlier = outlier.detection(toy_warp$q0,toy_data$time,toy_warp$mqn,k=.1)
# }