total_variation_depth: Total Variation Depth and Modified Shape Similarity Index

In fdaoutlier: Outlier Detection Tools for Functional Data Analysis

Total Variation Depth and Modified Shape Similarity Index

Description

This function computes the total variation depth (tvd) and the modified shape similarity index (mss) proposed in Huang and Sun (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2019.1574241")}.

Usage

total_variation_depth(dts)

Arguments

dts	A matrix or dataframe of size n observations/curves by p domain/evaluation points.

Details

This function computes the total variation depth (TVD) and modified shape similarity (MSS) index of a univariate functional data. The definition of the estimates of TVD and MSS can be found in Huang and Sun (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2019.1574241")}.

Value

Returns a list containing the following

tvd	the total variation depths of the observations of dts
mss	the modified shape similarity index of the observations of dts

Author(s)

Oluwasegun Ojo

References

Huang, H., & Sun, Y. (2019). A decomposition of total variation depth for understanding functional outliers. Technometrics, 61(4), 445-458.

See Also

tvd_mss for outlier detection using TVD and MSS.

Examples

dt6 <- simulation_model6()
tvd_object <- total_variation_depth(dt6$data)

fdaoutlier documentation built on Oct. 1, 2023, 1:06 a.m.