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depthf.BD

Band Depth for Functional Data

The (unadjusted) band depth for functional real-valued data of order J=2.

depthf.BD(datafA, datafB, range = NULL, d = 101)

`datafA`	Functions whose depth is computed, represented by a `dataf` object of their arguments and functional values. `m` stands for the number of functions.
`datafB`	Random sample functions with respect to which the depth of `datafA` is computed. `datafB` is represented by a `dataf` object of their arguments and functional values. `n` is the sample size. The grid of observation points for the functions `datafA` and `datafB` may not be the same.
`range`	The common range of the domain where the functions `datafA` and `datafB` are observed. Vector of length 2 with the left and the right end of the interval. Must contain all arguments given in `datafA` and `datafB`.
`d`	Grid size to which all the functional data are transformed. For depth computation, all functional observations are first transformed into vectors of their functional values of length `d` corresponding to equi-spaced points in the domain given by the interval `range`. Functional values in these points are reconstructed using linear interpolation, and extrapolation.

The function returns the vector of the sample (unadjusted) band depth values.

A vector of length m of the band depth values.

Lopez-Pintado, S. and Romo, J. (2009), On the concept of depth for functional data, J. Amer. Statist. Assoc. 104 (486), 718 - 734.

datafA = dataf.population()$dataf[1:20]
datafB = dataf.population()$dataf[21:50]
depthf.BD(datafA,datafB)

Depth-Based Classification and Calculation of Data Depth

v1.3.11

GPL-2

Authors

Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]

Initial release

2020-01-09