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depthf.HR

Half-Region Depth for Functional Data

The half-region depth for functional real-valued data.

depthf.HR(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 half-region depth values.

A vector of length m of the half-region depth values.

Lopez-Pintado, S. and Romo, J. (2011). A half-region depth for functional data. Computational Statistics & Data Analysis 55 (4), 1679–1695.

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