HI: Hypograph Index of univariate functional dataset

In roahd: Robust Analysis of High Dimensional Data

Description

This function computes the Hypograph Index (HI) of elements of a univariate functional dataset.

Usage

HI(Data)

## S3 method for class 'fData'
HI(Data)

## Default S3 method:
HI(Data)

Arguments

Data	either an fData object or a matrix-like dataset of functional data (e.g. fData$values), with observations as rows and measurements over grid points as columns.

Details

Given a univariate functional dataset, X_1(t), X_2(t), …, X_N(t), defined over a compact interval I=[a,b], this function computes the HI, i.e.:

HI( X(t) ) = \frac{1}{N} ∑_{i=1}^N I( G( X_i(t) ) \subset hyp( X(t) ) ) = \frac{1}{N} ∑_{i=1}^N I( X_i(t) ≤q X(t), \ \ \forall t \in I),

where G(X_i(t)) indicates the graph of X_i(t), hyp( X(t)) indicates the hypograph of X_i(t).

Value

The function returns a vector containing the values of HI for each element of the functional dataset provided in Data.

References

Lopez-Pintado, S. and Romo, J. (2012). A half-region depth for functional data, Computational Statistics and Data Analysis, 55, 1679-1695.

Arribas-Gil, A., and Romo, J. (2014). Shape outlier detection and visualization for functional data: the outliergram, Biostatistics, 15(4), 603-619.

See Also

MHI, EI, MEI, fData

Examples

N = 20
P = 1e2

grid = seq( 0, 1, length.out = P )

C = exp_cov_function( grid, alpha = 0.2, beta = 0.3 )

Data = generate_gauss_fdata( N,
                             centerline = sin( 2 * pi * grid ),
                             C )
fD = fData( grid, Data )

HI( fD )

HI( Data )

roahd documentation built on Nov. 4, 2021, 1:07 a.m.