MBD_relative {roahd}

Relative Modified Band Depth of functions in a univariate functional dataset

Description

This function computes Modified Band Depth (BD) of elements of a univariate functional dataset with respect to another univariate functional dataset.

Usage

MBD_relative(Data_target, Data_reference)

## S3 method for class 'fData'
MBD_relative(Data_target, Data_reference)

## Default S3 method:
MBD_relative(Data_target, Data_reference)

Arguments

Data_target

is the univariate functional dataset, provided either as an fData object or in matrix form (N observations as rows and P measurements as columns), whose MBD have to be computed with respect to the reference dataset.

Data_reference

is the dataset, provided either as an fData object or in matrix form (N observations as rows and P measurements as columns), containing the reference univariate functional data that must be used to compute the MBD of elements in Data_target. If Data_target is fData, it must be of class fData.

Details

Given a univariate functional dataset of elements X_1(t), X_2(t), \ldots, X_N(t), and another univariate functional dataset of elements Y_1(t), Y_2(t) \ldots, Y_M(t), defined over the same compact interval I=[a,b], this function computes the MBD of elements of the former with respect to elements of the latter, i.e.:

MBD( X_i( t ) ) = {M \choose 2 }^{-1} \sum_{1 \leq i_1 < i_2 \leq M} \tilde{\lambda}\big( {t : \min( Y_{i_1}(t), Y_{i_2}(t) ) \leq X_i(t) \leq \max( Y_{i_1}(t), Y_{i_2}(t) ) } \big),

\forall i = 1, \ldots, N, where \tilde{\lambda}(\cdot) is the normalized Lebesgue measure over I=[a,b], that is \tilde{\lambda(A)} = \lambda( A ) / ( b - a ).

Value

The function returns a vector containing the MBD of elements in Data_target with respect to elements in Data_reference.

See Also

MBD, BD, BD_relative, fData

Examples

grid = seq( 0, 1, length.out = 1e2 )

Data_ref = matrix( c( 0  + sin( 2 * pi * grid ),
                      1  + sin( 2 * pi * grid ),
                      -1 + sin( 2 * pi * grid ) ),
                   nrow = 3, ncol = length( grid ), byrow = TRUE )

Data_test_1 = matrix( c( 0.6 + sin( 2 * pi * grid ) ),
                      nrow = 1, ncol = length( grid ), byrow = TRUE )

Data_test_2 = matrix( c( 0.6 + sin( 2 * pi * grid ) ),
                      nrow = length( grid ), ncol = 1, byrow = TRUE )

Data_test_3 = 0.6 + sin( 2 * pi * grid )

Data_test_4 = array( 0.6 + sin( 2 * pi * grid ), dim = length( grid ) )

Data_test_5 = array( 0.6 + sin( 2 * pi * grid ), dim = c( 1, length( grid ) ) )

Data_test_6 = array( 0.6 + sin( 2 * pi * grid ), dim = c( length( grid ), 1 ) )

Data_test_7 = matrix( c( 0.5  + sin( 2 * pi * grid ),
                         -0.5 + sin( 2 * pi * grid ),
                         1.1 + sin( 2 * pi * grid ) ),
                      nrow = 3, ncol = length( grid ), byrow = TRUE )

fD_ref = fData( grid, Data_ref )
fD_test_1 = fData( grid, Data_test_1 )
fD_test_2 = fData( grid, Data_test_2 )
fD_test_3 = fData( grid, Data_test_3 )
fD_test_4 = fData( grid, Data_test_4 )
fD_test_5 = fData( grid, Data_test_5 )
fD_test_6 = fData( grid, Data_test_6 )
fD_test_7 = fData( grid, Data_test_7 )

MBD_relative( fD_test_1, fD_ref )
MBD_relative( Data_test_1, Data_ref )

MBD_relative( fD_test_2, fD_ref )
MBD_relative( Data_test_2, Data_ref )

MBD_relative( fD_test_3, fD_ref )
MBD_relative( Data_test_3, Data_ref )

MBD_relative( fD_test_4, fD_ref )
MBD_relative( Data_test_4, Data_ref )

MBD_relative( fD_test_5, fD_ref )
MBD_relative( Data_test_5, Data_ref )

MBD_relative( fD_test_6, fD_ref )
MBD_relative( Data_test_6, Data_ref )

MBD_relative( fD_test_7, fD_ref )
MBD_relative( Data_test_7, Data_ref )