Description Usage Arguments Details Value See Also Examples

View source: R/multivariate_depths.R

These functions compute the Modified Hypograph Index of elements of a multivariate functional dataset.

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`Data` |
specifies the the multivariate functional dataset.
It is either an object of class |

`weights` |
either a set of weights (of the same length of |

Given a multivariate functional dataset composed of *N* elements with
*L* components each, *\mathbf{X_1} =( X^1_1(t),* *X^2_1(t),
…, X^L_1(t))*, and a set of *L* non-negative weights,

* w_1, w_2, …, w_L, \qquad ∑_{i=1}^L w_i = 1,*

these functions compute the MHI of each element of the functional dataset, namely:

* MHI( \mathbf{X_j} ) = ∑_{i=1}^{L} w_i MHI( X^i_j ), \quad \forall
j = 1, … N.*

The function returns a vector containing the values of MHI of each element of the multivariate functional dataset.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
N = 20
P = 1e3
grid = seq( 0, 10, length.out = P )
# Generating an exponential covariance function to be used to simulate gaussian
# functional data
Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.8 )
# First component of the multivariate guassian functional dataset
Data_1 = generate_gauss_fdata( N, centerline = rep( 0, P ), Cov = Cov )
# First component of the multivariate guassian functional dataset
Data_2 = generate_gauss_fdata( N, centerline = rep( 0, P ), Cov = Cov )
mfD = mfData( grid, list( Data_1, Data_2 ) )
# Uniform weights
multiMHI( mfD, weights = 'uniform' )
# Non-uniform, custom weights
multiMHI( mfD, weights = c(2/3, 1/3) )
``` |

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