These functions compute the Modified Epigraph Index of elements of a multivariate functional dataset.
multiMEI(Data, weights = "uniform")# S3 method for mfData
multiMEI(Data, weights = "uniform")
# S3 method for default
multiMEI(Data, weights = "uniform")
specifies the the multivariate functional dataset.
It is either an object of class mfData
or a list of 2-dimensional
matrices having as rows the elements of that component and as columns the
measurements of the functional data over the grid.
either a set of weights (of the same length of Data
) or the string "uniform"
specifying that a set of uniform weights
(of value Data
) is to be used.
The function returns a vector containing the values of MEI of each element of the multivariate functional dataset.
Given a multivariate functional dataset composed of
these functions compute the MEI of each element of the functional dataset, namely:
# NOT RUN {
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
multiMEI( mfD, weights = 'uniform' )
# Non-uniform, custom weights
multiMEI( mfD, weights = c(2/3, 1/3) )
# }
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