# NOT RUN {
set.seed(1)
N <- 200
P <- 100
grid <- seq(0, 1, length.out = P)
# Creating an exponential covariance function to simulate Gaussian data
Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4)
# Simulating (independent) Gaussian functional data with given center and covariance function
Data_1 <- generate_gauss_fdata(
N = N,
centerline = sin(2 * pi * grid),
Cov = Cov
)
Data_2 <- generate_gauss_fdata(
N = N,
centerline = sin(4 * pi * grid),
Cov = Cov
)
Data_3 <- generate_gauss_fdata(
N = N,
centerline = sin(6 * pi * grid),
Cov = Cov
)
# Using the simulated data as (independent) components of a multivariate functional dataset
mfD <- mfData(grid, list(Data_1, Data_2, Data_3))
# }
# NOT RUN {
BCIntervalSpearmanMultivariate(mfD, ordering = "MEI")
# }
# NOT RUN {
# BC intervals contain zero since the functional samples are uncorrelated.
# }
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