Description Usage Arguments Details Value Examples
This function tests and detects changes jointly in the eigenvalue of the covariance operator.
1 | eval_joint(fdobj, d, h = 2, mean_change = FALSE, delta = 0.1, M = 1000)
|
fdobj |
A functional data object of class ' |
d |
Number of eigenvalues to include in testing. |
h |
The window parameter for the estimation of the long run covariance matrix. The default
value is |
mean_change |
If |
delta |
Trimming parameter to estimate the covariance function using partial sum estimates. |
M |
Number of monte carlo simulations used to get the critical values. The default value is |
This function dates and detects changes in the joint eigenvalues that is defined by d
of the covariance function.
The critical values are approximated via M
Monte Carlo simulations.
|
Approximate p value for testing whether there is a significant change in the desired eigenvalue of the covariance operator |
|
Estimated change location |
|
Estimated eigenvalues before the change |
|
Estimated eigenvalues after the change |
1 2 3 | # generate functional data
fdata = fun_IID(n=100, nbasis=21)
eval_joint(fdata, 2)
|
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