Description Usage Arguments Details Value References See Also Examples
This function tests whether there is a significant change in the mean function of the functional data, and it will give an estimate for the location of the change. The procedure is based on the standard L-2 norm and hence does not depend on any dimension reduction technique such as fPCA.
1 |
fdobj |
A functional data object of class ' |
M |
Number of monte carlo simulations used to get the critical values. The default value is |
h |
The window parameter for the estimation of the long run covariance kernel. The default
value is |
plot |
If |
... |
Further arguments to pass |
This function dates and detects changes in the mean function of functional data using a fully functional technique that does not dependent on dimension reduction. For more details, see Aue, Rice, Sonmez (2017+)
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Approximate p value for testing whether there is a significant change in the mean function |
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Estimated change location |
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Data before the estimated change |
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Data after the estimated change |
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Mean function before the estimated change |
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Mean function after the estimated change |
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Estimated change function |
Aue A., Rice G., Sonmez O. (2017+), Detecting and dating structural breaks in functional data without dimension reduction (https://arxiv.org/pdf/1511.04020.pdf)
1 2 3 4 5 | # generate functional data
fdata = fun_IID(n=100, nbasis=21)
# insert an artifiical change
data_c = insert_change(fdata, k=21, change_location = 0.5, SNR=1)$fundata
change_FF(data_c)$change
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