Description Usage Arguments Details Value References See Also Examples
This function tests whether there is a significant change in the mean function of functional data, and it gives an estimate
of the location of the change. The procedure will reduce the dimension of the functional data using functional
principal component analysis and will use d
leading principal curves to carry out the change point
analysis. The projection dimension d
can be chosen via total variation explained (TVE) using the function
pick_dim
.
1 | change_fPCA(fdobj, d, M = 1000, h = 0, plot = FALSE, ...)
|
fdobj |
A functional data object of class ' |
d |
Number of principal components |
M |
Number of monte carlo simulations 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 functions performs structural break analysis for the functional data using an fPCA based initial dimension reduction. It
is recommended that the dimension of the subspace, d
, that the functional observations are projected onto should be selected based on
TVE using pick_dim
.
|
An approximate p value for testing whether there is a significant change in the mean function |
|
Estimated change location |
|
Data before the estimated change |
|
Data after the estimated change |
|
Mean function before the estimated change |
|
Mean function after the estimated change |
|
Estimated change function |
Berkes, I., Gabrys, R.,Hovarth, L. & P. Kokoszka (2009)., Detecting changes in the mean of functional observations Journal of the Royal Statistical Society, Series B 71, 927<e2><80><93>946
Aue, A., Gabrys, R.,Hovarth, L. & P. Kokoszka (2009)., Estimation of a change-point in the mean function of functional dataJournal of Multivariate Analysis 100, 2254<e2><80><93>2269.
1 2 3 4 5 6 | # 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)$fundata
d.hat = pick_dim(data_c, 0.9)$d
change_fPCA(data_c, d=d.hat)$change
|
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