change_FF: Change Point Analysis Of Functional Data Without Dimension...
In SonmezOzan/fChange_0.2.0: Change Point Analysis in Functional Data

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	change_FF(fdobj, M = 1000, h = 0, plot = FALSE, ...)

`fdobj`	A functional data object of class '`fd`'
`M`	Number of monte carlo simulations used to get the critical values. The default value is `M=1000`
`h`	The window parameter for the estimation of the long run covariance kernel. The default value is `h=0`, i.e., it assumes iid data
`plot`	If `TRUE` plot of the functional data before and after the estimated change and plot of the estimated change function is given
`...`	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+)

`pvalue`	Approximate p value for testing whether there is a significant change in the mean function
`change`	Estimated change location
`DataBefore`	Data before the estimated change
`DataAfter`	Data after the estimated change
`MeanBefore`	Mean function before the estimated change
`MeanAfter`	Mean function after the estimated change
`change_fun`	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)

change_fPCA

# 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