eval_joint: Detecting Changes Jointly in the Eigenvalues of the...

In fChange: Change Point Analysis in Functional Data

Description

This function tests and detects changes jointly in the eigenvalue of the covariance operator.

Usage

eval_joint(fdobj, d, h = 2, mean_change = FALSE, delta = 0.1,
  M = 1000)

Arguments

fdobj	A functional data object of class 'fd'
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 h=2.
mean_change	If TRUE then the data is centered considering the change in the mean function.
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 M=1000

Details

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.

Value

pvalue	Approximate p value for testing whether there is a significant change in the desired eigenvalue of the covariance operator
change	Estimated change location
eval_before	Estimated eigenvalues before the change
eval_after	Estimated eigenvalues after the change

Examples

# generate functional data
fdata = fun_IID(n=100, nbasis=21)
eval_joint(fdata, 2)

fChange documentation built on May 2, 2019, 6:43 a.m.