fChange (version 0.2.1)

eval_joint: Detecting Changes Jointly in the Eigenvalues of the Covariance Operator of the 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

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

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.

Examples

# NOT RUN {
# generate functional data
fdata = fun_IID(n=100, nbasis=21)
eval_joint(fdata, 2)
# }