permTest: KCP Permutation Test

In kcpRS: Kernel Change Point Detection on the Running Statistics

KCP Permutation Test

Description

The KCP permutation test implements the variance test and the variance drop test to determine if there is at least one change point in the running statistics

Usage

permTest(
  data,
  RS_fun,
  wsize = 25,
  nperm = 1000,
  Kmax = 10,
  alpha = 0.05,
  varTest = FALSE
)

Arguments

data	data N x v dataframe where N is the number of time points and v the number of variables
RS_fun	Running statistics function: Should require the time series and wsize as input and return a dataframe of running statistics as output. This output dataframe should have rows that correspond to the time windows and columns that correspond to the variable(s) on which the running statistics were computed.
wsize	Window size
nperm	Number of permutations to be used in the permutation test
Kmax	Maximum number of change points desired
alpha	Significance level of the permutation test
varTest	If FALSE, only the variance DROP test is implemented, and if TRUE, both the variance and the variance DROP tests are implemented.

Value

sig	Significance of having at least one change point. 0 - Not significant, 1- Significant
p_var_test	P-value of the variance test.
p_varDrop_test	P-value of the variance drop test.
perm_rmin	A matrix of minimized variance criterion for the permuted data.
perm_rmin_without_NA	A matrix of minimized variance criterion for the permuted data without NA values.

References

Cabrieto, J., Tuerlinckx, F., Kuppens, P., Hunyadi, B., & Ceulemans, E. (2018). Testing for the presence of correlation changes in a multivariate time series: A permutation based approach. Scientific Reports, 8, 769, 1-20. doi:10.1038/s41598-017-19067-2

kcpRS documentation built on Oct. 25, 2023, 5:07 p.m.