kcpRS: KCP on the running statistics

Description

Given a user-specified function RS_fun to compute the running statistics (see runMean, runVar, runAR and runCorr), a KCP permutation test (see permTest) is first implemented to test whether there is at least one significant change point, then through model selection most optimal number of change points is chosen.

Usage

kcpRS(data, RS_fun, RS_name, wsize = 25, nperm = 1000, Kmax = 10,
  alpha = 0.05, varTest = FALSE, ncpu = 1)
# S3 method for kcpRS
plot(x, ...)
# S3 method for kcpRS
print(x, kcp_details = TRUE, ...)
# S3 method for kcpRS
summary(object, ...)

Value

RS_name: Name indicated for the monitored running statistic
RS: Dataframe of running statistics with rows corresponding to the time window and columns corresponding to the (combination of) variable(s) on which the running statistics were computed
wsize: Selected window size
varTest: Selected choice of implementation for varTest
nperm: Selected number of permutations
alpha: Selected significance level of the permutation test
subTest_alpha: Significance level of each subtest. If varTest=0, subTest_alpha is equal to alpha since only the variance drop test is implemented. If varTest=1, subTest_alpha=alpha/2 since two subtests are carried out and Bonferonni correction is applied.
BestK: Optimal number of change points
changePoints: Change point location(s)
p_var_test: P-value of the variance test
p_varDrop_test: P-value of the variance drop test
CPs_given_K: A matrix comprised of the minimized variance criterion Rmin and the optimal change point location(s) for each k from 1 to Kmax

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 wsize and wstep as input and return a dataframe of running statistics as output. The rows of this dataframe should correspond to the windows and the columns should correspond to the variable(s) on which the running statistics were computed.
RS_name: Name of the monitored running statistic.
wsize: Window size
nperm: Number of permutations used in the permutation test
Kmax: Maximum number of change points desired
alpha: Significance level of the permutation test
varTest: If set to FALSE, only the variance DROP test is implemented, and if set to TRUE, both the variance test and the variance DROP tests are implemented.
ncpu: number of cpu cores to use
x: An object of the type produced by kcpRS
...: Further plotting arguments.
kcp_details: If TRUE, then the matrix of optimal change points solutions given k is displayed. If FALSE, then this output is suppressed.
object: An object of the type produced by kcpRS_workflow

References

Cabrieto, J., Tuerlinckx, F., Kuppens, P., Wilhelm, F., Liedlgruber, M., & Ceulemans, E. (2018). Capturing correlation changes by applying kernel change point detection on the running correlations. Information Sciences, 447, 117-139. https://doi.org/10.1016/j.ins.2018.03.010

Cabrieto, J., Adolf, J., Tuerlinckx, F., Kuppens, P., & Ceulemans, E. (2018). Detecting long-lived autodependency changes in a multivariate system via change point detection and regime switching models. Scientific Reports, 8, 15637, 1-15. https://doi.org/10.1038/s41598-018-33819-8

Examples

Run this code

phase1=cbind(rnorm(50,0,1),rnorm(50,0,1)) #phase1: Means=0
phase2=cbind(rnorm(50,1,1),rnorm(50,1,1)) #phase2: Means=1
X=rbind(phase1,phase2)
res=kcpRS(data=X,RS_fun=runMean,RS_name="Mean",wsize=25,
nperm=1000,Kmax=10,alpha=.05,varTest=FALSE,ncpu=1)

summary(res)
plot(res)

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