Description Usage Arguments Value Author(s) References Examples
View source: R/test.change.point.R
This function compute the Cramer-von Mises and Kolmogorov-Smirnov test statistics based on the new sequential process of Bucher et al (2014), using multipliers and parallel computing.
1 2 3 4 5 6 7 | test.change.point(
x,
N = 1000,
n_cores = 2,
boot.method = "multipliers",
est = FALSE
)
|
x |
(n x d) matrix of data (observations or pseudo-observations, including residuals), d>=1 |
N |
number of multipliers samples to compute the P-value |
n_cores |
number of cores for parallel computing (default = 2) |
boot.method |
bootstrapping method: 'multipliers' (default, fastest) or 'bootstrap' |
est |
if TRUE, tau is estimated (default = FALSE) |
CVM |
Cramer-von Mises statistic |
KS |
Kolmogorov-Smirnov statistic |
pvalueCVM |
Pvalue for the Cramer-von Mises statistic |
pvalueKS |
Pvalue for theKolmogorov-Smirnov statistic |
tauCVM |
Estimated changepoint using the Cramer-von Mises statistic |
tauKS |
Estimated changepoint using the Kolmogorov-Smirnov statistic |
Bouchra R Nasri and Bruno N Remillard, August 6, 2020
Nasri, B. R. Remillard, B., & Bahraoui, T. (2021). Change-point problems for multivariate time series using pseudo-observations
1 2 | x=matrix(rnorm(600),ncol=3)
out = test.change.point(x)
|
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