kerseg2: Kernel-based change-point detection for changed-interval...

In kerSeg: New Kernel-Based Change-Point Detection

Kernel-based change-point detection for changed-interval alternatives

Description

This function finds an interval in the sequence where their underlying distribution differs from the rest of the sequence.

Usage

kerseg2(n, K, r1=1.2, r2=0.8, l0=0.05*n, l1=0.95*n,
   pval.appr=TRUE, skew.corr=TRUE, pval.perm=FALSE, B=100)

Arguments

n	The number of observations in the sequence.
K	The kernel matrix of observations in the sequence.
r1	The constant in the test statistics \textrm{Z}_{W,r1}(t_{1},t_{2}).
r2	The constant in the test statistics \textrm{Z}_{W,r2}(t_{1},t_{2}).
l0	The minimum length of the interval to be considered as a changed interval.
l1	The maximum length of the interval to be considered as a changed interval.
pval.appr	If it is TRUE, the function outputs the p-value approximation based on asymptotic properties.
skew.corr	This argument is useful only when pval.appr=TRUE. If skew.corr is TRUE, the p-value approximation would incorporate skewness correction.
pval.perm	If it is TRUE, the function outputs the p-value from doing B permutations, where B is another argument that you can specify. Doing permutation could be time consuming, so use this argument with caution as it may take a long time to finish the permutation.
B	This argument is useful only when pval.perm=TRUE. The default value for B is 100.

Value

Returns a list stat containing the each scan statistic, tauhat containing the estimated changed-interval, appr containing the approximated p-values of the fast tests when argument ‘pval.appr’ is TRUE, and perm containing the permutation p-values of the fast tests and GKCP when argument ‘pval.perm’ is TRUE. See below for more details.

seq	A matrix of each scan statistic (standardized counts).
Zmax	The test statistics (maximum of the scan statistics).
tauhat	An estimate of the two ends of the changed-interval.
fGKCP1_bon	The p-value of \textrm{fGKCP}_{1} obtained by the Bonferroni procedure.
fGKCP1_sim	The p-value of \textrm{fGKCP}_{1} obtained by the Simes procedure.
fGKCP2_bon	The p-value of \textrm{fGKCP}_{2} obtained by the Bonferroni procedure.
fGKCP2_sim	The p-value of \textrm{fGKCP}_{2} obtained by the Simes procedure.
GKCP	The p-value of GKCP obtained by the random permutation.

See Also

kerSeg-package, kerseg2, gaussiankernel, kerseg1

Examples

## Sequence 3: change in both the mean and variance happens on an interval.
d = 50
mu = 2
sigma = 0.5
tau1 = 25
tau2 = 35
n = 50
set.seed(1)
y1 = matrix(rnorm(d*tau1),tau1)
y2 = matrix(rnorm(d*(tau2-tau1),mu/sqrt(d),sigma), tau2-tau1)
y3 = matrix(rnorm(d*(n-tau2)), n-tau2)
y = rbind(y1, y2, y3)
K = gaussiankernel(y)
a = kerseg2(n, K, pval.perm=TRUE, B=1000)

kerSeg documentation built on Aug. 23, 2023, 1:07 a.m.