BS.univar: Standard binary segmentation for univariate mean change...

In changepoints: A Collection of Change-Point Detection Methods

Standard binary segmentation for univariate mean change points detection.

Description

Perform standard binary segmentation for univariate mean change points detection.

Usage

BS.univar(y, s, e, delta = 2, level = 0)

Arguments

y	A numeric vector of observations.
s	A integer scalar of starting index.
e	A integer scalar of ending index.
delta	A positive numeric scalar of minimum spacing.
level	Should be fixed as 0.

Value

An object of class "BS", which is a list with the following structure:

S	A vector of estimated change point locations (sorted in strictly increasing order).
Dval	A vector of values of CUSUM statistic.
Level	A vector representing the levels at which each change point is detected.
Parent	A matrix with the starting indices on the first row and the ending indices on the second row.

Author(s)

Haotian Xu

References

Wang, Yu and Rinaldo (2020) <doi:10.1214/20-EJS1710>.

See Also

thresholdBS for obtaining change points estimation, tuneBSunivar for a tuning version.

Examples

set.seed(0)
cpt_true = c(20, 50, 170)
y = rnorm(300) + c(rep(0,20),rep(2,30),rep(0,120),rep(2,130))
temp = BS.univar(y, 1, length(y), delta = 5)
plot.ts(y)
points(x = tail(temp$S[order(temp$Dval)],4),
       y = y[tail(temp$S[order(temp$Dval)],4)], col = "red")
BS_result = thresholdBS(temp, tau = 4)
BS_result
print(BS_result$BS_tree, "value")
print(BS_result$BS_tree_trimmed, "value")
cpt_hat = sort(BS_result$cpt_hat[,1]) # the threshold tau is specified to be 4
Hausdorff.dist(cpt_hat, cpt_true)
cpt_LR = local.refine.univar(cpt_hat, y)
Hausdorff.dist(cpt_LR, cpt_true)

changepoints documentation built on Sept. 4, 2022, 5:06 p.m.