hybrid.cpt {breakfast} | R Documentation |
This function estimates the number and locations of change-points in the piecewise-constant mean of the noisy input vector, combining the Tail-Greedy Unbalanced Haar and Adaptive Wild Binary Segmentation methods (see Details for the relevant literature references). The constant means between each pair of neighbouring change-points are also estimated. The method works best when the noise in the input vector is independent and identically distributed Gaussian.
hybrid.cpt(x, M = 1000, sigma = stats::mad(diff(x)/sqrt(2)), th.const = 1, p = 0.01, minseglen = 1, bal = 1/20, num.zero = 10^(-5))
x |
A vector containing the data in which you wish to find change-points. |
M |
The same as the corresponding parameter in |
sigma |
The same as the corresponding parameter in |
th.const |
The same as the corresponding parameter in |
p |
The same as the corresponding parameter in |
minseglen |
The same as the corresponding parameter in |
bal |
The same as the corresponding parameter in |
num.zero |
The same as the corresponding parameter in |
This is a hybrid method, which first estimates the number of change-points using
tguh.cpt
and then estimates their locations using wbs.K.cpt
.
The change-point detection algorithms used in tguh.cpt
are: the
Tail-Greedy Unbalanced Haar method as described in "Tail-greedy bottom-up data
decompositions and fast multiple change-point detection", P. Fryzlewicz (2017),
preprint, and Adaptive Wild Binary Segmentation as described in "Data-adaptive Wild Binary Segmentation",
P. Fryzlewicz (2017), in preparation as of September 28th, 2017.
A list with the following components:
est |
The estimated piecewise-constant mean of |
no.of.cpt |
The estimated number of change-points in the piecewise-constant mean of |
cpt |
The estimated locations of change-points in the piecewise-contant mean of |
Piotr Fryzlewicz, p.fryzlewicz@lse.ac.uk
segment.mean
, wbs.bic.cpt
,
wbs.thresh.cpt
, wbs.cpt
, tguh.cpt
, wbs.K.cpt
teeth <- rep(rep(0:1, each=5), 20) teeth.noisy <- teeth + rnorm(200)/5 teeth.cleaned <- hybrid.cpt(teeth.noisy) ts.plot(teeth.cleaned$est)