wbs.K.cpt {breakfast} | R Documentation |
K
change-points in the mean of a vector using the Adaptive WBS methodThis function estimates the number and locations of change-points in the
piecewise-constant mean of the noisy input vector, using the Adaptive Wild Binary Segmentation
method (see Details for the relevant literature reference). The number of change-points
is exactly K
. 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. As a by-product, the function
also computes the entire solution path, i.e. all estimated n-1
change-point locations
(where n
is the length of the input data) sorted from the most to the least important.
wbs.K.cpt(x, K, M = 1000)
x |
A vector containing the data in which you wish to find change-points. |
K |
The number of change-points you wish to detect. |
M |
The number of randomly selected sub-segments of the data on which to build the CUSUM statistics on each recursively identified interval in the Adaptive Wild Binary Segmentation algorithm. |
This function should only be used if (a) you know exactly how many change-points you wish
to detect, or (b) you wish to order all possible change-points from the most to the least
important. If you need a function to estimate the number of change-points for you,
try segment.mean
(for a
default recommended estimation technique), wbs.thresh.cpt
, wbs.bic.cpt
,
wbs.cpt
(if you require an (Adaptive) WBS-based technique), tguh.cpt
(if you require a TGUH-based technique), or hybrid.cpt
(to use a hybrid between TGUH and Adaptive WBS). If you are unsure where to start, try
segment.mean
.
The change-point detection algorithm used in wbs.K.cpt
is the
Adaptive Wild Binary Segmentaton method 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 |
cpt.sorted |
The list of all possible change-point locations, sorted from the most to the least likely |
Piotr Fryzlewicz, p.fryzlewicz@lse.ac.uk
segment.mean
, wbs.thresh.cpt
,
wbs.cpt
, tguh.cpt
, hybrid.cpt
, wbs.bic.cpt
teeth <- rep(rep(0:1, each=5), 20) teeth.noisy <- teeth + rnorm(200)/5 teeth.cleaned <- wbs.K.cpt(teeth.noisy, 39) teeth.cleaned$cpt teeth.cleaned <- wbs.K.cpt(teeth.noisy, 78) teeth.cleaned$cpt teeth.cleaned$cpt.sorted