breakfast {breakfast} | R Documentation |
This function estimates the number and locations of change-points in a data sequence, which is modelled as a piecewise-constant function plus i.i.d. Gaussian noise. This is carried out via a two-stage procedure combining solution path generation and model selection methodologies.
breakfast(x, solution.path = NULL, model.selection = NULL)
x |
A numeric vector containing the data to be processed |
solution.path |
A string or a vector of strings containing the name(s) of solution path generating method(s);
if individual methods are accessed via this option, default tuning parameters are used.
Alternatively, you can directly access each solution path generating method via
|
model.selection |
A string or a vector of strings containing the name(s) of model selection method(s);
if individual methods are accessed via this option, default tuning parameters are used.
Alternatively, you can directly access each model selection method via
|
Please also take a look at the vignette for tips/suggestions/examples of using the breakfast package.
An S3 object of class breakfast.cpts
, which contains the following fields:
x Input vector x
cptmodel.list A list containing S3 objects of class cptmodel
; each contains the following fields:
solution.path The solution path method used
model.selection The model selection method used to return the final change-point estimators object
no.of.cpt The number of estimated change-points in the piecewise-constant mean of the vector cptpath.object$x
cpts The locations of estimated change-points in the piecewise-constant mean of the vector cptpath.object$x
. These are the end-points of the corresponding constant-mean intervals
est An estimate of the piecewise-constant mean of the vector cptpath.object$x
; the values are the sample means of the data (replicated a suitable number of times) between each pair of consecutive detected change-points
A. Anastasiou & P. Fryzlewicz (2019). Detecting multiple generalized change-points by isolating single ones. arXiv preprint arXiv:1901.10852.
R. Baranowski, Y. Chen & P. Fryzlewicz (2019). Narrowest-over-threshold detection of multiple change points and change-point-like features. Journal of the Royal Statistical Society: Series B, 81(3), 649–672.
H. Cho & C. Kirch (2021) Two-stage data segmentation permitting multiscale change points, heavy tails and dependence. arXiv preprint arXiv:1910.12486.
P. Fryzlewicz (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6), 2243–2281.
P. Fryzlewicz (2020). Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection. To appear in Journal of the Korean Statistical Society.
P. Fryzlewicz (2018). Tail-greedy bottom-up data decompositions and fast multiple change-point detection. The Annals of Statistics, 46(6B), 3390–3421.
f <- rep(rep(c(0, 1), each = 50), 10)
x <- f + rnorm(length(f)) * .5
breakfast(x)