model.lp {breakfast} | R Documentation |
This function estimates the number and locations of change-points in the piecewise-constant mean of a noisy data sequence via the localised pruning method, which performs a Schwarz criterion-based model selection on the given candidate set in a localised way.
model.lp(
cptpath.object,
min.d = 5,
penalty = c("log", "polynomial"),
pen.exp = 1.01,
do.thr = TRUE,
th.const = 0.5
)
cptpath.object |
A solution-path object, returned by a |
min.d |
A number specifying the minimal spacing between change points; |
penalty |
A string specifying the type of penalty term to be used in Schwarz criterion; possible values are:
|
pen.exp |
Exponent for the penalty term (see |
do.thr |
If |
th.const |
A constant multiplied to |
Further information can be found in Cho and Kirch (2021).
An S3 object of class cptmodel
, which contains the following fields:
solution.path |
The solution path method used to obtain |
model.selection |
The model selection method used to return the final change-point estimators object, here its value is |
no.of.cpt |
The number of estimated change-points in the piecewise-constant mean of the vector |
cpts |
The locations of estimated change-points in the piecewise-constant mean of the vector |
est |
An estimate of the piecewise-constant mean of the vector |
H. Cho & C. Kirch (2021) Two-stage data segmentation permitting multiscale change points, heavy tails and dependence. arXiv preprint arXiv:1910.12486.
sol.idetect
, sol.idetect_seq
, sol.not
, sol.tguh
, sol.wbs
, sol.wbs2
, breakfast
f <- rep(rep(c(0, 1), each = 50), 10)
x <- f + rnorm(length(f)) * .5
model.lp(sol.not(x))