model.lp {breakfast}  R Documentation 
Estimating changepoints in the piecewiseconstant mean of a noisy data sequence via the localised pruning
Description
This function estimates the number and locations of changepoints in the piecewiseconstant mean of a noisy data sequence via the localised pruning method, which performs a Schwarz criterionbased model selection on the given candidate set in a localised way.
Usage
model.lp(
cptpath.object,
min.d = 5,
penalty = c("log", "polynomial"),
pen.exp = 1.01,
do.thr = TRUE,
th.const = 0.5
)
Arguments
cptpath.object 
A solutionpath 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 
Details
Further information can be found in Cho and Kirch (2022).
Value
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 changepoint estimators object, here its value is 
no.of.cpt 
The number of estimated changepoints in the piecewiseconstant mean of the vector 
cpts 
The locations of estimated changepoints in the piecewiseconstant mean of the vector 
est 
An estimate of the piecewiseconstant mean of the vector 
References
H. Cho & C. Kirch (2022) Twostage data segmentation permitting multiscale change points, heavy tails and dependence. Annals of the Institute of Statistical Mathematics, 74(4), 653–684.
See Also
sol.idetect
, sol.idetect_seq
, sol.not
, sol.tguh
, sol.wbs
, sol.wbs2
, breakfast
Examples
f < rep(rep(c(0, 1), each = 50), 10)
x < f + rnorm(length(f)) * .5
model.lp(sol.not(x))