sol.idetect {breakfast}  R Documentation 
Solution path generation via the IsolateDetect method
Description
This function arranges all possible changepoints in the mean of the input vector, or in its linear trend, in the order of importance, via the IsolateDetect (ID) method. It is developed to be used with the sdll and information criterion (ic) model selection rules.
Usage
sol.idetect(
x,
type = "const",
thr_ic_cons = 0.9,
thr_ic_lin = 1.25,
points = 3
)
Arguments
x 
A numeric vector containing the data to be processed. 
type 
The model type considered. 
thr_ic_cons 
A positive real number with default value equal to 0.9. It is used to create the solution path for the piecewiseconstant model. The lower the value, the longer the solution path. 
thr_ic_lin 
A positive real number with default value 1.25. Used to create the solution path if 
points 
A positive integer with default value equal to 3. It defines the distance between two consecutive end or startpoints of the right or leftexpanding intervals, as described in the IsolateDetect methodology. 
Details
The IsolateDetect method and its algorithm is described in "Detecting multiple generalized changepoints by isolating single ones", A. Anastasiou & P. Fryzlewicz (2022), Metrika, https://doi.org/10.1007/s00184021008216.
Value
An S3 object of class cptpath
, which contains the following fields:
solutions.nested 

solution.path 
Locations of possible changepoints in the mean of 
solution.set 
Empty list 
x 
Input vector 
type 
The input parameter 
cands 
Matrix of dimensions length( 
method 
The method used, which has value "idetect" here 
References
A. Anastasiou & P. Fryzlewicz (2022). Detecting multiple generalized changepoints by isolating single ones. Metrika, https://doi.org/10.1007/s00184021008216.
See Also
sol.idetect_seq
, sol.not
, sol.wbs
, sol.wbs2
, sol.tguh
Examples
r3 < rnorm(1000) + c(rep(0,300), rep(2,200), rep(4,300), rep(0,200))
sol.idetect(r3)