model.ic {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 sSIC (strengthened Schwarz information criterion) method.
model.ic(cptpath.object, alpha = 1.01, q.max = NULL)
cptpath.object |
A solution-path object, returned by a |
alpha |
The parameter associated with the sSIC. The default value is 1.01. Note that the SIC is recovered when alpha = 1. |
q.max |
The maximum number of change-points allowed. If nothing or |
The model selection method for algorithms that produce nested solution path is described in "Wild binary segmentation for multiple change-point detection", P. Fryzlewicz (2014), The Annals of Statitics, 42: 2243–2281. The corresponding description for those that produce non-nested solution set can be found in "Narrowest-over-threshold detection of multiple change points and change-point-like features", R. Baranowski, Y. Chen and P. Fryzlewicz (2019), Journal of Royal Statistical Society: Series B, 81(3), 649–672.
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 |
P. Fryzlewicz (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6), 2243–2281.
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.
sol.idetect
, sol.not
, sol.tguh
, sol.wbs
, sol.wbs2
, breakfast
x <- c(rep(0, 100), rep(1, 100), rep(0, 100)) + rnorm(300) model.ic(sol.wbs(x)) model.ic(sol.not(x))