sol.idetect {breakfast}

Solution path generation via the Isolate-Detect method

Description

This function arranges all possible change-points in the mean of the input vector, or in its linear trend, in the order of importance, via the Isolate-Detect (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. type = "const", type = "lin.cont", type = "lin.discont" mean, respectively, that the signal (mean of x) is piecewise constant, piecewise linear and continuous, and piecewise linear but not necessarily continuous. If not given, the default is type = "const"

thr_ic_cons

A positive real number with default value equal to 0.9. It is used to create the solution path for the piecewise-constant 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 type == "lin.cont" or type == "lin.discont"

points

A positive integer with default value equal to 3. It defines the distance between two consecutive end- or start-points of the right- or left-expanding intervals, as described in the Isolate-Detect methodology.

Details

The Isolate-Detect method and its algorithm is described in "Detecting multiple generalized change-points by isolating single ones", A. Anastasiou & P. Fryzlewicz (2022), Metrika, https://doi.org/10.1007/s00184-021-00821-6.

Value

An S3 object of class cptpath, which contains the following fields:

solutions.nested

TRUE, i.e., the change-point outputs are nested

solution.path

Locations of possible change-points in the mean of x, arranged in decreasing order of change-point importance

solution.set

Empty list

x

Input vector x

type

The input parameter type

cands

Matrix of dimensions length(x) - 1 by 4. The first two columns are (start, end)-points of the detection intervals of the corresponding possible change-point location in the third column. The fourth column is a measure of strength of the corresponding possible change-point. The order of the rows is the same as the order returned in solution.path

method

The method used, which has value "idetect" here

References

A. Anastasiou & P. Fryzlewicz (2022). Detecting multiple generalized change-points by isolating single ones. Metrika, https://doi.org/10.1007/s00184-021-00821-6.

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)