model.thresh: Estimating change-points in the piecewise-constant or...

In breakfast: Methods for Fast Multiple Change-Point/Break-Point Detection and Estimation

Estimating change-points in the piecewise-constant or piecewise-linear mean of a noisy data sequence via thresholding

Description

This function estimates the number and locations of change-points in the piecewise-constant or piecewise-linear mean of a noisy data sequence via thresholding.

Usage

model.thresh(cptpath.object, sigma = NULL, th.const = NULL)

Arguments

cptpath.object	A solution-path object, returned by a sol.[name] routine. The cptpath.object$type variable decides the model type: piecewise-constant (type == "const"), piecewise-linear and continuous (type == "lin.cont") or piecewise-linear and discontinuous (type == "lin.discont"). In the piecewise-linear model (whether continuous or not), the output of sol.idetect_seq or sol.not should be supplied as cptpath.object. Note that the field cptpath.object$x contains the input data sequence.
sigma	An estimate of the standard deviation of the noise in the data cptpath.object$x. Can be a functional of cptpath.object$x or a specific value if known. The default in the piecewise-constant model is the Median Absolute Deviation of the vector diff(cptpath.object$x)/sqrt(2), tuned to the Gaussian distribution. In the piecewise-linear models, diff(cptpath.object$x, differences = 2)/sqrt(6) is used by default. Note that model.thresh works particularly well when the noise is i.i.d. Gaussian.
th.const	A positive real number used to define the threshold for the detection process. The default used in the piecewise-constant model is 1.15, while in the piecewise-linear model, the value is taken equal to 1.4.

Value

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

solution.path	The solution path method used to obtain cptpath.object
type	The model type used, inherited from the given cptpath.object
model.selection	The model selection method used to return the final change-point estimators object, here its value is "thresh"
no.of.cpt	The number of estimated change-points
cpts	The locations of estimated change-points
est	An estimate of the mean of the vector cptpath.object$x

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))
model.thresh(sol.idetect_seq(x))

breakfast documentation built on Sept. 30, 2024, 9:43 a.m.