safely_detect_changepoints: Identifying Changes in a Series Using PELT Algorithm

In ModelOriented/rSAFE: Surrogate-Assisted Feature Extraction

Identifying Changes in a Series Using PELT Algorithm

Description

The safely_detect_changepoints() function calculates the optimal positioning and number of changepoints for given data and penalty. It uses a PELT algorithm with a nonparametric cost function based on the empirical distribution. The implementation is inspired by the code available on https://github.com/rkillick/changepoint.

Usage

safely_detect_changepoints(data, penalty = "MBIC", nquantiles = 10)

Arguments

data	a vector within which you wish to find changepoints
penalty	penalty for introducing another changepoint, one of "AIC", "BIC", "SIC", "MBIC", "Hannan-Quinn" or numeric non-negative value
nquantiles	the number of quantiles used in integral approximation

Value

a vector of optimal changepoint positions (last observations of each segment)

See Also

safely_transform_continuous

Examples

library(rSAFE)

data <- rep(c(2,7), each=4)
safely_detect_changepoints(data)

set.seed(123)
data <- c(rnorm(15, 0), rnorm(20, 2), rnorm(30, 8))
safely_detect_changepoints(data)
safely_detect_changepoints(data, penalty = 25)

ModelOriented/rSAFE documentation built on Aug. 19, 2022, 2:54 a.m.