change_point_analyzer: change_point_analyzer

In ChangePointTaylor: Identify Changes in Mean

change_point_analyzer

Description

a simple implementation of the change in mean detection methods developed by Wayne Taylor and utilized in his Change Point Analyzer software. The package recursively uses the 'MSE' change point calculation to identify candidate change points. Taylor's backwards elimination process is then employed to come up with a final set of change points.

Usage

change_point_analyzer(
  x,
  labels = NA,
  n_bootstraps = 1000,
  min_candidate_conf = 0.5,
  min_tbl_conf = 0.9,
  CI = 0.95
)

Arguments

x	a numeric vector
labels	a vector the same length as x. Will generate labels for the change points in the output dataframe.
n_bootstraps	an integer value. Determines the number of bootstraps when calculating the change confidence level.
min_candidate_conf	a value between 0 and 1. The minimum change confidence level to become a candidate change point before re-estimation and backwards elimination.
min_tbl_conf	a value between 0 and 1. The minimum change confidence level below which a candidate change point will be eliminated after re-estimation and backwards elimination.
CI	a value between 0 and 1. The value of the confidence interval.

Value

a dataframe containing the change points, their confidence levels, and other relevant information

References

Taylor, W. A. (2000). Change-point analysis: a powerful new tool for detecting changes.

Examples

x <- US_Trade_Deficit$deficit_billions
label_vals <- US_Trade_Deficit$date

change_point_analyzer(x)

change_point_analyzer(x, label = label_vals)

change_point_analyzer(x, label = label_vals, n_bootstraps = 10000)

change_point_analyzer(x, label = label_vals, min_candidate_conf = 0.66,  min_tbl_conf = 0.95)

ChangePointTaylor documentation built on March 18, 2022, 6:57 p.m.