PELT.meanvar.exp: Multiple Changes in Mean and Variance using PELT pruned method - Exponential Data

Description

Calculates the optimal positioning and number of changepoints for Exponential data using PELT pruned method.

Usage

PELT.meanvar.exp(data, pen=0)

Arguments

data

A vector containing the data within which you wish to find changepoints.

pen

Numeric value of the linear penalty function. This value is used in the decision for each individual changepoint so that in total the penalty is k*pen where k is the optimal number of changepoints detected.

Value

A vector of the changepoint locations is returned:
cptVector containing the changepoint locations for the penalty supplied. This always ends with n.

Details

This function is used to find a multiple changes in mean and variance for data that is assumed to be Exponential distributed. The value returned is the result of testing H0:existing number of changepoints against H1: one extra changepoint using the log of the likelihood ratio statistic coupled with the penalty supplied. The PELT method keeps track of the optimal number and location of changepoints as it passes through the data.

References

Change in Exponential model: Chen, J. and Gupta, A. K. (2000) Parametric statistical change point analysis, Birkhauser

PELT Algorithm: Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost, JASA 107(500), 1590--1598

Examples

Run this code

# Example of multiple changes in mean and variance at 50,100,150 in simulated Exponential data
set.seed(1)
x=c(rexp(50,rate=1),rexp(50,rate=3),rexp(50,rate=1),rexp(50,rate=10))
PELT.meanvar.exp(x,pen=2*log(200)) # returns c(53,100,150,200)

# Example no change in rate parameter
set.seed(1)
x=rexp(200,rate=1)
PELT.meanvar.exp(x,pen=2*log(200)) # returns 200 to show no change in mean or variance has been
#found

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