ChangePoints {offlineChange} | R Documentation |
Detect the number and locations of change points based on minimizing within segment quadratic loss and applying penalized model selection approach with restriction of largest candidate number of change points.
ChangePoints(x, point_max = 5, penalty = "bic", seg_min = 1, num_init = NULL, cpp = TRUE)
x |
The data to find change points. |
point_max |
The largest candidate number of change points. |
penalty |
Penalty type term. Default is "bic". Users can use other penalty term. |
seg_min |
Minimal segment size, must be positive integer. |
num_init |
The number of repetition times, in order to avoid local minimal. Default is squared root of number of observations. Must be integer. |
cpp |
Option to accelerate using rcpp. Default is TRUE. |
The K change points form K+1 segments (1 2 ... change_point(1)) ... (change_point(K) ... N).
num_change_point |
optimal number of change points. |
change_point |
location of change points. |
J. Ding, Y. Xiang, L. Shen, and V. Tarokh, Multiple Change Point Analysis: Fast Implementation and Strong Consistency. IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4495-4510, 2017.
a<-matrix(rnorm(40,mean=-1,sd=1),nrow=20,ncol=2) b<-matrix(rnorm(120,mean=0,sd=1),nrow=60,ncol=2) c<-matrix(rnorm(40,mean=1,sd=1),nrow=20,ncol=2) x<-rbind(a,b,c) ChangePoints(x,point_max=5) ChangePoints(x,point_max=5,penalty="hq")