nmcd: Non-parametric Multiple Change-points Detection

In nmcdr: Non-parametric Multiple Change-points Detection

Description

Detect multiple change-points using a non-parametric maximum likelihood approach.

Usage

nmcd(x, kmax, cpp, ncp = length(cpp), n)

Arguments

x	data vector
kmax	upper bound of the number of change-points
cpp	positions of candidate change-points. usually returned by functionisp. if this parameter is not assigned, the isp function will be called to assign it.
ncp	the number of the candidate change-points.
n	length of the data.

Details

NMCD use DP algorithm to select change-points, while the true number of change-points is determined by the Bayesian information criterion(BIC).

Value

a list with class nmcd is returned with elements:

npp	the true number of change-points
cpp	positions of true change-points
data	raw data, this is not printed on screen by default
bic	minimal BIC value gained.

Note

memory consume may be significant with large data.

References

Changliang Zou, Guosheng Yin, Long Feng, Zhaojun Wang. Non-parametric Maximum Likelihood Approach to Multiple Change-points Problem

Examples

t = nmcd(Blocks)
summary(t)
plot(t)

nmcdr documentation built on April 14, 2017, 4:52 p.m.