multiple.mrc1: Most recent changepoints from mrc method.

In cpcens: Changepoint Analysis using Censored Time Series Data

Description

Detecting most recent changepoints using mrc method consisting of many related univariate timeseries (Bardwell, Eckley, Fearnhead, and Smith, 2016) and pools information across the time-series by solving the K-median problem using tb.raw (Teitz and Bart, 1968).

Usage

multiple.mrc1(mrc, pmax = 10, alpha = 2, elbow.thresh = 0.5,
  n = 144)

Arguments

mrc	data obtained from mrc.mean1
pmax	Maximum number of most recent changepoints to search for. Default value pmax=10.
alpha	The variable penalty used to penalise the addition of a given changepoint into a given variable. Default value alpha = 2.
elbow.thresh	default 0.5
n	length of series

Value

indicates the most recent changepoint in each series .

See Also

mrc.mean1

Examples

#example(right censoring)
library(cpcens)
data("censoredex")
data=censoredex
n=144
N=100
mrc = mrc.mean1( data , beta = 1.5*log(n) )
c = multiple.mrc1( mrc , pmax=10, alpha = 2 , elbow.thresh = 0.5 , n=144)
p.hat = c$MDL
mrc.chpts = c$locs[[p.hat]][ c$affected[[p.hat]] ]
mrc.chpts

cpcens documentation built on Aug. 2, 2019, 5:05 p.m.