multiple.mrc: Most recent changepoints from mrc method using censored AR/MA...
In cpcens: Changepoint Analysis using Censored Time Series Data

Description Usage Arguments Value References See Also Examples

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

1 2	multiple.mrc(mrc, pmax = 10, alpha = 2, elbow.thresh = 0.5, n = 500)

`mrc`	data obtained from mrc.mean
`pmax`	Maximum number of most recent changepoints to search for. Default value pmax=10.
`alpha`	The variable specific 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

indicates the most recent changepoint in each series .

Teitz, M. B. and Bart, P. (1968). Heuristic methods for estimating the generalized vertex median of a weighted graph. Operations Research, 16(5):955–961.

Bardwell, L., Fearnhead, P., Eckley, I. A., Smith, S., and Spott, M. (2019). Most recent changepoint detection in panel data. Technometrics, 61(1):88–98.

mrc

#'#example(left censoring)
library(cpcens)
n=300
N=100
# Generate censored data using MA model
sim=MA1.data(n = 300, N = 100, K = 5, eps = 1,
rho = 0.4, mu = 0, siga = 1, rates = c(0.6,NA), Mrate = 0)
data=sim$data
mrc = mrc.mean( data , beta = 1.5*log(n) )
c = multiple.mrc(  mrc , pmax=10, alpha = 2 , elbow.thresh = 0.5, n=500 )
p.hat = c$MDL
mrc.chpts = c$locs[[p.hat]][ c$affected[[p.hat]] ]
mrc.chpts