PELT.ar: Most recent changepoints from AGG method using censored AR...

In cpcens: Changepoint Analysis using Censored Time Series Data

Description

Detecting most recent changepoints uing AGG method (detect changepoint in univariate time series) after generating censored data from AR model. We use PELT for segmenting a time series into changing mean, assuming normally distributed observations with changing mean but constant variance.

Usage

PELT.ar(data, pen = 200 * log(dim(data)[2]))

Arguments

data	a censored data matrix obtained from AR1.data . And then we add this data matrix column wise and use this as first argument in PELT.ar function.
pen	penalty term, default 200*log(dim(data)[2]). Here dim(data)[2] means consider length of series (n). The PELT function return cpts (Vector of changepoints in segmentation) and F (optimal cost of segmenting series upto time t).

Value

indicates the most recent changepoint in each series .

See Also

AR1.data

Examples

#example
library(cpcens)
# The size of series(n) should be greater than 200.
sim=AR1.data(n = 500, N = 100, K = 5, eps = 1,
  rho = 0.6, mu = 0, siga = 1, rates = c(NA, 0.2), Mrate = 0)
data=sim$data
N=100
agg = apply( data , 2 , sum )
pagg = PELT.ar( agg , 200*log(dim(data)[2]) )
agg.chpts = rep( rev( pagg$cpts )[1] , N )

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