FPOP2D: FPOP2D

In lpishchagina/FPOPdim2: FPOPdim2

Description

Detecting changepoints using the functional pruning optimal partitioning method (fpop) in bivariate time series.

Usage

FPOP2D(data1, data2, penalty, type)

Arguments

data1	is a vector of data1(a univariate time series).
data2	is a vector of data2(a univariate time series).
penalty	is a value of penalty (a non-negative real number).
type	is a value defining the type of geometry for FPOP-pruning: type=1: ("intersection" of sets), approximation - rectangle; type=2:("intersection" of sets)"minus"("union" of sets), approximation - rectangle; type=3: (last disk)"minus"("union" of sets), approximation - disk.

Value

a list of 4 elements = (chpts, means1, means2, globalCost).

chpts

is the vector of changepoints.

means1

is the vector of successive means for data1.

means2

is the vector of successive means for data2.

globalCost

is a number equal to the global cost.

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Examples

FPOP2D (data1 = c(0,0,0,1,1,1), data2 = c(2,2,2,0,0,0), penalty = 2*log(6),  type = 1) 

lpishchagina/FPOPdim2 documentation built on Dec. 21, 2021, 11:50 a.m.