jcp {jcp}

jcp

Description

Joint change point detection - expectation and variance - via bivariate moving sum statistics

Usage

jcp(x, H = NA, q = NA, alpha = 0.05, sim = 1000, region = "square")

Arguments

x

numeric vector. Input sequence of random variables.

H

NA or numeric vector. Window set. If NA (default), then H is automatically set. If not NA, then H must an increasing vector of positive integers with maximum =< length(x)/2.

q

NA or numeric value. Rejection threshold. If NA (default), then the rejection boundary is derived in simulations (from Gaussian process limit) according to sim and alpha. If not NA, then q is considered predefined and must be set a postive real number.

alpha

numeric value. Significance level. Must be in (0,1), default = 0.05. In case of predefined q, alpha is set to NA.

sim

numeric value. Number of simulations of limit process for approximation of q. Must be positive integer, default = 1000. In case of predefined q, sim is set to NA.

region

character string. Defines rejection region, default = "square". Must be chosen either "square", "circle" or "ellipse".

Value

invisible list

changepoints

detected change points (increasingly ordered)

mean_sd

matrix of estimated means and standard deviations

M

test statistic

q

rejection threshold

H

window set

sim

number of simulations of the limit process (approximation of q)

alpha

significance level

region

rejection region

method

derivation of threshold q, either asymptotic or predefined

x

input sequence

EVrho

list containing the auxiliary processes E, V and correlation rho, for each element of H one list entry

CP_meta

matrix containing meta information of estimation. Estimated change points (increasingly ordered), responsible window h, components E, V and rho of joint statistic at estimated change points (regarding responsible window)

SFA

detected change points of single filter algorithms

Author(s)

Michael Messer

References

Michael Messer (2021) Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547.

See Also

plot.jcp, summary.jcp

Examples

# Normal distributed sequence with 3 change points at
# c1=250 (change in expectation), 
# c2=500 (change in variance) and 
# c3=750 (change in expectation and variance) 
set.seed(0)
m      <- c(8,10,10,3);   s  <- c(4,4,10,5)
x      <- rnorm(1000, mean=rep(m,each=250), sd=rep(s,each=250))
result <- jcp(x)
summary(result)
plot(result)

# Set additional parameters (window set)
result2 <- jcp(x,H=c(80,160,240))
summary(result2)
plot(result2)