jcp (version 1.2)

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

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

# NOT RUN {
# 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)


# }