MFT.mean: MFT.mean

In MFT: The Multiple Filter Test for Change Point Detection

Description

The multiple filter test for mean change detection in time series or sequences of random variables.

Usage

MFT.mean(X, autoset.H = TRUE, S = NULL, E = NULL, H = NULL,
  alpha = 0.05, method = "asymptotic", sim = 10000,
  rescale = FALSE, Q = NA, perform.CPD = TRUE, print.output = TRUE)

Arguments

X	numeric vector, input sequence of random variables
autoset.H	logical, automatic choice of window size H
S	numeric, start of time interval, default: NULL, if NULL then 1 is chosen
E	numeric, end of time interval, default: NULL, if NULL then length(X) is chosen, needs E > S.
H	vector, window set H, all elements must be increasing, the largest element must be =< (T/2). H is automatically set if autoset.H = TRUE
alpha	numeric, in (0,1), significance level
method	either "asymptotic" or "fixed", defines how threshold Q is derived, default: "asymptotic", If "asymptotic": Q is derived by simulation of limit process L (Brownian motion); possible set number of simulations (sim), If "fixed": Q may be set manually (Q)
sim	integer, > 0, No of simulations of limit process (for approximation of Q), default = 10000
rescale	logical, if TRUE statistic G is rescaled to statistic R, default = FALSE
Q	numeric, rejection threshold, default: Q is simulated according to sim and alpha.
perform.CPD	logical, if TRUE change point detection algorithm is performed
print.output	logical, if TRUE results are printed to the console

Value

invisible

M	test statistic
Q	rejection threshold
method	how threshold Q was derived, see 'Arguments' for detailed description
sim	number of simulations of the limit process (approximation of Q)
rescale	states whether statistic G is rescaled to R
CP	set of change points estmated by the multiple filter algorithm, increasingly ordered in time
means	estimated mean values between adjacent change points
S	start of time interval
E	end of time interval
Tt	length of time interval
H	window set
alpha	significance level
perform.CPD	logical, if TRUE change point detection algorithm was performed
tech.var	list of technical variables with processes X and G_ht or R_ht
type	type of MFT which was performed: "mean"

Author(s)

Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider

References

Michael Messer, Stefan Albert and Gaby Schneider (2018). The multiple filter test for change point detection in time series. Metrika <doi:10.1007/s00184-018-0672-1>

See Also

plot.MFT, summary.MFT, MFT.rate, MFT.variance, MFT.peaks

Examples

# Normal distributed sequence with 3 change points of the mean (at n=100, 155, 350)
set.seed(50)
X1   <- rnorm(400,0,1); X2 <- rnorm(400,3,1); X3 <- rnorm(400,5,1); X4 <- rnorm(600,4.6,1)
X    <- c(X1[1:100],X2[101:155],X3[156:350],X4[351:600])
mft  <- MFT.mean(X)
plot(mft)
# Set additional parameters (window set)
mft2 <- MFT.mean(X,autoset.H=FALSE,H=c(80,160,240))
plot(mft2)

MFT documentation built on May 2, 2019, 10:58 a.m.