MFT.mean: MFT.mean

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/MFT.mean.R

Description

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

Usage

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MFT.mean(Y, rescale = TRUE, autoset.H = TRUE, S = NULL, E = NULL,
  H = NULL, alpha = 0.05, sim = 10000, method = "asymptotic", Q = NA,
  perform.CPD = TRUE, print.output = TRUE, plot.CPD = TRUE, col = NULL,
  ylab1 = NULL, ylab2 = NULL, cex.legend = 1.2, cex.diamonds = 1.4,
  main = TRUE, plot.Q = TRUE, plot.M = TRUE, plot.h = TRUE,
  plot.mean = FALSE, plot.cp = FALSE, plot.process = TRUE)

Arguments

Y

numeric vector, input sequence of random variables.

rescale

logical, if TRUE statistic G is rescaled to statistic R

autoset.H

logical, automatic choice of window size H

S

numeric, start of time interval, default: Smallest multiple of d that lies beyond min(Phi)

E

numeric, end of time interval, default: Smallest multiple of d that lies beyond max(Phi), needs E > S.

H

vector, window set H, all elements must be increasing ordered multiples of d, the largest element must be =< (T/2). H is automatically set if autoset.H = TRUE

alpha

numeric, in (0,1), significance level

sim

integer, > 0, No of simulations of limit process (for approximation of Q), default = 10000

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 automatically (Q)

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

plot.CPD

logical, if TRUE CPD-scenario is plotted. Only active if perform.CPD == TRUE

col

"gray" or vector of colors of length(H). Colors for (R_ht) plot, default: NULL -> rainbow colors from blue to red.

ylab1

character, ylab for 1. graphic

ylab2

character, ylab for 2. graphic

cex.legend

numeric, size of annotations in plot

cex.diamonds

numeric, size of diamonds that indicate change points

main

logical, indicates if title and subtitle are plotted

plot.Q

logical, indicates if rejection threshold Q is plotted

plot.M

logical, indicates if test statistic M is plotted

plot.h

logical, indicates if a legend for the window set H is plotted

plot.mean

logical, indicates if a legend of estimated rates is plotted

plot.cp

logical, indicates if a legend of detected CPs is plotted

plot.process

logical, indicates if there should be a plot of Y as second graphic.

Value

invisible

M

test statistic

Q

rejection threshold

sim

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

CP

set of change points estmated by the multiple filter algorithm, increasingly ordered in time

rate

estimated mean values between adjacent change points

SWD

sets of change points estimated from preprocessing single window detections

S

start of time interval

E

end of time interval

H

window set

alpha

significance level

Author(s)

Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider

References

Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014). A multiple filter test for the detection of rate changes in renewal processes with varying variance. The Annals of Applied Statistics 8(4): 2027-67 <doi:10.1214/14-AOAS782>

See Also

MFT.rate, MFT.variance

Examples

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# Normal distributed sequence with 3 change points of the mean (at n=100, 130, 350)
Y1 <- rnorm(400,0,1); Y2 <- rnorm(400,3,1); Y3 <- rnorm(400,5,1); Y4 <- rnorm(600,4.6,1)
Y  <- c(Y1[1:100],Y2[101:130],Y3[131:350],Y4[351:600])
MFT.mean(Y)
# Set additional parameters (window set)
MFT.mean(Y,autoset.H=FALSE,H=c(40,80,160))

MFT documentation built on Sept. 15, 2017, 5:05 p.m.