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

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

1 2 3 4 5 6 | ```
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)
``` |

`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. |

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 |

Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider

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>

1 2 3 4 5 6 | ```
# 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))
``` |

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