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

The multiple filter test for rate change detection in point processes on the line.

1 2 3 4 5 6 7 | ```
MFT.rate(Phi, m = 0, rescale = TRUE, cutout = TRUE, autoset.d_H = TRUE,
S = NULL, E = NULL, H = NULL, d = NULL, alpha = 0.05, sim = 10000,
method = "asymptotic", Q = NA, blocksize = NA, perform.CPD = TRUE,
plot.CPD = TRUE, print.output = 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.rate = FALSE,
plot.cp = FALSE, breaks = NULL)
``` |

`Phi` |
numeric vector of increasing events, input point process |

`m` |
non-negative integer, dependence parameter: serial corellation up to order m is respected for the estimation of rho |

`rescale` |
logical, if TRUE statistic G is rescaled to statistic R |

`cutout` |
logical, if TRUE for every point, for which the estimated rho becomes negative, the h-neighborhood of G (resp. R) is set to zero. This might only occur, if m > 0 |

`autoset.d_H` |
logical, automatic choice of window size H and step size d |

`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 smallest element must be >= d and the largest =< (T/2). H is automatically set if autoset.d_H = TRUE |

`d` |
numeric, > 0, step size delta at which processes are evaluated. d is automatically set if autoset.d_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", "bootstrap" 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 "bootstrap": Q is deried by (Block)-Bootstrapping; possibly set number of simulations (sim) and blocksize (blocksize), If "fixed": Q may be set automatically (Q) |

`Q` |
numeric, rejection threshold, default: Q is simulated according to sim and alpha. |

`blocksize` |
NA or integer >= 1, if method == 'bootstrap', blocksize determines the size of blocks (number of life times) for bootstrapping |

`perform.CPD` |
logical, if TRUE change point detection algorithm is performed |

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

`print.output` |
logical, if TRUE results are printed to the console |

`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.rate` |
logical, indicates if a legend of estimated rates is plotted |

`plot.cp` |
logical, indicates if a legend of detected CPs is plotted |

`breaks` |
integer, > 0, number of breaks in rate histogram |

invisible

`M` |
test statistic |

`Q` |
rejection threshold |

`sim` |
number of simulations of the limit process (approximation of Q) |

`blocksize` |
size of blocks (number of life times) for bootstrapping (approximation of Q) |

`rescale` |
states whether statistic G is rescaled to R |

`m` |
order of respected serial correlation (m-dependence) |

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

`rate` |
estimated mean rates 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 |

`d` |
step size delta at which processes were evaluated |

`alpha` |
significance level |

`cutout` |
states whether cutout was used (see arguments) |

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>

Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017). Multi-scale detection of rate changes in spike trains with weak dependencies. Journal of Computational Neuroscience, 42 (2), 187-201. <doi:10.1007/s10827-016-0635-3>

`MFT.m_est, MFT.variance, MFT.mean`

1 2 3 4 5 6 7 8 9 | ```
# Rate change detection in Poisson process
# with three change points (at t = 250, 600 and 680)
set.seed(0)
Phi1 <- runif(rpois(1,lambda=390),0,250)
Phi2 <- runif(rpois(1,lambda=380),250,600)
Phi3 <- runif(rpois(1,lambda=200),600,680)
Phi4 <- runif(rpois(1,lambda=400),680,1000)
Phi <- sort(c(Phi1,Phi2,Phi3,Phi4))
MFT.rate(Phi,breaks=30)
``` |

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