MFT.rate: MFT.rate

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

View source: R/MFT.rate.R

Description

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

Usage

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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)

Arguments

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

Value

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)

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>

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>

See Also

MFT.m_est, MFT.variance, MFT.mean

Examples

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# 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)

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

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