MFT.rate: MFT.rate

In MFT: The Multiple Filter Test for Change Point Detection

Description

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

Usage

MFT.rate(Phi, m = 0, cutout = TRUE, autoset.d_H = TRUE, S = NULL,
  E = NULL, d = NULL, H = NULL, alpha = 0.05,
  method = "asymptotic", sim = 10000, rescale = FALSE, Q = NA,
  blocksize = NA, perform.CPD = TRUE, print.output = TRUE)

Arguments

Phi	numeric vector of increasing events, input point process
m	non-negative integer, dependence parameter: serial corellation rho up to order m estimated
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.
d	numeric, > 0, step size delta at which processes are evaluated. d is automatically set if autoset.d_H = TRUE
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
alpha	numeric, in (0,1), significance level
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 derived by (Block)-Bootstrapping; possibly set number of simulations (sim) and blocksize (blocksize), 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.
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
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)
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
S	start of time interval
E	end of time interval
Tt	length 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')
perform.CPD	logical, if TRUE change point detection algorithm was performed
tech.var	list of technical variables with processes Phi and G_ht or R_ht
type	type of MFT which was performed: "rate"

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.variance, MFT.m_est, plot.MFT, summary.MFT, MFT.mean, MFT.peaks

Examples

# 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  <- MFT.rate(Phi)
plot(mft)

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