MFT.variance: MFT.variance

In MFT: The Multiple Filter Test for Change Point Detection

Description

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

Usage

MFT.variance(Phi, rcp = NULL, autoset.d_H = TRUE, S = NULL,
  E = NULL, d = NULL, H = NULL, alpha = 0.05,
  method = "asymptotic", sim = 10000, Q = NA, perform.CPD = TRUE,
  print.output = TRUE)

Arguments

Phi	numeric vector of increasing events, input point process
rcp	vector, rate CPs of Phi (if MFT for the rates is used: as CP[,1]), default: constant rate
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", 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 manually (Q)
sim	integer, > 0, No of simulations of limit process (for approximation of Q), default = 10000
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

Value

invisible

M	test statistic
varQ	rejection threshold
method	how threshold Q was derived, see 'Arguments' for detailed description
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
var	estimated variances 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
perform.CPD	logical, if TRUE change point detection algorithm was performed
tech.var	list of technical variables with processes Phi and G_ht
type	type of MFT which was performed: "variance"

Author(s)

Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider

References

Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper and Gaby Schneider (2017) Multi-scale detection of variance changes in renewal processes in the presence of rate change points. Journal of Time Series Analysis, <doi:10.1111/jtsa.12254>

See Also

MFT.rate, plot.MFT, summary.MFT, MFT.mean, MFT.peaks

Examples

# Rate and variance change detection in Gamma process 
# (rate CPs at t=30 and 37.5, variance CPs at t=37.5 and 52.5) 
set.seed(51)
mu <- 0.03; sigma <- 0.01
p1 <- mu^2/sigma^2; lambda1 <- mu/sigma^2
p2 <- (mu*0.5)^2/sigma^2; lambda2 <- (mu*0.5)/sigma^2
p3 <- mu^2/(sigma*1.5)^2; lambda3 <- mu/(sigma*1.5)^2
p4 <- mu^2/(sigma*0.5)^2; lambda4 <- mu/(sigma*0.5)^2
Phi <- cumsum(c(rgamma(1000,p1,lambda1),rgamma(500,p2,lambda2),
rgamma(500,p3,lambda3),rgamma(300,p4,lambda4)))
# rcp  <- MFT.rate(Phi)$CP[,1] # MFT for the rates
rcp <- c(30,37.5) # but here we assume known rate CPs
mft <- MFT.variance(Phi,rcp=rcp) # MFT for the variances
plot(mft)

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