MFT.variance {MFT} | R Documentation |
MFT.variance
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)