MFT.peaks: MFT.peaks

In MFT: The Multiple Filter Test for Change Point Detection

Description

The multiple filter test for peak detection in time series or sequences of random variables

Usage

MFT.peaks(x, autoset.H = TRUE, S = NULL, E = NULL, H = NULL,
  alpha = 0.05, method = "asymptotic", sim = 10000, Q = NA,
  blocksize = NA, two.sided = FALSE, perform.CPD = TRUE,
  print.output = TRUE)

Arguments

x	numeric vector, input sequence of random variables
autoset.H	logical, automatic choice of window size H
S	numeric, start of time interval, default: NULL, if NULL then 1 is chosen
E	numeric, end of time interval, default: NULL, if NULL then length(X) is chosen, needs E > S
H	vector, window set H, the smallest element must >= 3 be and the largest =< (T/2). H is automatically set if autoset.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 (Gaussian process); 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
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
two.sided	logical, if TRUE a two sided test is performed and also negative peaks are considered in peak detection
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)
CP	set of change points estmated by the multiple filter algorithm, increasingly ordered in time
S	start of time interval
E	end of time interval
Tt	length of time interval
H	window set
alpha	significance level
two.sided	logigal, if TRUE also negative peaks are considered
perform.CPD	logical, if TRUE change point detection algorithm was performed
tech.var	list of technical variables with processes x and D_ht
type	type of MFT which was performed: "peaks"

Author(s)

Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider

References

Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+). Peak detection in times series

See Also

MFT.filterdata, plot.MFT, summary.MFT, MFT.mean, MFT.rate, MFT.variance

Examples

# Normal distributed sequence with 2 peaks
set.seed(12)
m <- c(rep(0,30),seq(0,3,length.out = 100),seq(3,0,length.out = 80),rep(0,10),
       seq(0,6,length.out = 50),seq(6,0,length.out = 50),rep(0,30))
x <- rnorm(length(m),m)
mft <- MFT.peaks(x)
plot(mft)
# Set additional parameters (window set)
mft <- MFT.peaks(x,autoset.H = FALSE, H =c(30,60,90))
plot(mft)

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