| Title: | The Multiple Filter Test for Change Point Detection |
|---|---|
| Description: | Provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. 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 <doi:10.1214/14-AOAS782>. Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points <doi:10.1111/jtsa.12254>. Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies <doi:10.1007/s10827-016-0635-3>. Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series <doi:10.1007/s00184-018-0672-1>. Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series. |
| Authors: | Michael Messer, Stefan Albert, Solveig Plomer, Gaby Schneider |
| Maintainer: | Michael Messer <[email protected]> |
| License: | GPL-3 |
| Version: | 2.0 |
| Built: | 2024-10-31 21:10:25 UTC |
| Source: | CRAN |
Naive routine to remove trend from the data.
MFT.filterdata(x, filterwidth = NULL, filtersigma = NULL)MFT.filterdata(x, filterwidth = NULL, filtersigma = NULL)
x |
numeric vector, input sequence of random variables. |
filterwidth |
postive interger, < length(x)/2, number of data points left and right of the current value that are taken into account for Gaussian smoothing. |
filtersigma |
numeric, > 0, standard deviation of Gassian kernel. |
invisible
xfiltered |
filtered data (for filtering the first and last (filterwidth many) data points of the original series cannot be evaluated and are omited) |
xraw |
orignal data, but the first and last (filterwidth many) data point are omitted |
xtrend |
trend that is removed by filtering. That is xfiltered = xraw - xtrend |
x |
orignal data |
filterwidth |
number of data points left and right of the current value that are taken into account for Gaussian smoothing |
filtersigma |
standard deviation of the Gaussian kernel |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+). Peak detection in times series
MFT.peaks, plot.MFT, summary.MFT, MFT.rate, MFT.variance, MFT.mean
set.seed(0) # Normally distributed sequence with negative trend x <- rnorm(1000,mean=seq(5,0,length.out=1000)) MFT.filterdata(x) MFT.filterdata(x,filterwidth=200,filtersigma=200)set.seed(0) # Normally distributed sequence with negative trend x <- rnorm(1000,mean=seq(5,0,length.out=1000)) MFT.filterdata(x) MFT.filterdata(x,filterwidth=200,filtersigma=200)
Naive routine for the estimation of the order of serial correlation (m-dependence) in point processes.
MFT.m_est(Phi, n = 200, maxlag = 10, alpha = 0.05, plot = TRUE)MFT.m_est(Phi, n = 200, maxlag = 10, alpha = 0.05, plot = TRUE)
Phi |
point process, vector of time stamps |
n |
positive integer, number of life times used in segments for estimation of serial correlation |
maxlag |
non-negative integer, maximal lag up to which serial correlations are calculated |
alpha |
numeric, in (0,1), significance level |
plot |
logical, if TRUE, estimation procedure is plotted |
m_est |
non-negative integer, estimated order of serial correlation (m-dependence) |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
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>
MFT.rate, plot.MFT, summary.MFT, MFT.variance, MFT.mean, MFT.peaks
# 1. Independent life times (m=0) set.seed(117) n <- 5000 Phi1 <- cumsum(rexp(n,3.5)) Phi2 <- cumsum(rexp(n,5)) Phi3 <- cumsum(rexp(n,2)) Phi <- c(Phi1[Phi1<=200],Phi2[Phi2>200 & Phi2<400],Phi3[Phi3>400 & Phi3<700]) MFT.m_est(Phi) # 2. Point process simulated according to model # X_i = a_0 X_i + a_1 X_{i-1} + ... a_m X_{i-m} # with life times X_i gamma-distributed, 2 change points and true m = 3. set.seed(210) Tt <- 3000 m <- 3 a <- c(1,0.5,0.25,0.125) mu <- c(0.5,1,2)/(sum(a)) sigmaX <- sqrt(0.225/(sum(a^2))) shape <- mu^2/sigmaX^2; rate <- mu/sigmaX^2 len <- 10000 # build auxiliary processes X1 <- rgamma(len,rate=rate[1],shape=shape[1]); M1 <- embed(X1,m+1) v1 <- cumsum(as.vector(M1 %*% a)); v1 <- v1[v1<Tt] X2 <- rgamma(len,rate=rate[2],shape=shape[2]); M2 <- embed(X2,m+1) v2 <- cumsum(as.vector(M2 %*% a)); v2 <- v2[v2<Tt] X3 <- rgamma(len,rate=rate[3],shape=shape[3]); M3 <- embed(X3,m+1) v3 <- cumsum(as.vector(M3 %*% a)); v3 <- v3[v3<Tt] # build final point process with cps at 100 and 200 Phi <- c(v1[v1<Tt/3],v2[v2>Tt/3 & v2<(2/3)*Tt],v3[v3>(2/3)*Tt]) # estimate m MFT.m_est(Phi)# 1. Independent life times (m=0) set.seed(117) n <- 5000 Phi1 <- cumsum(rexp(n,3.5)) Phi2 <- cumsum(rexp(n,5)) Phi3 <- cumsum(rexp(n,2)) Phi <- c(Phi1[Phi1<=200],Phi2[Phi2>200 & Phi2<400],Phi3[Phi3>400 & Phi3<700]) MFT.m_est(Phi) # 2. Point process simulated according to model # X_i = a_0 X_i + a_1 X_{i-1} + ... a_m X_{i-m} # with life times X_i gamma-distributed, 2 change points and true m = 3. set.seed(210) Tt <- 3000 m <- 3 a <- c(1,0.5,0.25,0.125) mu <- c(0.5,1,2)/(sum(a)) sigmaX <- sqrt(0.225/(sum(a^2))) shape <- mu^2/sigmaX^2; rate <- mu/sigmaX^2 len <- 10000 # build auxiliary processes X1 <- rgamma(len,rate=rate[1],shape=shape[1]); M1 <- embed(X1,m+1) v1 <- cumsum(as.vector(M1 %*% a)); v1 <- v1[v1<Tt] X2 <- rgamma(len,rate=rate[2],shape=shape[2]); M2 <- embed(X2,m+1) v2 <- cumsum(as.vector(M2 %*% a)); v2 <- v2[v2<Tt] X3 <- rgamma(len,rate=rate[3],shape=shape[3]); M3 <- embed(X3,m+1) v3 <- cumsum(as.vector(M3 %*% a)); v3 <- v3[v3<Tt] # build final point process with cps at 100 and 200 Phi <- c(v1[v1<Tt/3],v2[v2>Tt/3 & v2<(2/3)*Tt],v3[v3>(2/3)*Tt]) # estimate m MFT.m_est(Phi)
The multiple filter test for mean change detection in time series or sequences of random variables.
MFT.mean(X, autoset.H = TRUE, S = NULL, E = NULL, H = NULL, alpha = 0.05, method = "asymptotic", sim = 10000, rescale = FALSE, Q = NA, perform.CPD = TRUE, print.output = TRUE)MFT.mean(X, autoset.H = TRUE, S = NULL, E = NULL, H = NULL, alpha = 0.05, method = "asymptotic", sim = 10000, rescale = FALSE, Q = NA, perform.CPD = TRUE, print.output = TRUE)
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, all elements must be increasing, the largest element must be =< (T/2). H is automatically set if autoset.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 |
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. |
perform.CPD |
logical, if TRUE change point detection algorithm is performed |
print.output |
logical, if TRUE results are printed to the console |
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) |
rescale |
states whether statistic G is rescaled to R |
CP |
set of change points estmated by the multiple filter algorithm, increasingly ordered in time |
means |
estimated mean values between adjacent change points |
S |
start of time interval |
E |
end of time interval |
Tt |
length of time interval |
H |
window set |
alpha |
significance level |
perform.CPD |
logical, if TRUE change point detection algorithm was performed |
tech.var |
list of technical variables with processes X and G_ht or R_ht |
type |
type of MFT which was performed: "mean" |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
Michael Messer, Stefan Albert and Gaby Schneider (2018). The multiple filter test for change point detection in time series. Metrika <doi:10.1007/s00184-018-0672-1>
plot.MFT, summary.MFT, MFT.rate, MFT.variance, MFT.peaks
# Normal distributed sequence with 3 change points of the mean (at n=100, 155, 350) set.seed(50) X1 <- rnorm(400,0,1); X2 <- rnorm(400,3,1); X3 <- rnorm(400,5,1); X4 <- rnorm(600,4.6,1) X <- c(X1[1:100],X2[101:155],X3[156:350],X4[351:600]) mft <- MFT.mean(X) plot(mft) # Set additional parameters (window set) mft2 <- MFT.mean(X,autoset.H=FALSE,H=c(80,160,240)) plot(mft2)# Normal distributed sequence with 3 change points of the mean (at n=100, 155, 350) set.seed(50) X1 <- rnorm(400,0,1); X2 <- rnorm(400,3,1); X3 <- rnorm(400,5,1); X4 <- rnorm(600,4.6,1) X <- c(X1[1:100],X2[101:155],X3[156:350],X4[351:600]) mft <- MFT.mean(X) plot(mft) # Set additional parameters (window set) mft2 <- MFT.mean(X,autoset.H=FALSE,H=c(80,160,240)) plot(mft2)
The multiple filter test for peak detection in time series or sequences of random variables
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)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)
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 |
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" |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+). Peak detection in times series
MFT.filterdata, plot.MFT, summary.MFT, MFT.mean, MFT.rate, MFT.variance
# 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)# 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)
The multiple filter test for rate change detection in point processes on the line.
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)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)
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 |
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" |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
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>
MFT.variance, MFT.m_est, plot.MFT, summary.MFT, MFT.mean, MFT.peaks
# 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)# 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)
The multiple filter test for variance change detection in point processes on the line.
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)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)
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 |
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" |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
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>
MFT.rate, plot.MFT, summary.MFT, MFT.mean, MFT.peaks
# 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)$CP[,1] # MFT for the rates rcp <- c(30,37.5) # but here we assume known rate CPs mft <- MFT.variance(Phi,rcp=rcp) # MFT for the variances plot(mft)# Rate and variance change detection in Gamma process # (rate CPs at t=30 and 37.5, variance CPs at t=37.5 and 52.5) set.seed(51) mu <- 0.03; sigma <- 0.01 p1 <- mu^2/sigma^2; lambda1 <- mu/sigma^2 p2 <- (mu*0.5)^2/sigma^2; lambda2 <- (mu*0.5)/sigma^2 p3 <- mu^2/(sigma*1.5)^2; lambda3 <- mu/(sigma*1.5)^2 p4 <- mu^2/(sigma*0.5)^2; lambda4 <- mu/(sigma*0.5)^2 Phi <- cumsum(c(rgamma(1000,p1,lambda1),rgamma(500,p2,lambda2), rgamma(500,p3,lambda3),rgamma(300,p4,lambda4))) # rcp <- MFT.rate(Phi)$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)
Plot method for class 'mft'.
## S3 method for class 'MFT' plot(x, 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, breaks = NULL, wid = NULL, ...)## S3 method for class 'MFT' plot(x, 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, breaks = NULL, wid = NULL, ...)
x |
object of class MFT |
col |
"gray" or vector of colors of length(H). Colors for (G_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 |
breaks |
integer, >0, number of breaks in rate histogram |
wid |
integer, >0, width of bars in variance histogram |
... |
additional parameters |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
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>
MFT.rate, MFT.variance, MFT.mean, MFT.peaks, summary.MFT
# 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)# 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)
Summary method for class 'mft'.
## S3 method for class 'MFT' summary(object, ...)## S3 method for class 'MFT' summary(object, ...)
object |
object of class MFT |
... |
additional parameters |
Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider
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>
MFT.rate, MFT.variance, MFT.mean, MFT.peaks, plot.MFT
# 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) summary(mft)# 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) summary(mft)