testData | R Documentation |
Generate piecewise stationary time series with independent innovations and change points in the mean.
testData( model = c("custom", "blocks", "fms", "mix", "stairs10", "teeth10")[1], lengths = NULL, means = NULL, sds = NULL, rand.gen = rnorm, seed = NULL, ... )
model |
a string indicating from which model a realisation is to be generated;
possible values are "custom" (for user-specified model
using |
lengths |
use iff |
means |
use iff |
sds |
use iff |
rand.gen |
optional; a function to generate the noise/innovations |
seed |
optional; if a seed value is provided ( |
... |
further arguments to be parsed to |
See Appendix B in the reference for details about the test signals.
a list containing the following entries:
x a numeric vector containing a realisation of the piecewise time series model, given as signal + noise
mu mean vector of piecewise stationary time series model
sigma scaling vector of piecewise stationary time series model
cpts a vector of change points in the piecewise stationary time series model
P. Fryzlewicz (2014) Wild Binary Segmentation for Multiple Change-Point Detection. The Annals of Statistics, Volume 42, Number 6, pp. 2243-2281.
# visualise estimated changepoints by solid vertical lines # and true changepoints by broken vertical lines td <- testData(lengths = c(50, 50, 200, 300, 300), means = c(0, 1, 2, 3, 2.3), sds = rep(1, 5), seed = 123) mbu <- multiscale.bottomUp(td$x) plot(mbu, display = "data") abline(v = td$cpts, col = 2, lwd = 2, lty = 2) # visualise estimated piecewise constant signal by solid line # and true signal by broken line td <- testData("blocks", seed = 123) mlp <- multiscale.localPrune(td$x) plot(mlp, display = "data") lines(td$mu, col = 2, lwd = 2, lty = 2)
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