Description Usage Arguments Value References See Also Examples
View source: R/simulQuantile.R
Simulate the quantiles of multiscale statistics for SMUCE, FDRSeg, and D-FDRSeg under null hypothesis.
1 2 | simulQuantile(alpha, n, r = round(50/min(alpha, 1-alpha)),
type = c("smuce","fdrseg","dfdrseg"), convKern, pos = .GlobalEnv)
|
alpha |
a scalar with values in [0, 1]; the |
n |
number of observations |
r |
numer of Monte Carlo simulations |
type |
"smuce" simulate quantile for SMUCE "fdrseg" simulate quantiles for FDRSeg "dfdrseg" simulate quantiles for D-FDRSeg |
convKern |
convolution kernel, only needed when |
pos |
environment for saving the simulations for possible later usage |
A scalar value if type
is chosen as "smuce"; a numeric vector of length n
if type
is chosen as "fdrseg" or "dfdrseg".
Frick, K., Munk, A., and Sieling, H. (2014). Multiscale Change-Point Inference. J. R. Statist. Soc. B, with discussion and rejoinder by the authors, 76:495–580.
Hotz, T., Schuette, O. M., Sieling, H., Polupanow, T., Diederichsen, U., Steinem, C., and Munk, A. (2013). Idealizing ion channel recordings by a jump segmentation multiresolution filter. IEEE Transactions on Nanobioscience, 12(4):376–86.
Li, H., Munk, A., and Sieling, H. (2015). FDR-control in multiscale change-point segmentation. arXiv:1412.5844.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | library(stepR)
# simulate quantiles for independent Gaussian noises
qs <- simulQuantile(0.9, 100, type = "smuce")
qfs <- simulQuantile(0.9, 100, type = "fdrseg")
# plot result
yrng <- range(qs, qfs)
plot(qfs, pch = 20, ylim = yrng, xlab = "n", ylab = "")
abline(h = qs)
# simulate quantiles for dependent Gaussian noises
convKern <- dfilter("bessel")$kern # create digital filters
qdfs <- simulQuantile(0.9, 100, type = "dfdrseg", convKern = convKern)
plot(qdfs, pch = 20, xlab = "n", ylab = "")
|
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