FDRSeg (version 1.0-2)

FDRSeg-package: FDRSeg

Description

FDRSeg

Details

Package:	FDRSeg
Type:	Package
Version:	1.0-1
Date:	2015-07-28
License:	The GNU General Public License

Index:

computeFdp       Compute false discovery proportion (FDP)
dfdrseg          Piecewise constant regression with D-FDRSeg
evalStepFun      Evaluate step function
fdrseg           Piecewise constant regression with FDRSeg
simulQuantile    Quantile simulations
smuce            Piecewise constant regression with SMUCE         
teethfun         Teeth function
v_measure        Compute V-measure          

References

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.

See Also

smuceR, jsmurf

Examples

# NOT RUN {
library(stepR)

## (I) Independent Gaussian Data
# simulate data
n  <- 300   # number of observations
K  <- 20    # number of change-points
u0 <- teethfun(n, K)
set.seed(2)
Y  <- rnorm(n, u0, 0.3)

# plot data
plot(Y, pch = 20, col = "grey", ylab = "")
lines(u0, type = "s")

# estimate standard deviation
sd <- sdrobnorm(Y)

# simulate quantiles
alpha <- 0.1
qs    <- simulQuantile(1 - alpha, n, type = "smuce")   # for SMUCE
qfs   <- simulQuantile(1 - alpha, n, type = "fdrseg")  # for FDRSeg

# compute estimates
us  <- smuce(Y, qs, sd = sd)   # SMUCE
ufs <- fdrseg(Y, qfs, sd = sd) # FDRSeg

# plot results
lines(evalStepFun(us),  type = "s", col = "blue")
lines(evalStepFun(ufs), type = "s", col = "red")
legend("topleft", c("Truth", "SMUCE", "FDRSeg"), lty = c(1, 1, 1), col = c("black", "blue", "red"))

## (II) Dependent Gaussian Data
# simulate data (a continuous time Markov chain)
ts        <- 0.1  # sampling time
SNR       <- 3    # signal-to-noise ratio
sampling  <- 1e4  # sampling rate 10 kHz
over      <- 10   # tenfold oversampling
cutoff    <- 1e3  # 1 kHz 4-pole Bessel-filter, adjusted for oversampling
simdf     <- dfilter("bessel", list(pole=4, cutoff=cutoff/sampling/over))
transRate <- 50
rates     <- rbind(c(0, transRate), c(transRate, 0))
set.seed(123)
sim <- contMC(ts*sampling, c(0,SNR), rates, sampling = sampling, family = "gaussKern",  
              param = list(df=simdf, over=over, sd=1))
Y   <- sim$data$y
x   <- sim$data$x

# D-FDRseg 
convKern <- dfilter("bessel", list(pole=4, cutoff=cutoff/sampling))$kern
alpha    <- 0.1
r        <- 10    # r could be much larger
qdfs     <- simulQuantile(1 - alpha, ts*sampling, r, "dfdrseg", convKern)
udfs     <- dfdrseg(Y, qdfs, convKern = convKern) 

# plot results
plot(x, Y, pch = 20, col = "grey", xlab="", ylab = "", main = "Simulate Ion Channel Data")
lines(sim$discr, col = "blue")
lines(x, evalStepFun(udfs), col = "red")
legend("topleft", c("Truth", "D-FDRSeg"), lty = c(1, 1), col = c("blue", "red"))
# }