Description Usage Arguments Value References See Also Examples
Reconstructs a piecewise constant function to which white noise was added and the sum filtered afterwards.
Deprecation warning: This function is mainly used for patchlamp recordings and may be transferred to a specialised package.
1 2 3 4 
y 
a numeric vector containing the serial data 
x 
a numeric vector of the same length as 
x0 
a single numeric giving the last unobserved sample point directly before sampling started 
q 
threshold value, by default chosen automatically 
alpha 
significance level; if set to a value in (0,1), 
r 
numer of simulations; if specified along 
lengths 
length of intervals considered; by default up to a sample size of 1000 all lengths, otherwise only dyadic lengths 
param 
a 
rm.out 
a 
jumpint 

confband 

An object object of class stepfit
that contains the fit; if jumpint == TRUE
function jumpint
allows to extract the 1  alpha
confidence interval for the jumps, if confband == TRUE
function confband
allows to extract the 1  alpha
confidence band.
Hotz, T., Schütte, O., 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), 376–386.
stepbound
, bounds
, family, MRC.asymptotic
, sdrobnorm
, stepfit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  # simulate filtered ion channel recording with two states
set.seed(9)
# sampling rate 10 kHz
sampling < 1e4
# tenfold oversampling
over < 10
# 1 kHz 4pole Besselfilter, adjusted for oversampling
cutoff < 1e3
df.over < dfilter("bessel", list(pole=4, cutoff=cutoff / sampling / over))
# two states, leaving state 1 at 10 Hz, state 2 at 20 Hz
rates < rbind(c(0, 10), c(20, 0))
# simulate 0.5 s, level 0 corresponds to state 1, level 1 to state 2
# noise level is 0.3 after filtering
sim < contMC(0.5 * sampling, 0:1, rates, sampling=sampling, family="gaussKern",
param = list(df=df.over, over=over, sd=0.3))
plot(sim$data, pch = ".")
lines(sim$discr, col = "red")
# fit using filter corresponding to sample rate
df < dfilter("bessel", list(pole=4, cutoff=cutoff / sampling))
fit < jsmurf(sim$data$y, sim$data$x, param=df, r=1e2)
lines(fit, col = "blue")
# fitted values take filter into account
lines(sim$data$x, fitted(fit), col = "green3", lty = 2)

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