deconvolve: Deconvolution of a single jump / isolated peak

In lowpassFilter: Lowpass Filtering

Deconvolution of a single jump / isolated peak

Description

For developers only; computes the deconvolution of a single jump or an isolated peak assuming that the observations are lowpass filtered. More details are given in (Pein et al., 2018).

Usage

.deconvolveJump(grid, observations, time, leftValue, rightValue,
                typeFilter, inputFilter, covariances) 
.deconvolvePeak(gridLeft, gridRight, observations, time, leftValue, rightValue,
                typeFilter, inputFilter, covariances, tolerance)

Arguments

grid, gridLeft, gridRight	numeric vectors giving the potential time points of the single jump, of the left and right jump points of the peak, respectively
observations	a numeric vector giving the observed data
time	a numeric vector of length length(observations) giving the time points at which the observations are observed
leftValue, rightValue	single numerics giving the value (conductance level) before and after the jump / peak, respectively
typeFilter, inputFilter	a description of the assumed lowpass filter, usually computed by lowpassFilter
covariances	a numeric vector giving the (regularized) covariances of the observations
tolerance	a single numeric giving a tolerance for the decision whether the left jump point is smaller than the right jump point

Value

For .deconvolveJump a single numeric giving the jump point. For .deconvolvePeak a list containing the entries left, right and value giving the left and right jump point and the value of the peak, respectively.

References

Pein, F., Tecuapetla-Gómez, I., Schütte, O., Steinem, C., Munk, A. (2018) Fully-automatic multiresolution idealization for filtered ion channel recordings: flickering event detection. IEEE Trans. Nanobioscience, 17(3):300-320.

See Also

lowpassFilter

lowpassFilter documentation built on April 30, 2022, 1:06 a.m.