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).
.deconvolveJump(grid, observations, time, leftValue, rightValue,
typeFilter, inputFilter, covariances)
.deconvolvePeak(gridLeft, gridRight, observations, time, leftValue, rightValue,
typeFilter, inputFilter, covariances, tolerance)numeric vectors giving the potential time points of the single jump, of the left and right jump points of the peak, respectively
a numeric vector giving the observed data
a numeric vector of length length(observations) giving the time points at which the observations are observed
single numerics giving the value (conductance level) before and after the jump / peak, respectively
a description of the assumed lowpass filter, usually computed by lowpassFilter
a numeric vector giving the (regularized) covariances of the observations
a single numeric giving a tolerance for the decision whether the left jump point is smaller than the right jump point
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.
Pein, F., Tecuapetla-G<U+00F3>mez, I., Sch<U+00FC>tte, 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.