t: time vector
y: univariate data vector of a smooth function
doPlotting: Plots the time course and the characteristic values
useSplines: Approximates the data y by a spline (default true)
percentageRecovery: percentage of maximum to define the recovery time (default: 20%)
characteristics: Struct with several fields
.peakAmplitude
.timeOfPeak
.recoveryTime
.signalDuration
.areaUnderCurve
.areaUnderFirstMomentCurve
.meanValue
.meanResidenceTime
.lastValue
category: See below.
Description
Most functions belong to one of four categories:
1. Monotonic increase from low to high level
2. Monotonic decrease from high to low level
3. Increase from low to high level and then decrease to second low level
4. Decrease from high to low level and then increase to second high level
We rule out cases 1 and 2 and determine the category 3 or 4
by investigating y(1) - y(2).
In cases 1 and 2 the peak belongs to the maximum value, i.e. at
t = 0 or t = end.
In case 4 the peak rather belongs to a minimum.
The spline based approach is slightly more accurate, because the
maximum and threshold crossings are determined based on the spline
and not the data points. This requires y to be a sufficently smooth
function, which may be a problem in dynamic systems with e.g.
pulsed input functions.
The approach is discussed e.g. in
'Principles behind the multifarious control of signal transduction'
by Hornberg et al., FEBS, 2004.
Implementation by Stefan Hengl and Thomas Maiwald.
See also