LinSpike {OscillatorGenerator} | R Documentation |
Generation of a Spike Signal with Linear Rise and Decline
Description
This function takes in numeric arguments for a customizable, spike shape, in which rise and decline are modelled by means of a linear function. A discretized time course is returned.
Usage
LinSpike(baseline, peak, period, duty_cycle, peak_pos, trend, duration,
resolution)
Arguments
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillating species (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
peak_pos |
position of the peak value in the active phase of an oscillation cycle (example: |
trend |
percental decrease or increase in the peak value for the successive oscillation cycles; if set to 1, peak value remains unchanged |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Details
Standards:
peak
must be larger thanbaseline
duration
must be larger thanresolution
duration
must be a multiple ofresolution
period
must be a multiple ofresolution
duration
,resolution
,peak
andperiod
must be larger than 0baseline
must be larger or equal to 0duty_cycle
must be larger than 0 and smaller or equal to 1trend
must be larger than 0peak_pos
must be larger or equal to 0 and smaller than 1
Value
Returns a matrix with two columns: a time vector and an oscillator abundance vector.
Examples
# test effect of changes in period
m1 = LinSpike(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
m3 = LinSpike(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
par(mfrow = c(3,1))
plot(m1, type = "l", xlab = "time", ylab = "abundance")
plot(m2, type = "l", xlab = "time", ylab = "abundance")
plot(m3, type = "l", xlab = "time", ylab = "abundance")
# test effect of changes in duty_cycle
m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
par(mfrow = c(3,1))
plot(m1, type = "l", xlab = "time", ylab = "abundance")
plot(m2, type = "l", xlab = "time", ylab = "abundance")
plot(m3, type = "l", xlab = "time", ylab = "abundance")
# test effect of changes in peak_pos
m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.6, trend = 1, duration = 500, resolution = 0.1)
m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.9, trend = 1, duration = 500, resolution = 0.1)
par(mfrow = c(3,1))
plot(m1, type = "l", xlab = "time", ylab = "abundance")
plot(m2, type = "l", xlab = "time", ylab = "abundance")
plot(m3, type = "l", xlab = "time", ylab = "abundance")
# test effect of changes in trend
m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.3, trend = 0.7, duration = 500, resolution = 0.1)
m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1)
m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6,
peak_pos = 0.3, trend = 1.3, duration = 500, resolution = 0.1)
par(mfrow = c(3,1))
plot(m1, type = "l", xlab = "time", ylab = "abundance")
plot(m2, type = "l", xlab = "time", ylab = "abundance")
plot(m3, type = "l", xlab = "time", ylab = "abundance")