Description Usage Arguments Value Author(s) See Also Examples
Langevin1D
calculates the Drift and Diffusion vectors (with errors)
for a given time series.
1 2 
data 
a vector containing the time series or a timeseries object. 
bins 
a scalar denoting the number of 
steps 
a vector giving the τ steps to calculate the conditional moments (in samples (=τ * sf)). 
sf 
a scalar denoting the sampling frequency (optional if 
bin_min 
a scalar denoting the minimal number of events per 
reqThreads 
a scalar denoting how many threads to use. Defaults to

Langevin1D
returns a list with thirteen components:
D1 
a vector of the Drift coefficient for each 
eD1 
a vector of the error of the Drift coefficient for each

D2 
a vector of the Diffusion coefficient for each 
eD2 
a vector of the error of the Driffusion coefficient for
each 
D4 
a vector of the fourth KramersMoyal coefficient for each

mean_bin 
a vector of the mean value per 
density 
a vector of the number of events per 
M1 
a matrix of the first conditional moment for each
τ. Rows corespond to 
eM1 
a matrix of the error of the first conditional moment
for each τ. Rows corespond to 
M2 
a matrix of the second conditional moment for each
τ. Rows corespond to 
eM2 
a matrix of the error of the second conditional moment
for each τ. Rows corespond to 
M4 
a matrix of the fourth conditional moment for each
τ. Rows corespond to 
U 
a vector of the 
Philip Rinn
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  # Set number of bins, steps and the sampling frequency
bins < 20;
steps < c(1:5);
sf < 1000;
#### Linear drift, constant diffusion
# Generate a time series with linear D^1 = x and constant D^2 = 1
x < timeseries1D(N=1e6, d11=1, d20=1, sf=sf);
# Do the analysis
est < Langevin1D(x, bins, steps, sf, reqThreads=2);
# Plot the result and add the theoretical expectation as red line
plot(est$mean_bin, est$D1);
lines(est$mean_bin, est$mean_bin, col='red');
plot(est$mean_bin, est$D2);
abline(h=1, col='red');
#### Cubic drift, constant diffusion
# Generate a time series with cubic D^1 = x  x^3 and constant D^2 = 1
x < timeseries1D(N=1e6, d13=1, d11=1, d20=1, sf=sf);
# Do the analysis
est < Langevin1D(x, bins, steps, sf, reqThreads=2);
# Plot the result and add the theoretical expectation as red line
plot(est$mean_bin, est$D1);
lines(est$mean_bin, est$mean_bin  est$mean_bin^3, col='red');
plot(est$mean_bin, est$D2);
abline(h=1, col='red');

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