Chaotic Time Series Modelling
A collection and description of functions to simulate different types of chaotic time series maps.
Chaotic Time Series Maps:
tentSim | Simulates data from the Tent Map, |
henonSim | simulates data from the Henon Map, |
ikedaSim | simulates data from the Ikeda Map, |
logisticSim | simulates data from the Logistic Map, |
lorentzSim | simulates data from the Lorentz Map, |
roesslerSim | simulates data from the Roessler Map. |
tentSim(n = 1000, n.skip = 100, parms = c(a = 2), start = runif(1), doplot = FALSE) henonSim(n = 1000, n.skip = 100, parms = c(a = 1.4, b = 0.3), start = runif(2), doplot = FALSE) ikedaSim(n = 1000, n.skip = 100, parms = c(a = 0.4, b = 6.0, c = 0.9), start = runif(2), doplot = FALSE) logisticSim(n = 1000, n.skip = 100, parms = c(r = 4), start = runif(1), doplot = FALSE) lorentzSim(times = seq(0, 40, by = 0.01), parms = c(sigma = 16, r = 45.92, b = 4), start = c(-14, -13, 47), doplot = TRUE, ...) roesslerSim(times = seq(0, 100, by = 0.01), parms = c(a = 0.2, b = 0.2, c = 8.0), start = c(-1.894, -9.920, 0.0250), doplot = TRUE, ...)
doplot: a logical flag. Should a plot be displayed?
n, n.skip: [henonSim][ikedaSim][logisticSim] -
the number of chaotic time series points to be generated and the number of initial values to be skipped from the series.
parms: the named parameter vector characterizing the chaotic map.
start: the vector of start values to initiate the chaotic map.
times: [lorentzSim][roesslerSim] -
the sequence of time series points at which to generate the map.
...: arguments to be passed.
[*Sim] -
All functions return invisible a vector of time series data.
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## logisticSim - set.seed(4711) x = logisticSim(n = 100) plot(x, main = "Logistic Map")