Description Usage Arguments Details Value
Source: Ye, H. et al. (2015). Distinguishing time-delayed causal interactions using convergent cross mapping. Sci. Rep. 5, 14750; doi: 10.1038/srep14750 (2015)
1 2 3 4 | logistic_map_fourspecies_transitive(n = 3000, y1.i = 0.4, y2.i = 0.4,
y3.i = 0.4, y4.i = 0.4, Ry1y1 = 3.9, Ry1y2 = 0.4, Ry2y2 = 3.6,
Ry2y3 = 0.4, Ry3y3 = 3.6, Ry3y4 = 0.35, Ry4y4 = 3.8,
add.timestep = FALSE, plot = FALSE)
|
n |
The number of time steps that will be generated. |
y1.i |
Initial value of time series y1. |
y2.i |
Initial value of time series y2. |
y3.i |
Initial value of time series y3. |
y4.i |
Initial value of time series y4. |
Ry1y1 |
Parameter controlling the influence of y1 at timestep i on itself at timestep i+1 |
Ry1y2 |
Parameter controlling the influence of y1 at timestep i on y2 at timestep i+1 |
Ry2y2 |
Parameter controlling the influence of y2 at timestep i on itself at timestep i+1 |
Ry2y3 |
Parameter controlling the influence of y2 at timestep i on y3 at timestep i+1 |
Ry3y3 |
Parameter controlling the influence of y1 at timestep i on itself at timestep i+1 |
Ry3y4 |
Parameter controlling the influence of y3 at timestep i on y4 at timestep i+1 |
Ry4y4 |
Parameter controlling the influence of y1 at timestep i on itself at timestep i+1 |
This function allows you to express your love of cats.
A dataframe containing columns time series for y1, y2, y3 and y4 (and optionally "t", a timestep column).
Defaults values are as in the original paper.
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