spatial logistic map

Usage

# S4 method for class 'sf'
slm(
  data,
  x,
  y = NULL,
  z = NULL,
  k = 4,
  step = 20,
  alpha_x = 0.625,
  alpha_y = 0.77,
  alpha_z = 0.55,
  beta_xy = 0.05,
  beta_xz = 0.05,
  beta_yx = 0.4,
  beta_yz = 0.4,
  beta_zx = 0.65,
  beta_zy = 0.65,
  threshold = 1e+10,
  nb = NULL
)

# S4 method for class 'SpatRaster'
slm(
  data,
  x,
  y = NULL,
  z = NULL,
  k = 4,
  step = 20,
  alpha_x = 0.625,
  alpha_y = 0.77,
  alpha_z = 0.55,
  beta_xy = 0.05,
  beta_xz = 0.05,
  beta_yx = 0.4,
  beta_yz = 0.4,
  beta_zx = 0.65,
  beta_zy = 0.65,
  threshold = 1e+10
)

Arguments

data: observation data.
x: name of first spatial variable.
y: (optional) name of second spatial variable.
z: (optional) name of third spatial variable.
k: (optional) number of neighbors to used.
step: (optional) number of simulation time steps.
alpha_x: (optional) growth parameter for x.
alpha_y: (optional) growth parameter for y.
alpha_z: (optional) growth parameter for y.
beta_xy: (optional) cross-inhibition from x to y.
beta_xz: (optional) cross-inhibition from x to z.
beta_yx: (optional) cross-inhibition from y to x.
beta_yz: (optional) cross-inhibition from y to z.
beta_zx: (optional) cross-inhibition from z to x.
beta_zy: (optional) cross-inhibition from z to y.
threshold: (optional) set to NaN if the absolute value exceeds this threshold.
nb: (optional) neighbours list.

Value

A list

References

Willeboordse, F.H., The spatial logistic map as a simple prototype for spatiotemporal chaos, Chaos, 533–540 (2003).

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
columbus$inc = sdsfun::normalize_vector(columbus$inc)
slm(columbus,"inc")
#> $x
#>  [1] 0.9359961 0.9381776 0.9318960 0.9167621 0.9264342 0.9316217 0.9218783
#>  [8] 0.9263860 0.9342999 0.9285665 0.9204183 0.9243912 0.9236050 0.9242617
#> [15] 0.9242590 0.9206688 0.9233470 0.9290393 0.9265536 0.9433793 0.9253383
#> [22] 0.9264466 0.9344554 0.9301023 0.9220555 0.9214062 0.9254505 0.9206858
#> [29] 0.9223061 0.9296161 0.9315532 0.9304259 0.9243044 0.9295402 0.9274787
#> [36] 0.9337240 0.9335903 0.9258377 0.9330425 0.9409895 0.9356034 0.9407316
#> [43] 0.9286240 0.9330746 0.9290264 0.9328690 0.9335784 0.9259826 0.9347650
#>