spatial logistic map

Usage

# S4 method for class 'sf'
slm(
  data,
  x = NULL,
  y = NULL,
  z = NULL,
  k = 4,
  step = 15,
  alpha_x = 0.28,
  alpha_y = 0.25,
  alpha_z = 0.22,
  beta_xy = 0.05,
  beta_xz = 0.05,
  beta_yx = 0.2,
  beta_yz = 0.2,
  beta_zx = 0.35,
  beta_zy = 0.35,
  threshold = Inf,
  transient = 1,
  nb = NULL
)

# S4 method for class 'SpatRaster'
slm(
  data,
  x = NULL,
  y = NULL,
  z = NULL,
  k = 4,
  step = 15,
  alpha_x = 0.28,
  alpha_y = 0.25,
  alpha_z = 0.22,
  beta_xy = 0.05,
  beta_xz = 0.05,
  beta_yx = 0.2,
  beta_yz = 0.2,
  beta_zx = 0.35,
  beta_zy = 0.35,
  threshold = Inf,
  transient = 1
)

Arguments

data: observation data.
x: (optional) 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.
transient: (optional) transients to be excluded from the results.
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] 0.9037104 0.9032733 0.9048553 0.9074780 0.9063829 0.9045626 0.9066237
#>  [8] 0.9062798 0.9047000 0.9050370 0.9067613 0.9064478 0.9064988 0.9064605
#> [15] 0.9065417 0.9067752 0.9062915 0.9059093 0.9060800 0.8972853 0.9063665
#> [22] 0.9062767 0.9017255 0.9054698 0.9067412 0.9067313 0.9061364 0.9067689
#> [29] 0.9066720 0.9054963 0.9040259 0.9013191 0.9063502 0.9044973 0.9057003
#> [36] 0.9035188 0.9048052 0.9062483 0.9033380 0.8973235 0.9015377 0.9005036
#> [43] 0.9056818 0.9046099 0.9050752 0.9033879 0.9032858 0.9056155 0.9037352
#>