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.
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
#>