simplex forecast — simplex • spEDM

simplex forecast

Usage

# S4 method for class 'sf'
simplex(
  data,
  target,
  lib = NULL,
  pred = NULL,
  E = 1:10,
  tau = 1,
  k = E + 2,
  nb = NULL,
  threads = detectThreads(),
  trend.rm = TRUE
)

# S4 method for class 'SpatRaster'
simplex(
  data,
  target,
  lib = NULL,
  pred = NULL,
  E = 1:10,
  tau = 1,
  k = E + 2,
  threads = detectThreads(),
  trend.rm = TRUE
)

Arguments

data: The observation data.
target: Name of target variable.
lib: (optional) Libraries indices.
pred: (optional) Predictions indices.
E: (optional) Dimensions of the embedding.
tau: (optional) Step of spatial lags.
k: (optional) Number of nearest neighbors used for prediction.
nb: (optional) The neighbours list.
threads: (optional) Number of threads.
trend.rm: (optional) Whether to remove the linear trend.

Value

A list

xmap: self mapping prediction results
varname: name of target variable

References

Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734-741.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
# \donttest{
simplex(columbus,target = "crime")
#> The suggested E and k for variable crime is 5 and 8 
# }