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simplex forecast

Usage

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

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

Arguments

data

observation data.

column

name of library variable.

target

name of target variable.

lib

(optional) libraries indices.

pred

(optional) predictions indices.

E

(optional) embedding dimensions.

tau

(optional) step of spatial lags.

k

(optional) number of nearest neighbors used.

nb

(optional) neighbours list.

threads

(optional) number of threads to use.

detrend

(optional) whether to remove the linear trend.

Value

A list

xmap

forecast performance

varname

name of target variable

method

method of cross mapping

tau

step of time lag

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,"inc","crime")
#> The suggested E and k for variable crime is 5 and 6 
# }