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smap forecast

Usage

# S4 method for class 'sf'
smap(
  data,
  target,
  lib = NULL,
  pred = NULL,
  E = 3,
  tau = 1,
  k = E + 2,
  theta = c(0, 1e-04, 3e-04, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 3,
    4, 6, 8),
  nb = NULL,
  threads = detectThreads(),
  trend.rm = TRUE
)

# S4 method for class 'SpatRaster'
smap(
  data,
  target,
  lib = NULL,
  pred = NULL,
  E = 3,
  tau = 1,
  k = E + 2,
  theta = c(0, 1e-04, 3e-04, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 3,
    4, 6, 8),
  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.

theta

(optional) Weighting parameter for distances.

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. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688):477-495.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
# \donttest{
smap(columbus,target = "inc")
#> The suggested theta for variable inc is 4 
# }