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,"inc")
#> The suggested theta for variable inc is 4 
# }