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smap forecast

Usage

# S4 method for class 'sf'
smap(
  data,
  column,
  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(),
  detrend = TRUE
)

# S4 method for class 'SpatRaster'
smap(
  data,
  column,
  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(),
  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 in prediction.

theta

(optional) weighting parameter for distances.

nb

(optional) neighbours list.

threads

(optional) number of threads to use.

detrend

(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","crime")
#> The suggested theta for variable crime is 1 
# }