smap: smap forecast

In spEDM: Spatial Empirical Dynamic Modeling

smap forecast

Description

smap forecast

Usage

## S4 method for signature '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 signature '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.
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

forecast performance

varname

name of target variable

method

method of cross mapping

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"))

smap(columbus,"inc","crime",E = 5,k = 6)

spEDM documentation built on June 25, 2025, 9:07 a.m.