smap: optimal parameter search for smap forecasting

In spEDM: Spatial Empirical Dynamic Modeling

optimal parameter search for smap forecasting

Description

optimal parameter search for smap forecasting

Usage

## S4 method for signature 'sf'
smap(
  data,
  column,
  target,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  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,
  nb = NULL
)

## S4 method for signature 'SpatRaster'
smap(
  data,
  column,
  target,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  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,
  grid.coord = TRUE,
  embed.direction = 0
)

Arguments

data	observation data.
column	name of library variable.
target	name of target variable.
E	(optional) embedding dimensions.
k	(optional) number of nearest neighbors used.
tau	(optional) step of spatial lags.
style	(optional) embedding style (0 includes current state, 1 excludes it).
stack	(optional) whether to stack embeddings.
lib	(optional) libraries indices (input needed: vector - spatial vector, matrix - spatial raster).
pred	(optional) predictions indices (input requirement same as lib).
dist.metric	(optional) distance metric (L1: Manhattan, L2: Euclidean).
dist.average	(optional) whether to average distance.
theta	(optional) weighting parameter for distances.
threads	(optional) number of threads to use.
detrend	(optional) whether to remove the linear trend.
nb	(optional) neighbours list.
grid.coord	(optional) whether to detrend using cell center coordinates (TRUE) or row/column numbers (FALSE).
embed.direction	(optional) direction selector for embeddings (0 returns all directions, 1-8 correspond to NW, N, NE, W, E, SW, S, SE).

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 Nov. 30, 2025, 5:07 p.m.