multiview: multiview embedding forecast

In spEDM: Spatial Empirical Dynamic Modeling

multiview embedding forecast

Description

multiview embedding forecast

Usage

## S4 method for signature 'sf'
multiview(
  data,
  column,
  target,
  nvar,
  lib = NULL,
  pred = NULL,
  E = 3,
  tau = 1,
  k = E + 2,
  nb = NULL,
  top = NULL,
  threads = detectThreads(),
  detrend = TRUE
)

## S4 method for signature 'SpatRaster'
multiview(
  data,
  column,
  target,
  nvar,
  lib = NULL,
  pred = NULL,
  E = 3,
  tau = 1,
  k = E + 2,
  top = NULL,
  threads = detectThreads(),
  detrend = TRUE
)

Arguments

data	observation data.
column	name of library variable.
target	name of target variable.
nvar	number of variable combinations.
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.
nb	(optional) neighbours list.
top	(optional) number of reconstructions used in MVE forecast.
threads	(optional) number of threads to use.
detrend	(optional) whether to remove the linear trend.

Value

A vector (when input is sf object) or matrix

References

Ye H., and G. Sugihara, 2016. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science 353:922-925.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))

multiview(columbus,
          column = c("inc","crime","open","plumb","discbd"),
          target = "hoval", nvar = 3)

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