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,
  columns,
  target,
  nvar,
  lib = NULL,
  pred = NULL,
  E = 3,
  tau = 1,
  k = E + 2,
  nb = NULL,
  top = NULL,
  threads = detectThreads(),
  trend.rm = TRUE
)

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

Arguments

data	The observation data.
columns	Names of individual variables.
target	Name of target variable.
nvar	Number of variable combinations.
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.
nb	(optional) The neighbours list.
top	(optional) Number of reconstructions used for MVE forecast.
threads	(optional) Number of threads.
trend.rm	(optional) Whether to remove the linear trend.

Value

A vector (when input is sf object) or matrix

Examples

columbus = sf::read_sf(system.file("shapes/columbus.gpkg", package="spData"))

multiview(columbus,
          columns = c("INC","CRIME","OPEN","PLUMB","DISCBD"),
          target = "HOVAL", nvar = 3)

spEDM documentation built on April 4, 2025, 2:41 a.m.