Package 'spEDM'

Title: Spatial Empirical Dynamic Modeling
Description: Inferring causal associations in cross-sectional earth system data through empirical dynamic modeling (EDM), with extensions to convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023)<doi:10.1016/j.fmre.2023.01.007>.
Authors: Wenbo Lv [aut, cre, cph]
Maintainer: Wenbo Lv <lyu.geosocial@gmail.com>
License: GPL-3
Version: 1.5
Built: 2025-03-12 07:27:09 UTC
Source: CRAN

Help Index

detect the number of available threads

Description

detect the number of available threads

Usage

detectThreads()

Value

An integer

Examples

detectThreads()

embedding spatial cross sectional data

Description

embedding spatial cross sectional data

Usage

## S4 method for signature 'sf'
embedded(data, target, E = 3, tau = 1, nb = NULL, trend.rm = FALSE)

## S4 method for signature 'SpatRaster'
embedded(data, target, E = 3, tau = 1, trend.rm = FALSE)

Arguments

data

The observation data.
target

Name of target variable.
E

(optional) Dimensions of the embedding.
tau

(optional) Step of spatial lags.
nb

(optional) The neighbours list.
trend.rm

(optional) Whether to remove the linear trend.

Value

A matrix

Examples

columbus = sf::read_sf(system.file("shapes/columbus.gpkg", package="spData"))
embedded(columbus,target = "CRIME", E = 3)

geographical convergent cross mapping

Description

geographical convergent cross mapping

Usage

## S4 method for signature 'sf'
gccm(
  data,
  cause,
  effect,
  libsizes,
  E = 3,
  tau = 1,
  k = E + 2,
  theta = 1,
  algorithm = "simplex",
  lib = NULL,
  pred = NULL,
  nb = NULL,
  threads = detectThreads(),
  parallel.level = "low",
  bidirectional = TRUE,
  trend.rm = TRUE,
  progressbar = TRUE
)

## S4 method for signature 'SpatRaster'
gccm(
  data,
  cause,
  effect,
  libsizes,
  E = 3,
  tau = 1,
  k = E + 2,
  theta = 1,
  algorithm = "simplex",
  lib = NULL,
  pred = NULL,
  threads = detectThreads(),
  parallel.level = "low",
  bidirectional = TRUE,
  trend.rm = TRUE,
  progressbar = TRUE
)

Arguments

data

The observation data.
cause

Name of causal variable.
effect

Name of effect variable.
libsizes

A vector of library sizes to use.
E

(optional) Dimensions of the embedding.
tau

(optional) Step of spatial lags.
k

(optional) Number of nearest neighbors to use for prediction.
theta

(optional) Weighting parameter for distances, useful when algorithm is smap.
algorithm

(optional) Algorithm used for prediction.
lib

(optional) Libraries indices.
pred

(optional) Predictions indices.
nb

(optional) The neighbours list.
threads

(optional) Number of threads.
parallel.level

(optional) Level of parallelism, low or high.
bidirectional

(optional) whether to identify bidirectional causal associations.
trend.rm

(optional) Whether to remove the linear trend.
progressbar

(optional) whether to print the progress bar.

Value

A list

xmap

cross mapping prediction results

varname

names of causal and effect variable

bidirectional

whether to identify bidirectional causal associations

Examples

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

g = gccm(columbus,"HOVAL","CRIME",libsizes = seq(5,45,5),E = 6)
g
plot(g, ylimits = c(0,0.85))

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)

simplex forecast

Description

simplex forecast

Usage

## S4 method for signature 'sf'
simplex(
  data,
  target,
  lib = NULL,
  pred = NULL,
  E = 1:10,
  tau = 1,
  k = E + 2,
  nb = NULL,
  threads = detectThreads(),
  trend.rm = TRUE
)

## S4 method for signature 'SpatRaster'
simplex(
  data,
  target,
  lib = NULL,
  pred = NULL,
  E = 1:10,
  tau = 1,
  k = E + 2,
  threads = detectThreads(),
  trend.rm = TRUE
)

Arguments

data

The observation data.
target

Name of target variable.
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.
threads

(optional) Number of threads.
trend.rm

(optional) Whether to remove the linear trend.

Value

A list

xmap

self mapping prediction results

varname

name of target variable

Examples

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

simplex(columbus,target = "CRIME")

smap forecast

Description

smap forecast

Usage

## S4 method for signature 'sf'
smap(
  data,
  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(),
  trend.rm = TRUE
)

## S4 method for signature 'SpatRaster'
smap(
  data,
  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(),
  trend.rm = TRUE
)

Arguments

data

The observation data.
target

Name of target variable.
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.
theta

(optional) Weighting parameter for distances.
nb

(optional) The neighbours list.
threads

(optional) Number of threads.
trend.rm

(optional) Whether to remove the linear trend.

Value

A list

xmap

self mapping prediction results

varname

name of target variable

Examples

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

smap(columbus,target = "INC")