spEDM (version 1.9)

gccm: geographical convergent cross mapping

Description

geographical convergent cross mapping

Usage

# S4 method for sf
gccm(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  theta = 1,
  algorithm = "simplex",
  threads = detectThreads(),
  detrend = TRUE,
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE,
  nb = NULL
)
# S4 method for SpatRaster
gccm(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  theta = 1,
  algorithm = "simplex",
  threads = detectThreads(),
  detrend = TRUE,
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE,
  grid.coord = TRUE,
  embed.direction = 0,
  win.ratio = 0
)

Value

A list

xmap

cross mapping results

varname

names of causal and effect variables

bidirectional

whether to examine bidirectional causality

Arguments

data

observation data.

cause

name of causal variable.

effect

name of effect variable.

libsizes

(optional) number of spatial units used (input needed: vector - spatial vector, matrix - spatial raster).

E

(optional) embedding dimensions.

k

(optional) number of nearest neighbors.

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 requirement same as libsizes).

pred

(optional) predictions indices (input requirement same as libsizes).

dist.metric

(optional) distance metric (L1: Manhattan, L2: Euclidean).

dist.average

(optional) whether to average distance.

theta

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

algorithm

(optional) prediction algorithm.

threads

(optional) number of threads to use.

detrend

(optional) whether to remove the linear trend.

parallel.level

(optional) level of parallelism, low or high.

bidirectional

(optional) whether to examine bidirectional causality.

progressbar

(optional) whether to show the progress bar.

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).

win.ratio

(optional) ratio of sliding window scale to speed up state-space predictions.

References

Gao, B., Yang, J., Chen, Z. et al. Causal inference from cross-sectional earth system data with geographical convergent cross mapping. Nat Commun 14, 5875 (2023).

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg",package="spEDM"))
# \donttest{
g = gccm(columbus,"hoval","crime",libsizes = seq(5,45,5),E = 6)
g
plot(g,ylimits = c(0,0.85))
# }