pc: optimal parameter search for pattern causality

In spEDM: Spatial Empirical Dynamic Modeling

optimal parameter search for pattern causality

Description

optimal parameter search for pattern causality

Usage

## S4 method for signature 'sf'
pc(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  maximize = "positive",
  threads = detectThreads(),
  detrend = FALSE,
  nb = NULL
)

## S4 method for signature 'SpatRaster'
pc(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  maximize = "positive",
  threads = detectThreads(),
  detrend = FALSE,
  grid.coord = TRUE
)

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).
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).
zero.tolerance	(optional) maximum number of zeros tolerated in signature space.
relative	(optional) whether to calculate relative changes in embeddings.
weighted	(optional) whether to weight causal strength.
maximize	(optional) causality metric to maximize: one of "positive", "negative", or "dark".
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).

Value

A list

xmap

cross mapping performance

varname

name of target variable

method

method of cross mapping

maximize

maximized causality metric

References

Stavroglou, S.K., Pantelous, A.A., Stanley, H.E., Zuev, K.M., 2020. Unveiling causal interactions in complex systems. Proceedings of the National Academy of Sciences 117, 7599–7605.

Examples

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

pc(columbus,"hoval","crime",E = 5:10,maximize = "negative")

spEDM documentation built on Nov. 30, 2025, 5:07 p.m.