spEDM (version 1.9)

pc: optimal parameter search for pattern causality

Description

optimal parameter search for pattern causality

Usage

# S4 method for 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 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
)

Value

A list

xmap

cross mapping performance

varname

name of target variable

method

method of cross mapping

maximize

maximized causality metric

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).

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"))
# \donttest{
pc(columbus,"hoval","crime",E = 5:10,maximize = "negative")
# }