Optimal Parameter Search for Pattern Causality

Usage

# S4 method for class 'data.frame'
ops(
  data,
  target,
  source,
  E = 3:5,
  k = E,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  maximize = c("dark", "positive", "negative"),
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(E),
  higher.parallel = TRUE,
  h = 0
)

# S4 method for class 'sf'
ops(
  data,
  target,
  source,
  E = 3:5,
  k = E + 1,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  maximize = c("dark", "positive", "negative"),
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(E),
  higher.parallel = TRUE,
  nb = NULL
)

# S4 method for class 'SpatRaster'
ops(
  data,
  target,
  source,
  E = 3:5,
  k = E + 1,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  maximize = c("positive", "negative", "dark"),
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(E),
  higher.parallel = TRUE
)

Arguments

data: Observation data.
target: Integer of column indice for the target variable.
source: Integer of column indice for the source variable.
E: (optional) Embedding dimensions.
k: (optional) Number of nearest neighbors used for projection.
tau: (optional) Step of lag.
style: (optional) Embedding style (0 includes current state, 1 excludes it).
lib: (optional) Libraries indices.
pred: (optional) Predictions indices.
maximize: (optional) Causality metric to maximize: one of "positive", "negative", or "dark".
dist.metric: (optional) Distance measure to be used.
zero.tolerance: (optional) Maximum number of zeros tolerated in signature space.
relative: (optional) Whether to calculate relative changes in embedding.
weighted: (optional) Whether to weight causal strength.
threads: (optional) Number of threads used.
higher.parallel: (optional) Whether to use a higher level of parallelism.
h: (optional) Prediction horizon.
nb: (optional) Neighbours list.

Value

A list.

param: The selected optimal parameter combination.
xmap: A data.frame containing cross-mapping performance across parameter settings.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
pc::ops(columbus, 1, 3, E = 2:10, threads = 1)
#> The suggested E, k, tau is 5, 6 and 1