Pattern Causality

Usage

# S4 method for class 'data.frame'
pc(
  data,
  target,
  source,
  libsizes = NULL,
  E = 3,
  k = E,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  boot = 99,
  random = TRUE,
  seed = 42L,
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(libsizes),
  higher.parallel = TRUE,
  verbose = TRUE,
  h = 0
)

# S4 method for class 'sf'
pc(
  data,
  target,
  source,
  libsizes = NULL,
  E = 3,
  k = E + 1,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  boot = 99,
  random = TRUE,
  seed = 42L,
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(libsizes),
  higher.parallel = TRUE,
  verbose = TRUE,
  nb = NULL
)

# S4 method for class 'SpatRaster'
pc(
  data,
  target,
  source,
  libsizes = NULL,
  E = 3,
  k = E + 1,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  boot = 99,
  random = TRUE,
  seed = 42L,
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(libsizes),
  higher.parallel = TRUE,
  verbose = TRUE
)

Arguments

data: Observation data.
target: Integer of column indice for the target variable.
source: Integer of column indice for the source variable.
libsizes: (optional) Number of observations used.
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.
boot: (optional) Number of bootstraps to perform.
random: (optional) Whether to use random sampling.
seed: (optional) Random seed.
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.
verbose: (optional) Whether to show the progress bar.
h: (optional) Prediction horizon.
nb: (optional) Neighbours list.

Value

A list (when libsizes is NULL) or data.frame. If libsizes is NULL, a list with two components is returned:

causality: A data.frame containing per-sample causality results.
summary: A data.frame summarizing overall causality metrics.

Note

pc only supports numeric input data.

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::pc(columbus, 1, 3, E = 5, k = 9, threads = 1)
#>       type  strength
#> 1 positive 0.0000000
#> 2 negative 0.7753134
#> 3     dark 0.6213416