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,
  replace = FALSE,
  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,
  replace = FALSE,
  seed = 42L,
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(libsizes),
  higher.parallel = TRUE,
  verbose = TRUE,
  detrend = FALSE,
  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,
  replace = FALSE,
  seed = 42L,
  dist.metric = c("euclidean", "manhattan", "maximum"),
  zero.tolerance = max(k),
  relative = TRUE,
  weighted = TRUE,
  threads = length(libsizes),
  higher.parallel = TRUE,
  verbose = TRUE,
  detrend = FALSE,
  ...
)

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.
replace: (optional) Should sampling be with replacement?
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.
...: Additional arguments to absorb unused inputs in method dispatch.
detrend: (optional) Whether to remove the linear trend.
nb: (optional) Neighbours list.

Value

A list.

causality: A data.frame of causality results. When libsizes is NULL, it contains per-sample causality estimates; otherwise, it contains causality results evaluated across different library sizes.
summary: A data.frame summarizing overall causality metrics. Only returned when libsizes is NULL.

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

crash = sf::read_sf(system.file("case/crash.gpkg", package = "pc"))
p1 = pc::pc(crash, 1, 2, E = 8, k = 8, threads = 1)
print(p1)
#>       type   strength
#> 1 positive 0.49969746
#> 2 negative 0.00000000
#> 3     dark 0.01034483
plot(p1)


# convergence diagnostics
p2 = pc::pc(crash, 1, 2, libsizes = seq(10,172,40), E = 8, k = 8, threads = 1)
#> 
Computing: [========================================] 100% (done)                         
print(p2)
#>    libsizes     type        mean         q05        q50        q95
#> 1        10 positive 0.015376669 0.000000000 0.00000000 0.00000000
#> 2        50 positive 0.063568472 0.000000000 0.00000000 0.49977149
#> 3        90 positive 0.136579574 0.000000000 0.00000000 0.99815552
#> 4       130 positive 0.186631291 0.000000000 0.00000000 0.99890072
#> 5       170 positive 0.476135903 0.299818474 0.49969746 0.49969746
#> 6        10 negative 0.011110787 0.000000000 0.00000000 0.00000000
#> 7        50 negative 0.065172928 0.000000000 0.00000000 0.49995337
#> 8        90 negative 0.005044505 0.000000000 0.00000000 0.00000000
#> 9       130 negative 0.000000000 0.000000000 0.00000000 0.00000000
#> 10      170 negative 0.000000000 0.000000000 0.00000000 0.00000000
#> 11       10     dark 0.012385738 0.000000000 0.01067757 0.02948670
#> 12       50     dark 0.017525127 0.002500290 0.01653382 0.03506015
#> 13       90     dark 0.022218695 0.006429697 0.02189141 0.04178204
#> 14      130     dark 0.021216780 0.006645161 0.01973684 0.04173889
#> 15      170     dark 0.011373253 0.006891828 0.01034483 0.01765704
plot(p2)