pcLightweight: Calculate Pattern Causality Using Lightweight Algorithm

In patterncausality: Pattern Causality Algorithm

Calculate Pattern Causality Using Lightweight Algorithm

Description

Implements a computationally efficient version of the Pattern Causality Model Mk. II for analyzing causal interactions between two time series. This function uses pattern and signature spaces to assess causality through reconstructed state spaces and hashed pattern analysis.

Usage

pcLightweight(
  X,
  Y,
  E,
  tau,
  h,
  weighted,
  metric = "euclidean",
  distance_fn = NULL,
  state_space_fn = NULL,
  relative = TRUE,
  verbose = FALSE
)

Arguments

X	A numeric vector representing the first time series
Y	A numeric vector representing the second time series
E	Integer; embedding dimension for state space reconstruction (E > 1)
tau	Integer; time delay for state space reconstruction (tau > 0)
h	Integer; prediction horizon for future projections (h >= 0)
weighted	Logical; whether to use weighted causality strength calculations
metric	Character string specifying the distance metric; one of "euclidean", "manhattan", or "maximum"
distance_fn	Custom distance function for state space reconstruction
state_space_fn	Custom function for state space transformation
relative	Logical; if TRUE calculates relative changes ((new-old)/old), if FALSE calculates absolute changes (new-old) in signature space. Default is TRUE.
verbose	Logical; whether to display progress information (default: FALSE)

Details

Calculate Pattern Causality Using Lightweight Algorithm

The function implements these key steps:

• State space reconstruction using embedding parameters

• Pattern and signature space transformation

• Nearest neighbor analysis in reconstructed spaces

• Causality strength calculation using prediction accuracy

• Classification of causality types (positive/negative/dark)

Value

An object of class "pc_fit" containing:

• total: Total causality strength (0-1)

• positive: Proportion of positive causality (0-1)

• negative: Proportion of negative causality (0-1)

• dark: Proportion of dark causality (0-1)

See Also

pcFullDetails for detailed analysis pcMatrix for analyzing multiple time series

Examples

data(climate_indices)
X <- climate_indices$AO
Y <- climate_indices$AAO
result <- pcLightweight(X, Y, E = 3, tau = 1, 
                       metric = "euclidean", h = 2, 
                       weighted = TRUE, verbose = FALSE)
print(result)
summary(result)
plot(result)

patterncausality documentation built on April 3, 2025, 6:57 p.m.