Analyzes pattern causality relationships between multiple time series in X and multiple time series in Y by computing pairwise causality measures and organizing them into a matrix.
pcCrossMatrix(
X,
Y,
E,
tau,
metric = "euclidean",
h,
weighted = TRUE,
distance_fn = NULL,
state_space_fn = NULL,
verbose = FALSE,
n_cores = 1
)A pc_matrix object containing causality matrices
Matrix or data frame of time series for the cause
Matrix or data frame of time series for the effect
Integer; embedding dimension
Integer; time delay
Character; distance metric ("euclidean", "manhattan", "maximum")
Integer; prediction horizon
Logical; whether to use weighted causality
Optional custom distance function
Optional custom state space reconstruction function
Logical; whether to print progress
Integer; number of cores for parallel computation
vars: Vector autoregression analysis
tseries: Time series analysis tools
forecast: Time series forecasting methods
Compute Cross Pattern Causality Matrix Analysis
The function performs these key steps:
Validates input data and parameters
Computes pairwise causality measures between X and Y
Organizes results into a causality matrix
Provides summary statistics for each causality type