pcEffect {patterncausality} | R Documentation |
Pattern Causality Effect Analysis
Description
Analyzes pattern causality matrices to compute and summarize the directional effects of different causality types (positive, negative, dark) between system components.
Usage
pcEffect(pcmatrix, verbose = FALSE)
Arguments
pcmatrix |
An object of class "pc_matrix" containing causality matrices |
verbose |
Logical; whether to display computation progress (default: FALSE) |
Details
Calculate Pattern Causality Effect Analysis
The function performs these key steps:
Processes raw causality matrices
Computes received and exerted influence for each component
Calculates net causality effect (difference between received and exerted)
Normalizes results to percentage scale
Value
An object of class "pc_effect" containing:
positive: Data frame of positive causality effects
negative: Data frame of negative causality effects
dark: Data frame of dark causality effects
items: Vector of component names
summary: Summary statistics for each causality type
Related Packages
-
vars: Vector autoregression for multivariate time series
-
lmtest: Testing linear regression models
-
causality: Causality testing and modeling
See Also
pcMatrix
for generating causality matrices
plot.pc_effect
for visualizing causality effects
Examples
data(climate_indices)
dataset <- climate_indices[, -1]
pcmatrix <- pcMatrix(dataset, E = 3, tau = 1,
metric = "euclidean", h = 1,
weighted = TRUE)
effects <- pcEffect(pcmatrix)
print(effects)
plot(effects)