pcEffect: Pattern Causality Effect Analysis

In patterncausality: Pattern Causality Algorithm

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)

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