patterncausality (version 0.2.1)

pcEffect: 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)

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

Arguments

pcmatrix

An object of class "pc_matrix" containing causality matrices

verbose

Logical; whether to display computation progress (default: FALSE)

Related Packages

• vars: Vector autoregression for multivariate time series

• lmtest: Testing linear regression models

• causality: Causality testing and modeling

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

See Also

pcMatrix for generating causality matrices plot.pc_effect for visualizing causality effects

Examples

# \donttest{
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)
# }