Analyzes pattern causality matrices to compute and summarize the directional effects of different causality types (positive, negative, dark) between system components.
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
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
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
# \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)
#> Pattern Causality Effect Analysis
#> --------------------------------
#>
#> Positive Causality Effects:
#> received exerted Diff
#> AO 92.59 71.61 20.98
#> AAO 83.15 109.65 -26.49
#> NAO 71.51 64.57 6.93
#> PNA 85.56 86.98 -1.42
#>
#> Negative Causality Effects:
#> received exerted Diff
#> AO 71.12 78.40 -7.28
#> AAO 82.71 50.33 32.38
#> NAO 78.74 97.21 -18.46
#> PNA 75.75 82.39 -6.63
#>
#> Dark Causality Effects:
#> received exerted Diff
#> AO 136.29 149.99 -13.71
#> AAO 134.14 140.02 -5.88
#> NAO 149.75 138.22 11.53
#> PNA 138.69 130.63 8.06
#>
plot(effects)
# }