patterncausality (version 0.1.3)

pcEffect: Calculate and Summarize Pattern Causality Effects

Description

The pcEffect function processes a pattern causality matrix to compute and summarize the effects of positive, negative, and dark causality. It aggregates these causality measures to determine the total received and exerted influence for each item in the matrix, along with the difference between them.

Usage

pcEffect(pcmatrix)

Value

A list containing three data frames (positive, negative, and dark), each summarizing the causality effects. Each data frame includes columns for the received, exerted, and the difference (Diff) of causality for each item.

Arguments

pcmatrix

A list containing three matrices (positive, negative, and dark) which represent the respective causality types for different items, as well as an items vector indicating the names of the items.

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)
# }