optimalParametersSearch {patterncausality} | R Documentation |
Optimal Parameters Search for Causality Analysis
Description
Searches for the optimal embedding dimension (E) and time delay (tau) to maximize the accuracy of causality predictions in a dataset. It evaluates each combination of E and tau for their ability to predict different types of causality: total, positive, negative, and dark.
Usage
optimalParametersSearch(Emax, tauMax, metric, dataset)
Arguments
Emax |
The maximum embedding dimension to test. |
tauMax |
The maximum time delay to test. |
metric |
The distance metric to use in the causality analysis (e.g., 'euclidean'). |
dataset |
A matrix where each column represents a time series to be analyzed. |
Value
A data frame summarizing the causality analysis results across all tested E and tau values, showing the mean total, positive, negative, and dark causality accuracies for each parameter combination.
Examples
data(climate)
dataset <- climate[, -1]
optimalParams <- optimalParametersSearch(Emax=3, tauMax=3, metric="euclidean", dataset=dataset)
print(optimalParams)
[Package patterncausality version 0.1.2 Index]