optimalParametersSearch {patterncausality} | R Documentation |
Search for Optimal Parameters in Pattern Causality Analysis
Description
Searches for the optimal embedding dimension (E) and time delay (tau) to maximize the accuracy of causality predictions in a dataset. This function implements a grid search approach to evaluate different parameter combinations.
Usage
optimalParametersSearch(
Emax,
tauMax,
metric = "euclidean",
distance_fn = NULL,
state_space_fn = NULL,
dataset,
h = 0,
weighted = FALSE,
relative = TRUE,
verbose = FALSE
)
Arguments
Emax |
Positive integer > 2; maximum embedding dimension to test |
tauMax |
Positive integer; maximum time delay to test |
metric |
Character string; distance metric for causality analysis ('euclidean', 'manhattan', 'maximum'). Defaults to "euclidean". Ignored if |
distance_fn |
Optional custom distance function; takes two numeric vectors as input and returns a numeric distance. (default: NULL) |
state_space_fn |
Optional custom function for state space reconstruction; takes a numeric vector and parameters E and tau as input and returns a reconstructed state space. (default: NULL) |
dataset |
Numeric matrix; each column represents a time series. |
h |
Positive integer; prediction horizon. |
weighted |
Logical; if TRUE, weighted causality analysis is performed. |
relative |
Logical; if TRUE calculates relative changes ((new-old)/old), if FALSE calculates absolute changes (new-old) in signature space. Default is TRUE. |
verbose |
Logical; if TRUE, prints progress information. (default: FALSE) |
Details
Search for Optimal Parameters in Pattern Causality Analysis
This function evaluates each combination of embedding dimension and time delay for their effectiveness in detecting different types of causality:
Total causality: Overall causal relationship strength
Positive causality: Direct positive influences
Negative causality: Direct negative influences
Dark causality: Complex or indirect causal relationships
Value
A pc_params
object containing:
accuracy_summary: A data frame summarizing the accuracy for each parameter combination.
computation_time: The time taken for the analysis.
parameters: A list of the input parameters used.
Examples
data(climate_indices)
dataset <- climate_indices[, -1]
optimalParams <- optimalParametersSearch(
Emax = 3,
tauMax = 3,
metric = "euclidean",
dataset = dataset,
h = 1,
weighted = FALSE
)
print(optimalParams)