Evaluates the causality prediction accuracy across multiple time series within a dataset using the PC Mk. II Light method. This function analyzes pairwise causality relationships and computes different types of causality measures.
Usage
pcAccuracy(
dataset,
E,
tau,
metric = "euclidean",
h,
weighted,
distance_fn = NULL,
state_space_fn = NULL,
verbose = FALSE
)Arguments
- dataset
A matrix or data frame where each column represents a time series
- E
Integer; embedding dimension for state space reconstruction (E > 1)
- tau
Integer; time delay for state space reconstruction (tau > 0)
- metric
Character; distance metric to use, one of "euclidean", "manhattan", or "maximum"
- h
Integer; prediction horizon, indicating forecast distance (h >= 0)
- weighted
Logical; whether to use weighted approach in calculating causality strengths
- distance_fn
Optional custom distance function for computing distances (default: NULL)
- state_space_fn
Optional custom function for state space reconstruction (default: NULL)
- verbose
Logical; whether to display progress information (default: FALSE)
Value
An object of class "pc_accuracy" containing:
parameters: List of input parameters (E, tau, metric, h, weighted)
total: Mean total causality across all pairs
positive: Mean positive causality across all pairs
negative: Mean negative causality across all pairs
dark: Mean dark causality across all pairs
matrices: Raw causality matrices for each type
See also
pcMatrix for analyzing individual causality matrices
pcLightweight for pairwise causality analysis
Examples
# \donttest{
data(climate_indices)
data <- climate_indices[, -1]
results <- pcAccuracy(dataset = data, E = 3, tau = 1,
metric = "euclidean", h = 1,
weighted = TRUE, verbose = TRUE)
#> Analyzing causality relationships...
#>
Analyzing relationships: 2/12 (17%)
Analyzing relationships: 3/12 (25%)
Analyzing relationships: 4/12 (33%)
Analyzing relationships: 4/12 (33%)
Analyzing relationships: 6/12 (50%)
Analyzing relationships: 7/12 (58%)
Analyzing relationships: 7/12 (58%)
Analyzing relationships: 8/12 (67%)
Analyzing relationships: 10/12 (83%)
Analyzing relationships: 10/12 (83%)
Analyzing relationships: 11/12 (92%)
Analyzing relationships: 12/12 (100%)
#>
#> Computing summary statistics...
print(results)
#> Pattern Causality Accuracy Analysis
#> ----------------------------------
#> Parameters:
#> E: 3
#> tau: 1
#> metric: euclidean
#> h: 1
#>
#> Causality measures:
#> Total: 0.2825
#> Positive: 0.2773
#> Negative: 0.2569
#> Dark: 0.4657
#>
# }