This function performs dynamic network analysis to detect causal interactions between multiple time series within a dataset. It uses the Pattern Causality Model Mk. II to evaluate positive, negative, and dark causality relationships by analyzing the reconstructed state spaces of the time series. The function iterates through each time series in the dataset, comparing them to identify potential causal links.
Arguments
- dataset
A data frame where each column represents a time series to be analyzed for causal relationships.
- E
An integer specifying the embedding dimension for reconstructing the state space of the time series.
- tau
An integer representing the time delay used in reconstructing the time series in the embedded space.
- metric
A character string specifying the distance metric to be used in the analysis (e.g., 'euclidean').
- h
An integer indicating the prediction horizon, i.e., the number of steps ahead for which predictions are made.
- weighted
A logical value indicating whether the analysis should apply a weighted approach when calculating causality strength.
Value
A list containing three matrices (positive
, negative
, dark
) that represent the detected causal relationships between the time series in the dataset. Each matrix provides the strength of positive, negative, and dark causality, respectively.
Examples
# \donttest{
data(climate_indices)
dataset <- climate_indices[,-1] # remove the date column
result <- pcMatrix(dataset, E = 3, tau = 1, metric = "euclidean", h = 2, weighted = TRUE)
#> CAUSE: AO
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> CAUSE: AAO
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> CAUSE: NAO
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> CAUSE: PNA
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> Calculation duration: 7.96907925605774
print(result)
#> $positive
#> [,1] [,2] [,3] [,4]
#> [1,] NA 0.2371795 0.1910828 0.2413793
#> [2,] 0.3241379 NA 0.2711864 0.2101449
#> [3,] 0.1988950 0.2402597 NA 0.1768707
#> [4,] 0.3378378 0.2411348 0.2968750 NA
#>
#> $negative
#> [,1] [,2] [,3] [,4]
#> [1,] NA 0.3846154 0.3694268 0.3448276
#> [2,] 0.1724138 NA 0.2796610 0.3913043
#> [3,] 0.3093923 0.2532468 NA 0.3741497
#> [4,] 0.2702703 0.3687943 0.2421875 NA
#>
#> $dark
#> [,1] [,2] [,3] [,4]
#> [1,] NA 0.3782051 0.4394904 0.4137931
#> [2,] 0.5034483 NA 0.4491525 0.3985507
#> [3,] 0.4917127 0.5064935 NA 0.4489796
#> [4,] 0.3918919 0.3900709 0.4609375 NA
#>
#> $items
#> [1] "AO" "AAO" "NAO" "PNA"
#>
# }