nodeStatsVAR1(sparseA, sparseP, as.table = FALSE)matrix $\mathbf{A}$ of auto-regression parameters, which is assumed to be sparse.matrix $\mathbf{\Omega}_{\varepsilon}$ the error, which is assumed to be sparse.logical indicating if the output should be in tabular format.list (when as.table = FALSE) with slots:numeric vector with the number of (temporal) edges pointing to each node ('in'-degree).numeric vector with the number of (temporal) edges leaving each node ('out'-degree).numeric vector with the number of negative (temporal) edges pointing to each node.numeric vector with the number of positive (temporal) edges pointing to each node ('in'-degree)numeric vector with the number of negative (temporal) edges leaving each node ('out'-degree)numeric vector with the number of positive (temporal) edges leaving each node ('out'-degree)numeric vector with the number of contemporaneous edges of each node (as implied by the error precision matrix)numeric vector representing the contemporaneous betweenness centrality for each node.numeric vector representing the contemporaneous closeness centrality for each node.numeric vector representing the contemporaneous eigen centrality for each node.numeric vector representing the number of negative contemporaneous edges for each node.numeric vector representing the number of positive contemporaneous edges for each node.numeric vector representing the error variance of each node.numeric vector representing the partial error variance of each node.numeric vector representing the variance of each node.numeric number of edges of each node in the global Markov graph.numeric vector representing the betweenness centrality for each node in the global Markov graph.numeric vector representing the closeness centrality for each node in the global Markov graph.numeric vector representing the eigen centrality for each node in the global Markov graph.numeric vector with for each node its mutual information with all other nodes at the next (t+1) time point.numeric vector with for each node its mutual information with all other nodes at the (t+2)-th time point.numeric vector with for each node its mean absolute impulse response on all other nodes at the next (t+1) time point.numeric vector with for each node its mean absolute impulse response on all other nodes at the (t+2)-th time point.as.table = TRUE the list items above are represented in tabular form as an object of
class matrix.
Future versions of this function may include additional statisticsridgeVAR1, sparsifyVAR1, graphVAR1# specify VAR(1) model parameters
A <- matrix(c(-0.1, -0.3, 0, 0.5, 0, 0, 0, 0, -0.4), byrow=TRUE, ncol=3)
P <- matrix(c(1, 0.5, 0, 0.5, 1, 0, 0, 0, 1), byrow=TRUE, ncol=3)
# adjacency matrix of (global) conditional independencies.
nodeStatsVAR1(A, P)Run the code above in your browser using DataLab