View source: R/correlation_structure_plots.R
active_node_plot | R Documentation |
Produces an active node matrix heat-map, which compares the local impact each node has on all the other ones (i.e., regressing j
on i
) once a model order has been chosen. The local relevance indes is
\mathrm{local} (i, j) := \bigg ( w_{ij} \sum_{k = 1}^{p} |\hat{\beta}_{kr}| \bigg ) \bigg \{ \sum_{l \in \mathcal{N} (i)} \sum_{r = 1}^{r^*} \sum_{k = 1}^{p} w_{il} |\hat{\beta}_{kr}| \bigg) \bigg \}^{-1},
which is closer to one the more relevant j
is when forecasting i
.
active_node_plot(vts, network, max_lag, r_stages)
vts |
Vector time series under study. |
network |
GNAR network object, which is the underlying network for the time series under study. |
max_lag |
Maximum lag of the fitted GNAR model - i.e., |
r_stages |
Neighbourhood regression oreder of the fitted GNAR model - i.e., |
Produces the local influence matrix heat-map for a specific model order. Does not return any values.
Daniel Salnikov and Guy Nason
Nason, G.P., Salnikov, D. and Cortina-Borja, M. (2023) New tools for network time series with an application to COVID-19 hospitalisations. https://arxiv.org/abs/2312.00530
#
# Produces an active node heat-map matrix from a stationary GNAR(2, [2, 1]) simulation.
#
gnar_simulation <- GNARsim(n = 100, net=fiveNet,
alphaParams = list(rep(0.25, 5), rep(0.12, 5)),
betaParams = list(c(0.25, 0.13), c(0.20)), sigma=1)
#
# Active node plot
#
active_node_plot(gnar_simulation, fiveNet, 2, c(2, 1))
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