wagner_plot {GNAR}

Produces a Wagner plot for the specified choice of covariates and/or time slices.

Description

Produces a Wagner plot for comparing the netowrk autocorrelation and partial network autocorrerlation function values for a choice of maximum lag and maximum r-stage depth. The plot consists of rings linked to a time-slice or covariate, and indicates if the network autocorrelation differs for different time-slices and/or covariates. It is a visual for studying whether or not the network time series acts differently depending on lag or covariate. Starting from one and continuing to the outside, each ring corresponds to said choice of r-stage depth, the numbers on the outside ring are time-lags, and each dot corresponds to a specific time-slice or covariate.

Usage

wagner_plot(vts_frames, network_list, max_lag, max_stage, weight_matrices, 
frame_names, same_net="no", viridis_color_option="viridis", size_option="absolute_val", 
partial="no", wagner="yes")

Arguments

vts_frames

List containing the vector time series linked to each of the covariate and/or time-slice, which the Wagner plot compares.

network_list

List of network objects for which the Wagner plot compares network autocorrelation or partial network autocorrelation.

max_lag

Maximum lag for the Wagner plot.

max_stage

Maximum r-stage depth for the Wagner plot (i.e., the number of rings in the Wagner plot).

weight_matrices

List of weight matrices, each weight matrix corresponds to a particular choice of time-slice or covariate. If all the time-slices have the same weight matrix, then the argument is a list with said list.

frame_names

Indicates the name of each time-slice or covariate time series, order should be the same as in the weight matrices and vector time series lists.

same_net

Indicates whether or not all time-slices or covariates share the same weight matrix. Default choice is no, if the time-slices or covariates share the same weight matrix, then this argument should be set to "yes" (i.e., same_net = "yes").

viridis_color_option

Colour scale for the Corbit plot. The default option is viridis, each option is colout blind friendly; see viridis package.

size_option

Point size scale for the Corbit plot. The default is the absolute value of the network autocorrelation function (i.e., |nacf(h, r)| or |pnacf(h, r)|). Alternate option is the coefficient of determination coming from a global-\alpha model with fixed lag and stage.

partial

Option for selecting between computing the network autocorrelation function or the partial network autocorrelation funciton. Default choice is network autocorrelation (i.e., partial="no"), change argument to "yes" for computing the partial network autocorrelation funciton.

wagner

Choice for distinguishing between Corbit and Wagner plots, default is set to Corbit (inner function call). For producing Wagner plots one should use wagner_plot.

Details

Wagner plots compare the network autocorrelation function (NACF) and partial network autocorrelation function (PNACF) values of different time-slices and/or covariate weights. Wagner plots are read in the same manner as Corbit plots corbit_plot, and include a legend on the right-hand side for distinguishing between covariates and/or time-slices. The point at the centre is the mean value of the NACF or PNACF values arising from the time-slices and/or covariate data splits. Essentially, if c \in \{1, \dots, C\}, where C \in \mathbb{N} is the number of covariates or time-slices, then the value at the centre is \mathrm{nacf}(h, r) = C^{-1} \sum_{c = 1}^{C} \mathrm{nacf}_c (h, r), where \mathrm{nacf}_c(h, r) is the NACF value corresponding to the covariate/time-slice c. The number of covariates and time-slices C must be equal to the length of the lists used for producing the Wagner plot.

Value

Produces the specified Wagner plot, does not return the network autocorrelaiton values.

Author(s)

Daniel Salnikov and Guy Nason.

References

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

Examples

## Not run: 
#
# Produces a Wagner plot, which compares three stationary GNAR simulations, 
# where the underlying network is fiveNet.
#
# Compute the weight matrix
W = weights_matrix(fiveNet)
#
# Simulate three stationary GNAR processe
sim1 <-  GNARsim(n = 100, net=fiveNet, alphaParams = list(c(0.1, 0.12, 0.16, 0.075, 0.21),
                                                          c(0.12, 0.14, 0.15, 0.6, 0.22)), 
                 betaParams = list(c(0.1, 0.16), c(0.11, 0.14)))

sim2 <-  GNARsim(n = 100, net=fiveNet, alphaParams = list(rep(.25, 5)), 
                 betaParams = list(c(0.1, 0.16)))

sim3 <- GNARsim(n = 100, net=fiveNet, alphaParams = list(rep(.25, 5), rep(0.13, 5)), 
                betaParams = list(c(0.1, 0.16), c(0.11)))
                
# Produce NACF Wagner plot with the same network and weights matrix
wagner_plot(list(sim1, sim2, sim3), list(fiveNet), 10, 3, list(W), 
		c("sim1", "sim2", "sim3"), same_net = "yes")
# 
# Produce PNACF Wagner with different networks and weight matrices
wagner_plot(list(sim1, sim2, sim3), list(fiveNet, fiveNet, fiveNet), 10, 3, list(W, W, W), 
		c("sim1", "sim2", "sim3"), same_net = "no", partial = "yes")

## End(Not run)