View source: R/corbit_plot_edit.r
r_corbit_plot | R Documentation |
Produces a R-Corbit plot for comparing the network autocorrelation (NACF) and partial network autocorrelation function (PNACF) values for a choice of maximum lag and maximum r
-stage depth. Starting from the first and continuing to the outermost ring, each ring corresponds to said choice of r
-stage depth. The numbers on the outermost ring are time-lags, and each dot corresponds to a specific time-slice or covariate-level.
r_corbit_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", r_corbit="yes")
vts_frames |
List containing the vector time series linked to each of the covariate-levels and/or time-slices, which the R-Corbit plot compares. |
network_list |
List of network objects for which the R-Corbit plot compares network autocorrelation or partial network autocorrelation. |
max_lag |
Maximum lag for the R-Corbit plot. |
max_stage |
Maximum |
weight_matrices |
List of weigth matrices, each weight matrix corresponds to a particular choice of time-slice or covariate-level. If all the time-slices have the same weight matrix, then the argument is a list, where all the entries are equal to the unique weight matrix. |
frame_names |
Indicates the name of each time-slice or covariate-level 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 covariate-levels share the same weight matrix. Default choice is no, if the time-slices or covariate-levels 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 R-Corbit plot. The default option is |
size_option |
Point size scale for the R-Corbit plot. Default is the absolute value of the network autocorrelation function (i.e., |
partial |
Option for selecting between computing the network autocorrelation function or the partial network autocorrelation function. Default choice is network autocorrelation (i.e., partial="no"). Change argument to "yes" for computing the partial network autocorrelation function (PNACF). |
r_corbit |
Choice for distinguishing between Corbit and R-Corbit plots, default is set to Corbit (inner function call). For producing R-Corbit plots one should use |
R-Corbit plots compare the network autocorrelation function (NACF) and partial network autocorrelation function (PNACF) values for a choice of different time-slices and/or covariate-levels. R-Corbit plots are read in the same manner as Corbit plots corbit_plot
, and include a legend on the right-hand side for distinguishing between covariate-levels 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-levels data splits. Essentially, if c \in \{1, \dots, C\}
, where C \in \mathbb{N}
is the number of covariate-levels or time-slices, then the value at the centre is \mathrm{(p)nacf}(h, r) = C^{-1} \sum_{c = 1}^{C} \mathrm{(p)nacf}_c (h, r),
where \mathrm{(p)nacf}_c(h, r)
is the (P)NACF value corresponding to the covariate-level/time-slice c
. The number of covariate-levels and time-slices C
must be equal to the length of the lists used for producing the R-Corbit plot.
Produces the specified, i.e., NACF or PNACF, values for a choice of lag and r
-stage depth, (h, r)
, R-Corbit plot. Does not print (P)NACF values, these are stored as invisble data frames (matrices), and can be accessed by printing or calling the object produced by the r_corbit_plot
call. The invisible object is a list of matrices, one matrix for each covariate-level/time-slice.
Guy Nason and Daniel Salnikov
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
## Not run:
#
# Produces a R-Corbit 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 R-Corbit plot with the same network and weights matrix
r_corbit_plot(list(sim1, sim2, sim3), list(fiveNet), 10, 3, list(W),
c("sim1", "sim2", "sim3"), same_net = "yes")
#
# Produce PNACF R-Corbit with different networks and weight matrices
print(r_corbit_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)
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