plot.JANE: Plot JANE fits

In JANE: Just Another Latent Space Network Clustering Algorithm

Plot JANE fits

Description

S3 plot method for object of class "JANE".

Usage

## S3 method for class 'JANE'
plot(
  x,
  type = "lsnc",
  true_labels,
  initial_values = FALSE,
  zoom = 100,
  density_type = "contour",
  rotation_angle = 0,
  alpha_edge = 0.1,
  alpha_node = 1,
  swap_axes = FALSE,
  main,
  xlab,
  ylab,
  cluster_cols,
  remove_noise_edges = FALSE,
  ...
)

Arguments

x	An object of S3 class "JANE", a result of a call to JANE.
type	A character string to select the type of plot: 'lsnc': plot the network using the estimated latent positions and color-code actors by cluster (default) 'misclassified': (can only be used if true_labels is !NULL) similar to 'lsnc', but will color misclassified actors in black 'uncertainty': similar to 'lsnc', but here the color gradient applied represents the actor-specific classification uncertainty 'trace_plot': presents various trace plots across the iterations of the EM algorithm
true_labels	(optional) A numeric, character, or factor vector of known true cluster labels. Must have the same length as number of actors in the fitted network. Need to account for potential isolates removed.
initial_values	A logical; if TRUE then plots fit using the starting parameters used in the EM algorithm (default is FALSE, i.e., the results after the EM algorithm is run are plotted).
zoom	A numeric value > 0 that controls the % magnification of the plot (default is 100%).
density_type	Choose from one of the following three options: 'contour' (default), 'hdr', 'image', and 'persp' indicating the density plot type.
rotation_angle	A numeric value that rotates the estimated latent positions and contours of the multivariate normal distributions clockwise (or counterclockwise if swap_axes = TRUE) through the specified angle about the origin (default is 0 degrees). Only relevant when D (i.e., dimension of the latent space) >= 2 and type != 'trace_plot'.
alpha_edge	A numeric value in [0,1] that controls the transparency of the network edges (default is 0.1).
alpha_node	A numeric value in [0,1] that controls the transparency of the actors in the network (default is 1).
swap_axes	A logical; if TRUE will swap the x and y axes (default is FALSE).
main	An optional overall title for the plot.
xlab	An optional title for the x axis.
ylab	An optional title for the y axis.
cluster_cols	An optional vector of colors for the clusters. Must have a length of at least K.
remove_noise_edges	(only applicable if JANE was run with noise_weights = TRUE) A logical; if TRUE will remove noise edges based on hard clustering rule of \{h | \hat{Z}^{W}_{eh} = max(\hat{Z}^{W}_{e1}, \hat{Z}^{W}_{e2})\} for e = 1,\ldots,|E|, where \hat{Z}^{W}_{e1} and \hat{Z}^{W}_{e2} are the estimated conditional probabilities that the e^{th} edge is a non-noise and noise edge, respectively (default is FALSE).
...	Unused.

Details

The classification of actors into specific clusters is based on a hard clustering rule of \{h | \hat{Z}^{U}_{ih} = max_k \hat{Z}^{U}_{ik}\}. Additionally, the actor-specific classification uncertainty is derived as 1 - max_k \hat{Z}^{U}_{ik}.

The trace plot contains up to five unique plots tracking various metrics across the iterations of the EM algorithm, depending on the JANE control parameter termination_rule:

• termination_rule = 'prob_mat': Five plots will be presented. Specifically, in the top panel, the plot on the left presents the change in the absolute difference in {\hat{Z}^U} (i.e., the N \times K cluster membership probability matrix) between subsequent iterations and, if noise_weights = TRUE, the change in the absolute difference in {\hat{Z}^W} (i.e., the |E| \times 2 edge weight cluster membership probability matrix) between subsequent iterations. The exact quantile of the absolute difference plotted are presented in parentheses and determined by the JANE control parameter quantile_diff. For example, the default control parameter quantile_diff = 1, so the values being plotted are the max absolute difference in {\hat{Z}^U} (and potentially {\hat{Z}^W}) between subsequent iterations. The plot on the right of the top panel presents the absolute difference in the cumulative average of the absolute change in {\hat{Z}^U} (and potentially {\hat{Z}^W}) and \hat{U} (i.e., the N \times D matrix of latent positions) across subsequent iterations (absolute change in {\hat{Z}^U}, {\hat{Z}^W}, and \hat{U} are computed in an identical manner as described previously). This metric is only tracked beginning at an iteration determined by the n_its_start_CA control parameter in JANE. Note, this plot may be empty if the EM algorithm converges before the n_its_start_CA-th iteration. Finally, the bottom panel presents ARI, NMI, and CER values comparing the classifications between subsequent iterations, respectively. Specifically, at a given iteration we determine the classification of actors in clusters based on a hard clustering rule of \{h | \hat{Z}^{U}_{ih} = max_k \hat{Z}^{U}_{ik}\} and given these labels from two subsequent iterations, we compute and plot the ARI, NMI and CER.

• termination_rule = 'Q': Plots generated are similar to those described in the previous bullet point. However, instead of tracking the change in {\hat{Z}^U} (and potentially {\hat{Z}^W}) over iterations, here the absolute difference in the objective function of the E-step evaluated using parameters from subsequent iterations is tracked. Furthermore, the cumulative average of the absolute change in \hat{U} is no longer tracked.

• termination_rule %in% c('ARI', 'NMI', 'CER'): Four plots will be presented. Specifically, the top left panel presents a plot of the absolute difference in the cumulative average of the absolute change in the specific termination_rule employed and \hat{U} across iterations. As previously mentioned, if the EM algorithm converges before the n_its_start_CA-th iteration then this will be an empty plot. Furthermore, the other three plots present ARI, NMI, and CER values comparing the classifications between subsequent iterations, respectively.

Value

A plot of the network or trace plot of the EM run.

Note

If an error interrupts the plotting process, the graphics device may be left in a state where par("new") = TRUE. This can cause subsequent plots to be overlaid. To reset the graphics state, call plot.new() or close and reopen the device with dev.off(); dev.new().

See Also

surfacePlot, adjustedRandIndex, classError, NMI

Examples

# Simulate network
mus <- matrix(c(-1,-1,1,-1,1,1), 
              nrow = 3,
              ncol = 2, 
              byrow = TRUE)
omegas <- array(c(diag(rep(7,2)),
                  diag(rep(7,2)), 
                  diag(rep(7,2))), 
                  dim = c(2,2,3))
p <- rep(1/3, 3)
beta0 <- 1.0
sim_data <- JANE::sim_A(N = 100L, 
                        model = "NDH",
                        mus = mus, 
                        omegas = omegas, 
                        p = p, 
                        params_LR = list(beta0 = beta0), 
                        remove_isolates = TRUE)
                        
# Run JANE on simulated data
res <- JANE::JANE(A = sim_data$A,
                  D = 2L,
                  K = 3L,
                  initialization = "GNN", 
                  model = "NDH",
                  case_control = FALSE,
                  DA_type = "none")

# plot trace plot
plot(res, type = "trace_plot")
                   
# plot network
plot(res)

# plot network - misclassified
plot(res, type = "misclassified", true_labels = apply(sim_data$Z_U, 1, which.max))

# plot network - uncertainty and swap axes
plot(res, type = "uncertainty", swap_axes = TRUE)

# plot network - but only show contours of MVNs
plot(res, swap_axes = TRUE, alpha_edge = 0, alpha_node = 0)

# plot using starting values of EM algorithm
plot(res, initial_values = TRUE)

JANE documentation built on Aug. 12, 2025, 1:08 a.m.