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Plot JANE fits

Source: R/generics.R
plot.JANE.Rd

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.

Value

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

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.

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

# \donttest{
# 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)
# }