Title:	Just Another Latent Space Network Clustering Algorithm
Version:	1.1.0
Description:	Fit latent space network cluster models using an expectation-maximization algorithm. Enables flexible modeling of unweighted or weighted network data (with or without noise edges), supporting both directed and undirected networks (with or without degree heterogeneity). Designed to handle large networks efficiently, it allows users to explore network structure through latent space representations, identify clusters (i.e., community detection) within network data, and simulate networks with varying clustering, connectivity patterns, and noise edges. Methodology for the implementation is described in Arakkal and Sewell (2025) <doi:10.1016/j.csda.2025.108228>.
License:	GPL (≥ 3)
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
LinkingTo:	Rcpp, RcppArmadillo
Imports:	Rcpp (≥ 1.0.10), Matrix, extraDistr, mclust, scales, aricode, stringdist, utils, splines, rlang, future.apply, future, progressr, progress, igraph, methods
RoxygenNote:	7.3.2
URL:	https://github.com/a1arakkal/JANE
BugReports:	https://github.com/a1arakkal/JANE/issues
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2025-06-06 20:35:13 UTC; alanarakkal
Author:	Alan Arakkal [aut, cre, cph], Daniel Sewell [aut]
Maintainer:	Alan Arakkal <alan-arakkal@uiowa.edu>
Repository:	CRAN
Date/Publication:	2025-06-06 23:50:02 UTC

Fit JANE

Description

Fit a latent space cluster model, with or without noise edges, using an EM algorithm.

Usage

JANE(
  A,
  D = 2,
  K = 2,
  family = "bernoulli",
  noise_weights = FALSE,
  guess_noise_weights = NULL,
  model,
  initialization = "GNN",
  case_control = FALSE,
  DA_type = "none",
  seed = NULL,
  control = list()
)

Arguments

Details

Isolates are removed from the adjacency matrix A. If an unsymmetric adjacency matrix A is supplied for model %in% c('NDH', 'RS') the user will be asked if they would like to proceed with converting A to a symmetric matrix (i.e., A <- 1.0 * ( (A + t(A)) > 0.0 )); only able to do so if family = 'bernoulli'. Additionally, if a weighted network is supplied and noise_weights = FALSE, then the network will be converted to an unweighted binary network (i.e., (A > 0.0)*1.0) and a latent space cluster model is fit.

control:

The control argument is a named list that the user can supply containing the following components:

verbose

A logical; if TRUE causes additional information to be printed out about the progress of the EM algorithm (default is FALSE).

max_its

An integer specifying the maximum number of iterations for the EM algorithm (default is 1e3).

min_its

An integer specifying the minimum number of iterations for the EM algorithm (default is 10).

priors

A list of prior hyperparameters (default is NULL). See specify_priors on how to specify the hyperparameters.

n_interior_knots

(only relevant for model %in% c('RS', 'RSR')) An integer specifying the number of interior knots used in fitting a natural cubic spline for degree heterogeneity models (default is 5).

termination_rule

A character string to specify the termination rule to determine the convergence of the EM algorithm:

• 'prob_mat': uses change in the absolute difference in \hat{Z}^{U} (i.e., the N \times K cluster membership probability matrix) between subsequent iterations (default)

• 'Q': uses change in the absolute difference in the objective function of the E-step evaluated using parameters from subsequent iterations

• 'ARI': comparing the classifications between subsequent iterations using adjusted Rand index

• 'NMI': comparing the classifications between subsequent iterations using normalized mutual information

• 'CER': comparing the classifications between subsequent iterations using classification error rate

tolerance

A numeric specifying the tolerance used for termination_rule %in% c('Q', 'prob_mat') (default is 1e-3).

tolerance_ARI

A numeric specifying the tolerance used for termination_rule = 'ARI' (default is 0.999).

tolerance_NMI

A numeric specifying the tolerance used for termination_rule = 'NMI' (default is 0.999).

tolerance_CER

A numeric specifying the tolerance used for termination_rule = 'CER' (default is 0.01).

n_its_start_CA

An integer specifying what iteration to start computing the change in cumulative averages (note: the change in the cumulative average of \hat{U}, the latent position matrix, is not tracked when termination_rule = 'Q') (default is 20).

tolerance_diff_CA

A numeric specifying the tolerance used for the change in cumulative average of termination_rule metric and \hat{U} (note: the change in the cumulative average of \hat{U} is not tracked when termination_rule = 'Q') (default is 1e-3).

consecutive_diff_CA

An integer specifying the tolerance for the number of consecutive instances where the change in cumulative average is less than tolerance_diff_CA (default is 5).

quantile_diff

A numeric in [0,1] specifying the quantile used in computing the change in the absolute difference of \hat{Z}^{U} and \hat{U} between subsequent iterations (default is 1, i.e., max).

beta_temp_schedule

(Experimental) A numeric vector specifying the temperature schedule for deterministic annealing (default is 1, i.e., deterministic annealing not utilized).

n_control

An integer specifying the fixed number of controls (i.e., non-links) sampled for each actor; only relevant when case_control = TRUE (default is 100 when case_control = TRUE and NULL when case_control = FALSE).

n_start

An integer specifying the maximum number of starts for the EM algorithm (default is 5).

max_retry

An integer specifying the maximum number of re-attempts if starting values cause issues with EM algorithm (default is 5).

IC_selection

A character string to specify the information criteria used to select the optimal fit based on the combinations of K, D, and n_start considered:

• 'BIC_model': BIC computed from logistic regression or Hurdle model component

• 'BIC_mbc': BIC computed from model based clustering component

• 'ICL_mbc': ICL computed from model based clustering component

• 'Total_BIC': sum of 'BIC_model' and 'BIC_mbc'

• 'Total_ICL': sum of 'BIC_model' and 'ICL_mbc' (default)

sd_random_U_GNN

(only relevant when initialization = 'GNN') A positive numeric value specifying the standard deviation for the random draws from a normal distribution to initialize U (default is 1).

max_retry_GNN

(only relevant when initialization = 'GNN') An integer specifying the maximum number of re-attempts for the GNN approach before switching to random starting values (default is 10).

n_its_GNN

(only relevant when initialization = 'GNN') An integer specifying the maximum number of iterations for the GNN approach (default is 10).

downsampling_GNN

(only relevant when initialization = 'GNN') A logical; if TRUE employs downsampling s.t. the number of links and non-links are balanced for the GNN approach (default is TRUE).

Running JANE in parallel:

JANE integrates the future and future.apply packages to fit the various combinations of K, D, and n_start in parallel. The 'Examples' section below provides an example of how to run JANE in parallel. See plan and future.apply for more details.

Choosing the number of clusters:

JANE allows for the following model selection criteria to choose the number of clusters (smaller values are favored):

• 'BIC_model': BIC computed from logistic regression or Hurdle model component

• 'BIC_mbc': BIC computed from model based clustering component

• 'ICL_mbc': ICL (Biernacki et al. (2000)) computed from model based clustering component

• 'Total_BIC': Sum of 'BIC_model' and 'BIC_mbc', this is the model selection criterion proposed by Handcock et al. (2007)

• 'Total_ICL': (default) sum of 'BIC_model' and 'ICL_mbc', this criterion is similar to 'Total_BIC', but uses ICL for the model based clustering component, which tends to favor more well-separated clusters.

Based on simulation studies, Biernacki et al. (2000) recommends that when the interest in mixture modeling is cluster analysis, instead of density estimation, the ICL_{mbc} criterion should be favored over the BIC_{mbc} criterion, as the BIC_{mbc} criterion tends to overestimate the number of clusters. The BIC_{mbc} criterion is designed to choose the number of components in a mixture model rather than the number of clusters.

Warning: It is not certain whether it is appropriate to use the model selection criterion above to select D.

Value

A list of S3 class "JANE" containing the following components:

References

Biernacki, C., Celeux, G., Govaert, G., 2000. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725.

Handcock, M.S., Raftery, A.E., Tantrum, J.M., 2007. Model-based clustering for social networks. Journal of the Royal Statistical Society Series A: Statistics in Society 170, 301–354.

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")

# Run JANE on simulated data - consider multiple D and K
res <- JANE::JANE(A = sim_data$A,
                  D = 2:5,
                  K = 2:10,
                  initialization = "GNN", 
                  model = "NDH",
                  case_control = FALSE,
                  DA_type = "none")
                  
# Run JANE on simulated data - parallel with 5 cores
# future::plan(future::multisession, workers = 5)
# res <- JANE::JANE(A = sim_data$A,
#                   D = 2L,
#                   K = 3L,
#                   initialization = "GNN", 
#                   model = "NDH",
#                   case_control = FALSE,
#                   DA_type = "none")
# future::plan(future::sequential)

# Run JANE on simulated data - case/control approach with 20 controls sampled for each actor
res <- JANE::JANE(A = sim_data$A,
                  D = 2L,
                  K = 3L,
                  initialization = "GNN", 
                  model = "NDH",
                  case_control = TRUE,
                  DA_type = "none",
                  control = list(n_control = 20))
                   
# Reproducibility
res1 <- JANE::JANE(A = sim_data$A,
                   D = 2L,
                   K = 3L,
                   initialization = "GNN", 
                   seed = 1234,
                   model = "NDH",
                   case_control = FALSE,
                   DA_type = "none")

res2 <- JANE::JANE(A = sim_data$A,
                   D = 2L,
                   K = 3L,
                   initialization = "GNN", 
                   seed = 1234,
                   model = "NDH",
                   case_control = FALSE,
                   DA_type = "none")  

## Check if results match
all.equal(res1, res2)    

# Another reproducibility example where the seed was not set. 
# It is possible to replicate the results using the starting values due to 
# the nature of EM algorithms
res3 <- JANE::JANE(A = sim_data$A,
                   D = 2L,
                   K = 3L,
                   initialization = "GNN", 
                   model = "NDH",
                   case_control = FALSE,
                   DA_type = "none")
## Extract starting values                    
start_vals <- res3$optimal_start  

## Run JANE using extracted starting values, no need to specify D and K 
## below as function will determine those values from start_vals
res4 <- JANE::JANE(A = sim_data$A,
                   initialization = start_vals, 
                   model = "NDH",
                   case_control = FALSE,
                   DA_type = "none")
                   
## Check if optimal_res are identical
all.equal(res3$optimal_res, res4$optimal_res)                   
                            

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

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)

Simulate unweighted or weighted networks, with or without noise edges, from latent space cluster models

Description

Simulate an unweighted or weighted network, with or without noise edges, from a D-dimensional latent space cluster model with K clusters and N actors. The squared euclidean distance is used (i.e., dist(U_i,U_j)^2), where U_i and U_j are the respective actor's positions in a D-dimensional social space.

Usage

sim_A(
  N,
  mus,
  omegas,
  p,
  model = "NDH",
  family = "bernoulli",
  params_LR,
  params_weights = NULL,
  noise_weights_prob = 0,
  mean_noise_weights,
  precision_noise_weights,
  remove_isolates = TRUE
)

Arguments

Details

The returned scalar q_prob represents the proportion of non-edges in the simulated network to be converted to noise edges, computed as \frac{p_{noise} \times D_{A}}{(1-D_{A}) \times (1-p_{noise})}, where D_{A} is the density of the simulated network without noise and p_{noise} is the inputted noise_weights_prob.

Value

A list containing the following components:

Examples

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

# Simulate an undirected, unweighted network, with no noise and no degree heterogeneity
JANE::sim_A(N = 100L, 
            model = "NDH",
            mus = mus, 
            omegas = omegas, 
            p = p, 
            params_LR = list(beta0 = beta0),
            remove_isolates = TRUE)

# Simulate a directed, weighted network, with no noise and degree heterogeneity
JANE::sim_A(N = 100L, 
            model = "RSR",
            family = "lognormal",
            mus = mus, 
            omegas = omegas, 
            p = p, 
            params_LR = list(beta0 = beta0),
            params_weights = list(beta0 = 2,
                                  precision_weights = 1),
            remove_isolates = TRUE)

# Simulate an undirected, weighted network, with noise and degree heterogeneity
JANE::sim_A(N = 100L, 
            model = "RS",
            family = "poisson",
            mus = mus, 
            omegas = omegas, 
            p = p, 
            params_LR = list(beta0 = beta0),
            params_weights = list(beta0 = 2),
            noise_weights_prob = 0.1,
            mean_noise_weights = 1,
            remove_isolates = TRUE)

Specify starting values for EM algorithm

Description

A function that allows the user to specify starting values for the EM algorithm in a structure accepted by JANE.

Usage

specify_initial_values(
  A,
  D,
  K,
  model,
  family = "bernoulli",
  noise_weights = FALSE,
  n_interior_knots = NULL,
  U,
  omegas,
  mus,
  p,
  Z,
  beta,
  beta2,
  precision_weights,
  precision_noise_weights
)

Arguments

Details

To match JANE, this function will remove isolates from the adjacency matrix A and determine the total number of actors after excluding isolates. If this is not done, errors with respect to incorrect dimensions in the starting values will be generated when executing JANE.

Similarly to match JANE, if an unsymmetric adjacency matrix A is supplied for model %in% c('NDH', 'RS') the user will be asked if they would like to proceed with converting A to a symmetric matrix (i.e., A <- 1.0 * ( (A + t(A)) > 0.0 )). Additionally, if a weighted network is supplied and noise_weights = FALSE, then the network will be converted to an unweighted binary network (i.e., (A > 0.0)*1.0).

Value

A list of starting values for the EM algorithm generated from the input values in a structure accepted by JANE.

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
sim_data <- JANE::sim_A(N = 100L, 
                        model = "RSR",
                        mus = mus, 
                        omegas = omegas, 
                        p = p, 
                        params_LR = list(beta0 = beta0),
                        remove_isolates = TRUE)

# Specify starting values
D <- 3L
K <- 5L
N <- nrow(sim_data$A)
n_interior_knots <- 5L

U <- matrix(stats::rnorm(N*D), nrow = N, ncol = D)
omegas <- stats::rWishart(n = K, df = D+1, Sigma = diag(D))
mus <- matrix(stats::rnorm(K*D), nrow = K, ncol = D)
p <- extraDistr::rdirichlet(n = 1, rep(3,K))[1,]
Z <-  extraDistr::rdirichlet(n = N, alpha = rep(1, K))
beta <- stats::rnorm(n = 1 + 2*(1 + n_interior_knots))

my_starting_values <- JANE::specify_initial_values(A = sim_data$A,
                                                   D = D,
                                                   K = K,
                                                   model = "RSR",
                                                   n_interior_knots = n_interior_knots,
                                                   U = U,
                                                   omegas = omegas, 
                                                   mus = mus, 
                                                   p = p, 
                                                   Z = Z,
                                                   beta = beta)         

# Run JANE using my_starting_values (no need to specify D and K as function will 
# determine those values from my_starting_values)
res <- JANE::JANE(A = sim_data$A,
                  initialization = my_starting_values,
                  model = "RSR")

Specify prior hyperparameters for EM algorithm

Description

A function that allows the user to specify the prior hyperparameters for the EM algorithm in a structure accepted by JANE.

Usage

specify_priors(
  D,
  K,
  model,
  family = "bernoulli",
  noise_weights = FALSE,
  n_interior_knots = NULL,
  a,
  b,
  c,
  G,
  nu,
  e,
  f,
  h,
  l,
  e_2,
  f_2,
  m_1,
  o_1,
  m_2,
  o_2
)

Arguments

Details

Prior on \mu_k and \Omega_k (note: the same prior is used for k = 1,\ldots,K) :

\pi(\mu_k, \Omega_k) = \pi(\mu_k | \Omega_k) \pi(\Omega_k), thus

\mu_k | \Omega_k \sim MVN(a, (b\Omega_k)^{-1})

\Omega_k \sim Wishart(c, G^{-1})

Prior on p:

For the current implementation we require that all elements of the nu vector be \ge 1 to prevent against negative mixture weights for empty clusters.

p \sim Dirichlet(\nu_1 ,\ldots,\nu_K)

Prior on \beta_{LR}:

\beta_{LR} \sim MVN(e, f^{-1})

Prior on \beta_{GLM}:

\beta_{GLM} \sim MVN(e_{2}, f_{2}^{-1})

Prior on q:

q \sim Beta(h, l)

Prior on \tau^2_{weights}:

\tau^2_{weights} \sim Gamma(\frac{m_1}{2}, \frac{o_1}{2})

Prior on \tau^2_{noise \ weights}:

\tau^2_{noise \ weights} \sim Gamma(\frac{m_2}{2}, \frac{o_2}{2})

Value

A list of prior hyperparameters for the EM algorithm generated from the input values in a structure accepted by JANE.

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 = "RS",
                        mus = mus, 
                        omegas = omegas, 
                        p = p, 
                        params_LR = list(beta0 = beta0), 
                        remove_isolates = TRUE)
                        
                        
# Specify prior hyperparameters
D <- 3L
K <- 5L
n_interior_knots <- 5L

a <- rep(1, D)
b <- 3
c <- 4
G <- 10*diag(D)
nu <- rep(2, K)
e <- rep(0.5, 1 + (n_interior_knots + 1))
f <- diag(c(0.1, rep(0.5, n_interior_knots + 1)))

my_prior_hyperparameters <- specify_priors(D = D,
                                           K = K,
                                           model = "RS",
                                           n_interior_knots = n_interior_knots,
                                           a = a,
                                           b = b,
                                           c = c,
                                           G = G,
                                           nu = nu,
                                           e = e,
                                           f = f)
                                           
# Run JANE on simulated data using supplied prior hyperparameters
res <- JANE::JANE(A = sim_data$A,
                  D = D,
                  K = K,
                  initialization = "GNN",
                  model = "RS",
                  case_control = FALSE,
                  DA_type = "none",
                  control = list(priors = my_prior_hyperparameters))
                                                         

Summarizing JANE fits

Description

S3 summary method for object of class "JANE".

Usage

## S3 method for class 'JANE'
summary(object, true_labels = NULL, initial_values = FALSE, ...)

Arguments

Value

A list of S3 class "summary.JANE" containing the following components (Note: N is the number of actors in the network, K is the number of clusters, and D is the dimension of the latent space):

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")
                  
# Summarize fit 
summary(res)

# Summarize fit and compare to true cluster labels
summary(res, true_labels = apply(sim_data$Z_U, 1, which.max))

# Summarize fit using starting values of EM algorithm
summary(res, initial_values = TRUE)