Nothing
# an S4 class for gergm objects
setClass(Class = "gergm",
representation = representation(
network = "matrix",
bounded.network = "matrix",
formula = "formula",
stats = "matrix",
theta.coef = "data.frame",
lambda.coef = "data.frame",
weights = "numeric",
num_nodes = "numeric",
MCMC_output = "list",
observed_network = "matrix",
observed_bounded_network = "matrix",
data_transformation = "array",
stats_to_use = "numeric",
previous_theta.coef = "data.frame",
previous_lambda.coef = "data.frame",
reduced_weights = "numeric",
theta.par = "numeric",
lambda.par = "numeric",
theta_estimation_converged = "logical",
simulated_vs_observed_p_values = "numeric",
acceptable_fit = "logical",
lambda_estimation_converged = "logical",
observed_simulated_t_test = "data.frame",
console_output = "character",
print_output = "logical",
is_correlation_network = "logical",
beta_correlation_model = "logical",
directed_network = "logical",
simulation_only = "logical",
thresholds = "numeric",
BZ = "matrix",
BZstdev = "numeric",
transformation_type = "character",
downweight_statistics_together = "logical",
hyperparameter_optimization = "logical",
target_accept_rate = "numeric",
proposal_variance = "numeric",
estimation_method = "character",
number_of_simulations = "numeric",
thin = "numeric",
burnin = "numeric",
MPLE_gain_factor = "numeric",
simulated_statistics_for_GOF = "data.frame",
hessians = "list",
standard_errors = "list",
theta_grid_optimization_list = "list",
using_grid_optimization = "logical",
mu = "numeric",
phi = "numeric",
weighted_MPLE = "logical",
fine_grained_pv_optimization = "logical",
parallel = "logical",
parallel_statistic_calculation = "logical",
cores = "numeric",
use_stochastic_MH = "logical",
stochastic_MH_proportion = "numeric",
endogenous_statistic_node_sets = "list",
non_base_statistic_indicator = "numeric",
legacy_statistics = "numeric",
legacy_alphas = "numeric",
legacy_thetas = "data.frame",
legacy_thresholds = "numeric",
use_legacy_MH_sampler = "logical",
theta_names = "character",
possible_endogenous_statistic_indices = "numeric",
statistic_auxiliary_data = "list",
full_theta_names = "character",
covariate_terms_only = "logical",
simulated_bounded_networks_for_GOF = "array",
additional_stats = "list",
slackr_integration_list = "list",
using_slackr_integration = "logical",
start_time = "character",
end_time = "character",
elapsed_time = "character",
distribution_estimator = "character",
include_diagonal = "logical",
user_specified_initial_thetas = "numeric",
use_user_specified_initial_thetas = "logical",
integration_intervals = "numeric",
regularization_weight = "numeric",
convex_hull_proportion = "numeric",
start_with_zeros = "logical",
convex_hull_convergence_proportion = "numeric",
optimization_method = "character",
sample_edges_at_a_time = "numeric",
use_previous_thetas = "logical"
),
validity = function(object) {
if (!"matrix" %in% class(object@network) & is.null(object@network)
== FALSE) {
stop("'network' must be a 'matrix' object or 'NULL'.")
}
if (!"matrix" %in% class(object@bounded.network)) {
stop("'bounded.network' must be a 'matrix' object.")
}
if (!is.data.frame(object@theta.coef)) {
stop("'theta.coef' must be a 'data.frame' object.")
}
if (!"formula" %in% class(object@formula)) {
stop("'formula' is not a 'formula' object.")
}
if (!is.data.frame(object@lambda.coef) & is.null(object@lambda.coef)
== FALSE) {
stop("'lambda.coef' must be a 'data.frame' object or 'NULL'.")
}
if (!"matrix" %in% class(object@stats)) {
stop("'stats' must be a 'matrix' object.")
}
if (!is.numeric(object@weights) & is.null(object@weights)
== FALSE) {
stop("'weights' must be a 'data.frame' object or 'NULL'.")
}
if (!is.numeric(object@num_nodes) & is.null(object@num_nodes)
== FALSE) {
stop("'num_nodes' must be a 'data.frame' object or 'NULL'.")
}
if (!"list" %in% class(object@MCMC_output)) {
stop("'MCMC_output' must be a 'list' object.")
}
if (!"matrix" %in% class(object@observed_network)) {
stop("'observed_network' must be a 'matrix' object.")
}
if (!"matrix" %in% class(object@observed_bounded_network)) {
stop("'observed_bounded_network' must be a 'matrix' object.")
}
if (!"array" %in% class(object@data_transformation)) {
stop("'data_transformation' must be a 'array' object.")
}
if (!is.numeric(object@stats_to_use) & is.null(object@stats_to_use)
== FALSE) {
stop("'stats_to_use' must be a 'numeric' object or 'NULL'.")
}
if (!is.data.frame(object@previous_lambda.coef) & is.null(object@previous_lambda.coef)
== FALSE) {
stop("'previous_lambda.coef' must be a 'data.frame' object or 'NULL'.")
}
if (!is.data.frame(object@previous_theta.coef) & is.null(object@previous_theta.coef)
== FALSE) {
stop("'previous_theta.coef' must be a 'data.frame' object or 'NULL'.")
}
if (!is.numeric(object@reduced_weights) & is.null(object@reduced_weights)
== FALSE) {
stop("'reduced_weights' must be a 'numeric' object or 'NULL'.")
}
if (!is.numeric(object@theta.par) & is.null(object@theta.par)
== FALSE) {
stop("'theta.par' must be a 'numeric' object or 'NULL'.")
}
if (!is.numeric(object@lambda.par) & is.null(object@lambda.par)
== FALSE) {
stop("'lambda.par' must be a 'numeric' object or 'NULL'.")
}
if (!is.numeric(object@simulated_vs_observed_p_values)
& is.null(object@simulated_vs_observed_p_values) == FALSE) {
stop("'simulated_vs_observed_p_values' must be a 'numeric' object or 'NULL'.")
}
if (!"logical" %in% class(object@theta_estimation_converged)) {
stop("'theta_estimation_converged' must be a 'logical' value.")
}
if (!"logical" %in% class(object@lambda_estimation_converged)) {
stop("'lambda_estimation_converged' must be a 'logical' value.")
}
if (!"logical" %in% class(object@acceptable_fit)) {
stop("'acceptable_fit' must be a 'logical' value.")
}
if (!is.data.frame(object@observed_simulated_t_test)
& is.null(object@observed_simulated_t_test)
== FALSE) {
stop("'observed_simulated_t_test' must be a 'data.frame' object or 'NULL'.")
}
if (!is.character(object@console_output) & is.null(object@console_output)
== FALSE) {
stop("'console_output' must be a 'character' object or 'NULL'.")
}
if (!is.character(object@console_output) & is.null(object@console_output)
== FALSE) {
stop("'console_output' must be a 'character' object or 'NULL'.")
}
return(TRUE)
}
)
# define coef for pretty output of gergm object
setMethod(f = "coef", signature = "gergm", definition = function(object, ...) {
return(list(Theta = object@theta.coef, Lambda = object@lambda.coef))
}
)
# define 'show' to get a pretty output of the gergm
setMethod(f = "show", signature = "gergm", definition = function(object){
message("Theta:")
print(object@theta.coef)
message("Lambda:")
print(object@lambda.coef)
message("Weights:")
print(object@weights)
message("Network Statistics:")
print(object@stats)
}
)
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