Performs edgewise predictions from a GERGM model fit.
conditional_edge_prediction(GERGM_Object, simulation_method = c("Metropolis",
"Gibbs"), number_of_networks_to_simulate = 500, thin = 1,
proposal_variance = 0.1, MCMC_burnin = 100, seed = 123,
return_constrained_networks = FALSE, optimize_proposal_variance = FALSE,
target_accept_rate = 0.25, use_stochastic_MH = FALSE,
stochastic_MH_proportion = 1)A GERGM object output by the gergm() estimation function. The following terms must still be specified: number_of_networks_to_simulate, thin, and MCMC_burnin. proposal_variance may also be specified, or if set equal to NULL, then the proposal variance from parameter estimation will be instead (this option is likely preferred in most situations).
Default is "Metropolis" which allows for exponential down weighting, can also be "Gibbs".
Number of simulations generated for estimation via MCMC. Default is 500.
The proportion of samples that are kept from each simulation. For example, thin = 1/200 will keep every 200th network in the overall simulated sample. Default is 1.
The variance specified for the Metropolis Hastings simulation method. This parameter is inversely proportional to the average acceptance rate of the M-H sampler and should be adjusted so that the average acceptance rate is approximately 0.25. Default is 0.1.
Number of samples from the MCMC simulation procedure that will be discarded before drawing the samples used for estimation. Default is 100.
Seed used for reproducibility. Default is 123.
Logical argument indicating whether simulated networks should be transformed back to observed scale or whether constrained [0,1] networks should be returned. Defaults to FALSE, in which case networks are returned on observed scale.
Logical indicating whether proposal variance should be optimized if using Metropolis Hastings for simulation. Defaults to FALSE.
Defaults to 0.25, can be used to optimize Metropolis Hastings simulations.
A logical indicating whether a stochastic approximation to the h statistics should be used under Metropolis Hastings in-between thinned samples. This may dramatically speed up estimation. Defaults to FALSE. HIGHLY EXPERIMENTAL!
Percentage of dyads/triads to use for approximation, defaults to 0.25
A list object containing simulated networks.