A Function to predict edge weights from a GERGM fit object.

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Description

Performs edgewise predictions from a GERGM model fit.

Usage

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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)

Arguments

GERGM_Object

A GERGM object output by the gergm() estimation function. The folowing 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).

simulation_method

Default is "Metropolis" which allows for exponential downweighting, can also be "Gibbs".

number_of_networks_to_simulate

Number of simulations generated for estimation via MCMC. Default is 500.

thin

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.

proposal_variance

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.

MCMC_burnin

Number of samples from the MCMC simulation procedure that will be discarded before drawing the samples used for estimation. Default is 100.

seed

Seed used for reproducibility. Default is 123.

return_constrained_networks

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.

optimize_proposal_variance

Logical indicating whether proposal variance should be optimized if using Metropolis Hastings for simulation. Defaults to FALSE.

target_accept_rate

Defaults to 0.25, can be used to optimize Metropolis Hastings simulations.

use_stochastic_MH

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. Defualts to FALSE. HIGHLY EXPERIMENTAL!

stochastic_MH_proportion

Percentage of dyads/triads to use for approximation, defaults to 0.25

Value

A list object containing simulated networks.