set_options {mlergm}

Set and adjust options and settings.

Description

Function allows for specification of options and settings for simulation and estimation procedures.

Usage

set_options(
  burnin = 10000,
  interval = 1000,
  sample_size = 1000,
  NR_tol = 1e-04,
  NR_max_iter = 50,
  MCMLE_max_iter = 10,
  do_parallel = TRUE,
  number_cores = detectCores(all.tests = FALSE, logical = TRUE) - 1,
  adaptive_step_len = TRUE,
  step_len_multiplier = 0.5,
  step_len = 1,
  bridge_num = 10,
  bridge_burnin = 10000,
  bridge_interval = 500,
  bridge_sample_size = 5000
)

Arguments

burnin

The burnin length for MCMC chains.

interval

The sampling interval for MCMC chains.

sample_size

The number of points to sample from MCMC chains for the MCMLE procedure.

NR_tol

The convergence tolerance for the Newton-Raphson optimization (implemented as Fisher scoring).

NR_max_iter

The maximum number of Newton-Raphson updates to perform.

MCMLE_max_iter

The maximum number of MCMLE steps to perform.

do_parallel

(logical) Whether or not to use parallel processesing (defaults to TRUE).

number_cores

The number of parallel cores to use for parallel computations.

adaptive_step_len

(logical) If TRUE, an adaptive steplength procedure is used for the Newton-Raphson procedure. Arguments NR_step_len and NR_step_len_multiplier are ignored when adaptive_step_len is TRUE.

step_len_multiplier

The step_len adjustment multplier when convergence fails.

step_len

The step length adjustment default to be used for the Newton-Raphson updates.

bridge_num

The number of bridges to use for likelihood computations.

bridge_burnin

The burnin length for the bridge MCMC chain for approximate likelihood computation.

bridge_interval

The sampling interval for the brdige MCMC chain for approximate likelihood computation.

bridge_sample_size

The number of points to sample from the bridge MCMC chain for approximate likelihood computation.

Details

The main simulation settings are burnin, interval, and sample_size. For estimation of the loglikelihood value, options include bridge_num which controls the number of bridges to be used for approximating the loglikelihood (see, e.g., Hunter and Handcock (2006) for a discussion). The main estimation settings and options include NR_tol, NR_max_iter, MCMLE_max_iter, adaptive_step_len, and step_len. Parameters NR_tol and NR_max_iter control the convergence tolerance and maximum number of iterations for the Newton-Raphson, or Fisher scoring, optimization. When the L2 norm of the incremenet in the Newton-Raphson procedure is under the specified tolerance NR_tol convergence is reached; and, no more than NR_max_iter iterations are performed. The MCMLE procedure uses the stepping algorithn of Hummel, et al., (2012) to give stabiity to the estimation procedure. Each MCMLE iteration draws samples from an MCMC chain, and MCMLE_max_iter controls how many iterations are performed before termination. Most functions support parallel computing for efficiency; by default do_parallel is TRUE. The number of computing cores can be adjusted by number_cores, and the default is one less than the number of cores available.

References

Hunter, D. R., and Handcock, M. S. (2006). Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15(3), 565-583.

Hummel, R. M., Hunter, D. R., and Handcock, M. S. (2012). Improving simulation-based algorithms for fitting ERGMs. Journal of Computational and Graphical Statistics, 21(4), 920-939.