Function allows for specification of options and settings for simulation and estimation procedures.
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)The burnin length for MCMC chains.
The sampling interval for MCMC chains.
The number of points to sample from MCMC chains for the MCMLE procedure.
The convergence tolerance for the Newton-Raphson optimization (implemented as Fisher scoring).
The maximum number of Newton-Raphson updates to perform.
The maximum number of MCMLE steps to perform.
(logical) Whether or not to use parallel processesing (defaults to TRUE).
The number of parallel cores to use for parallel computations.
(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.
The step_len adjustment multplier when convergence fails.
The step length adjustment default to be used for the Newton-Raphson updates.
The number of bridges to use for likelihood computations.
The burnin length for the bridge MCMC chain for approximate likelihood computation.
The sampling interval for the brdige MCMC chain for approximate likelihood computation.
The number of points to sample from the bridge MCMC chain for approximate likelihood computation.
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.
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.