Creates an object of class `sampler.iglm` (and `R6`) which holds all parameters controlling the MCMC sampling process for `iglm` models. This includes global settings like the number of simulations and burn-in, as well as references to specific samplers for the network (`z`) and attribute (`x`, `y`) components.
This function provides a convenient way to specify these settings before passing them to the `iglm` constructor or simulation functions.
Usage
sampler.iglm(
sampler_x = NULL,
sampler_y = NULL,
sampler_z = NULL,
n_simulation = 100,
n_burn_in = 10,
init_empty = TRUE,
seed = NA,
cluster = NULL,
file = NULL
)Arguments
- sampler_x
An object of class `sampler.net.attr` (created by `sampler.net.attr()`) specifying how to sample the `x_attribute`. If `NULL` (default), default `sampler.net.attr()` settings are used.
- sampler_y
An object of class `sampler.net.attr` specifying how to sample the `y_attribute`. If `NULL` (default), default settings are used.
- sampler_z
An object of class `sampler.net.attr` specifying how to sample the `z_network` ties *within* the defined neighborhood/overlap region. If `NULL` (default), default settings are used.
- n_simulation
(integer) The number of independent samples to generate after the burn-in period. Default: 100. Must be non-negative.
- n_burn_in
(integer) The number of MCMC iterations to discard at the start for burn-in. Default: 10. Must be non-negative.
- init_empty
(logical) If `TRUE` (default), initialize the MCMC chain from an empty state.
- seed
(integer or `NA`) A single integer seed set once before sampling begins to ensure reproducibility. If `NA` (default), a random seed is generated automatically.
- cluster
A parallel cluster object (e.g., from `parallel::makeCluster()`) for parallel simulations. If `NULL` (default), simulations run sequentially.
- file
(character or `NULL`) If provided, loads the sampler state from the specified .rds file instead of initializing from parameters.
Examples
n_actor <- 50
sampler_new <- sampler.iglm(
n_burn_in = 100, n_simulation = 10,
seed = 42,
sampler_x = sampler.net.attr(n_proposals = n_actor * 10),
sampler_y = sampler.net.attr(n_proposals = n_actor * 10),
sampler_z = sampler.net.attr(n_proposals = n_actor^2, tnt = TRUE),
init_empty = FALSE
)
sampler_new
#> Sampler settings
#> ------------------------------------------------------------
#> Core parameters
#> n_simulation :10
#> n_burn_in :100
#> init_empty :FALSE
#> seed :42
#>
#> Sub-samplers
#> sampler_x:
#> Number of proposals : 500
#> TNT sampling : TRUE
#> sampler_y:
#> Number of proposals : 500
#> TNT sampling : TRUE
#> sampler_z:
#> Number of proposals : 2500
#> TNT sampling : TRUE
sampler_new$seed
#> [1] 42
sampler_new$set_n_simulation(100)
sampler_new$n_simulation
#> [1] 100