sampler.iglm.generator: iglm Sampler Settings (R6 Class)
In iglm: Regression under Interference in Connected Populations
sampler.iglm.generator R Documentation
iglm Sampler Settings (R6 Class)
Description
The 'sampler.iglm' class is an R6 container for specifying and storing
the parameters that control the MCMC (Markov Chain Monte Carlo) sampling
process used in iglm simulations and potentially during estimation.
It includes settings for the number of simulations, burn-in period,
initialization, and
parallelization options. It also holds references to component samplers
(sampler.net.attr objects) responsible for sampling individual parts
(attributes x, y, network z).
Active bindings
sampler_x('sampler_net_attr') The sampler configuration object for the x attribute.
sampler_y('sampler_net_attr') The sampler configuration object for the y attribute.
sampler_z('sampler_net_attr') The sampler configuration object for the z network (overlap region).
n_simulation('integer') The number of configurations to simulate.
n_burn_in('integer') The number of initial MCMC iterations to discard.
init_empty('logical') Whether to initialize simulations from an empty state.
seed('integer') Read-only. The random seed used for sampling.
cluster('cluster' object or 'NULL') The parallel cluster object being used, or 'NULL'.
Methods
Public methods
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Method new()
Create a new 'sampler.iglm' object. Initializes all sampler settings,
using defaults for component samplers ('sampler.net.attr') if not provided,
and validates inputs.
Usage
sampler.iglm.generator$new(
sampler_x = NULL,
sampler_y = NULL,
sampler_z = NULL,
n_simulation = 100,
n_burn_in = 10,
init_empty = TRUE,
seed = NA,
cluster = NULL,
file = NULL
)
Arguments
sampler_xAn object of class 'sampler.net.attr' controlling
sampling for the x attribute. If 'NULL', defaults from 'sampler.net.attr()' are used.
sampler_yAn object of class 'sampler.net.attr' controlling
sampling for the y attribute. If 'NULL', defaults from 'sampler.net.attr()' are used.
sampler_zAn object of class 'sampler.net.attr' controlling
sampling for the z network (within the defined neighborhood/overlap).
If 'NULL', defaults from 'sampler.net.attr()' are used.
n_simulation(integer) The number of network/attribute configurations
to simulate and store after the burn-in period. Default is 100. Must be non-negative.
n_burn_in(integer) The number of initial MCMC iterations to discard
(burn-in) before starting to collect simulations. Default is 10. Must be non-negative.
init_empty(logical) If 'TRUE' (default), the MCMC chain is
initialized from an empty state (e.g., empty network, attributes at mean).
If 'FALSE', initialization might depend on the specific sampler implementation
(e.g., starting from observed data).
seed(integer or 'NA') A single integer seed for the random number
generator, set once before sampling begins. If 'NA' (default), a random
seed is generated automatically.
clusterA parallel cluster object (e.g., from the 'parallel' package)
to use for running simulations in parallel. If 'NULL' (default), simulations
are run sequentially.
file(character or 'NULL') If provided, loads the sampler state from
the specified .rds file instead of initializing from parameters.
Returns
A new 'sampler.iglm' object.
Method set_cluster()
Sets the parallel cluster object to be used for simulations.
Usage
sampler.iglm.generator$set_cluster(cluster)
Arguments
clusterA parallel cluster object from the 'parallel' package.
Method deactive_cluster()
Deactivates parallel processing for this sampler instance by setting
the internal cluster object reference to 'NULL'.
Usage
sampler.iglm.generator$deactive_cluster()
Returns
The 'sampler.iglm' object itself ('self'), invisibly.
Method set_n_simulation()
Sets the number of simulations to generate after burn-in.
Usage
sampler.iglm.generator$set_n_simulation(n_simulation)
Arguments
n_simulation(integer) The number of simulations to set.
Returns
None.
Method set_n_burn_in()
Sets the number of burn-in iterations.
Usage
sampler.iglm.generator$set_n_burn_in(n_burn_in)
Arguments
n_burn_in(integer) The number of burn-in iterations to set.
Returns
None.
Method set_init_empty()
Sets whether to initialize simulations from an empty state.
Usage
sampler.iglm.generator$set_init_empty(init_empty)
Arguments
init_empty(logical) 'TRUE' to initialize from empty, 'FALSE' otherwise.
Returns
None.
Method set_x_sampler()
Sets the sampler configuration for the x attribute.
Usage
sampler.iglm.generator$set_x_sampler(sampler_x)
Arguments
sampler_xAn object of class 'sampler_net_attr'.
Returns
None.
Method set_y_sampler()
Sets the sampler configuration for the y attribute.
Usage
sampler.iglm.generator$set_y_sampler(sampler_y)
Arguments
sampler_yAn object of class 'sampler_net_attr'.
Returns
None.
Method set_z_sampler()
Sets the sampler configuration for the z attribute.
Usage
sampler.iglm.generator$set_z_sampler(sampler_z)
Arguments
sampler_zAn object of class 'sampler_net_attr'.
Returns
None.
Method set_seed()
Sets the random seed for this sampler.
Usage
sampler.iglm.generator$set_seed(seed)
Arguments
seed(integer) The random seed to set.
Returns
None.
Method print()
Prints a formatted summary of the sampler configuration to the console.
Usage
sampler.iglm.generator$print(digits = 3, ...)
Arguments
digits(integer) Number of digits for formatting numeric values. Default: 3.
...Additional arguments (currently ignored).
Returns
The 'sampler.iglm' object itself ('self'), invisibly.
Method gather()
Gathers all data from private fields into a list.
Usage
sampler.iglm.generator$gather()
Returns
A list containing all information of the sampler.
Method save()
Save the object's complete state to a directory.
This will save the main sampler's settings to a file
named 'sampler.iglm_state.rds' within the specified
directory, and will also call the 'save()' method for each
nested sampler (.x, .y, .z), saving them into the same
directory.
Usage
sampler.iglm.generator$save(file)
Arguments
file(character) The file to a directory where the
state files will be saved. The directory will be created
if it does not exist.
Returns
The object itself, invisibly.
Method clone()
The objects of this class are cloneable with this method.
Usage
sampler.iglm.generator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
iglm documentation built on April 23, 2026, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| sampler.iglm.generator | R Documentation |
iglm Sampler Settings (R6 Class)
Description
The 'sampler.iglm' class is an R6 container for specifying and storing
the parameters that control the MCMC (Markov Chain Monte Carlo) sampling
process used in iglm simulations and potentially during estimation.
It includes settings for the number of simulations, burn-in period,
initialization, and
parallelization options. It also holds references to component samplers
(sampler.net.attr objects) responsible for sampling individual parts
(attributes x, y, network z).
Active bindings
sampler_x('sampler_net_attr') The sampler configuration object for the x attribute.
sampler_y('sampler_net_attr') The sampler configuration object for the y attribute.
sampler_z('sampler_net_attr') The sampler configuration object for the z network (overlap region).
n_simulation('integer') The number of configurations to simulate.
n_burn_in('integer') The number of initial MCMC iterations to discard.
init_empty('logical') Whether to initialize simulations from an empty state.
seed('integer') Read-only. The random seed used for sampling.
cluster('cluster' object or 'NULL') The parallel cluster object being used, or 'NULL'.
Methods
Public methods
Method new()
Create a new 'sampler.iglm' object. Initializes all sampler settings, using defaults for component samplers ('sampler.net.attr') if not provided, and validates inputs.
Usage
sampler.iglm.generator$new( sampler_x = NULL, sampler_y = NULL, sampler_z = NULL, n_simulation = 100, n_burn_in = 10, init_empty = TRUE, seed = NA, cluster = NULL, file = NULL )
Arguments
sampler_xAn object of class 'sampler.net.attr' controlling sampling for the x attribute. If 'NULL', defaults from 'sampler.net.attr()' are used.
sampler_yAn object of class 'sampler.net.attr' controlling sampling for the y attribute. If 'NULL', defaults from 'sampler.net.attr()' are used.
sampler_zAn object of class 'sampler.net.attr' controlling sampling for the z network (within the defined neighborhood/overlap). If 'NULL', defaults from 'sampler.net.attr()' are used.
n_simulation(integer) The number of network/attribute configurations to simulate and store after the burn-in period. Default is 100. Must be non-negative.
n_burn_in(integer) The number of initial MCMC iterations to discard (burn-in) before starting to collect simulations. Default is 10. Must be non-negative.
init_empty(logical) If 'TRUE' (default), the MCMC chain is initialized from an empty state (e.g., empty network, attributes at mean). If 'FALSE', initialization might depend on the specific sampler implementation (e.g., starting from observed data).
seed(integer or 'NA') A single integer seed for the random number generator, set once before sampling begins. If 'NA' (default), a random seed is generated automatically.
clusterA parallel cluster object (e.g., from the 'parallel' package) to use for running simulations in parallel. If 'NULL' (default), simulations are run sequentially.
file(character or 'NULL') If provided, loads the sampler state from the specified .rds file instead of initializing from parameters.
Returns
A new 'sampler.iglm' object.
Method set_cluster()
Sets the parallel cluster object to be used for simulations.
Usage
sampler.iglm.generator$set_cluster(cluster)
Arguments
clusterA parallel cluster object from the 'parallel' package.
Method deactive_cluster()
Deactivates parallel processing for this sampler instance by setting the internal cluster object reference to 'NULL'.
Usage
sampler.iglm.generator$deactive_cluster()
Returns
The 'sampler.iglm' object itself ('self'), invisibly.
Method set_n_simulation()
Sets the number of simulations to generate after burn-in.
Usage
sampler.iglm.generator$set_n_simulation(n_simulation)
Arguments
n_simulation(integer) The number of simulations to set.
Returns
None.
Method set_n_burn_in()
Sets the number of burn-in iterations.
Usage
sampler.iglm.generator$set_n_burn_in(n_burn_in)
Arguments
n_burn_in(integer) The number of burn-in iterations to set.
Returns
None.
Method set_init_empty()
Sets whether to initialize simulations from an empty state.
Usage
sampler.iglm.generator$set_init_empty(init_empty)
Arguments
init_empty(logical) 'TRUE' to initialize from empty, 'FALSE' otherwise.
Returns
None.
Method set_x_sampler()
Sets the sampler configuration for the x attribute.
Usage
sampler.iglm.generator$set_x_sampler(sampler_x)
Arguments
sampler_xAn object of class 'sampler_net_attr'.
Returns
None.
Method set_y_sampler()
Sets the sampler configuration for the y attribute.
Usage
sampler.iglm.generator$set_y_sampler(sampler_y)
Arguments
sampler_yAn object of class 'sampler_net_attr'.
Returns
None.
Method set_z_sampler()
Sets the sampler configuration for the z attribute.
Usage
sampler.iglm.generator$set_z_sampler(sampler_z)
Arguments
sampler_zAn object of class 'sampler_net_attr'.
Returns
None.
Method set_seed()
Sets the random seed for this sampler.
Usage
sampler.iglm.generator$set_seed(seed)
Arguments
seed(integer) The random seed to set.
Returns
None.
Method print()
Prints a formatted summary of the sampler configuration to the console.
Usage
sampler.iglm.generator$print(digits = 3, ...)
Arguments
digits(integer) Number of digits for formatting numeric values. Default: 3.
...Additional arguments (currently ignored).
Returns
The 'sampler.iglm' object itself ('self'), invisibly.
Method gather()
Gathers all data from private fields into a list.
Usage
sampler.iglm.generator$gather()
Returns
A list containing all information of the sampler.
Method save()
Save the object's complete state to a directory. This will save the main sampler's settings to a file named 'sampler.iglm_state.rds' within the specified directory, and will also call the 'save()' method for each nested sampler (.x, .y, .z), saving them into the same directory.
Usage
sampler.iglm.generator$save(file)
Arguments
file(character) The file to a directory where the state files will be saved. The directory will be created if it does not exist.
Returns
The object itself, invisibly.
Method clone()
The objects of this class are cloneable with this method.
Usage
sampler.iglm.generator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.