View source: R/control.simulate.R

control.simulate.ergm | R Documentation |

Auxiliary function as user interface for fine-tuning ERGM
simulation. `control.simulate`

, `control.simulate.formula`

, and
`control.simulate.formula.ergm`

are all aliases for the same
function.

While the others supply a full set of simulation
settings, `control.simulate.ergm`

when passed as a control
parameter to `simulate.ergm()`

allows some settings to be
inherited from the ERGM stimation while overriding others.

```
control.simulate.formula.ergm(
MCMC.burnin = MCMC.interval * 16,
MCMC.interval = 1024,
MCMC.prop = trim_env(~sparse + .triadic),
MCMC.prop.weights = "default",
MCMC.prop.args = list(),
MCMC.batch = NULL,
MCMC.effectiveSize = NULL,
MCMC.effectiveSize.damp = 10,
MCMC.effectiveSize.maxruns = 1000,
MCMC.effectiveSize.burnin.pval = 0.2,
MCMC.effectiveSize.burnin.min = 0.05,
MCMC.effectiveSize.burnin.max = 0.5,
MCMC.effectiveSize.burnin.nmin = 16,
MCMC.effectiveSize.burnin.nmax = 128,
MCMC.effectiveSize.burnin.PC = FALSE,
MCMC.effectiveSize.burnin.scl = 1024,
MCMC.effectiveSize.order.max = NULL,
MCMC.maxedges = Inf,
MCMC.packagenames = c(),
MCMC.runtime.traceplot = FALSE,
network.output = "network",
term.options = NULL,
parallel = 0,
parallel.type = NULL,
parallel.version.check = TRUE,
parallel.inherit.MT = FALSE,
...
)
control.simulate(
MCMC.burnin = MCMC.interval * 16,
MCMC.interval = 1024,
MCMC.prop = trim_env(~sparse + .triadic),
MCMC.prop.weights = "default",
MCMC.prop.args = list(),
MCMC.batch = NULL,
MCMC.effectiveSize = NULL,
MCMC.effectiveSize.damp = 10,
MCMC.effectiveSize.maxruns = 1000,
MCMC.effectiveSize.burnin.pval = 0.2,
MCMC.effectiveSize.burnin.min = 0.05,
MCMC.effectiveSize.burnin.max = 0.5,
MCMC.effectiveSize.burnin.nmin = 16,
MCMC.effectiveSize.burnin.nmax = 128,
MCMC.effectiveSize.burnin.PC = FALSE,
MCMC.effectiveSize.burnin.scl = 1024,
MCMC.effectiveSize.order.max = NULL,
MCMC.maxedges = Inf,
MCMC.packagenames = c(),
MCMC.runtime.traceplot = FALSE,
network.output = "network",
term.options = NULL,
parallel = 0,
parallel.type = NULL,
parallel.version.check = TRUE,
parallel.inherit.MT = FALSE,
...
)
control.simulate.formula(
MCMC.burnin = MCMC.interval * 16,
MCMC.interval = 1024,
MCMC.prop = trim_env(~sparse + .triadic),
MCMC.prop.weights = "default",
MCMC.prop.args = list(),
MCMC.batch = NULL,
MCMC.effectiveSize = NULL,
MCMC.effectiveSize.damp = 10,
MCMC.effectiveSize.maxruns = 1000,
MCMC.effectiveSize.burnin.pval = 0.2,
MCMC.effectiveSize.burnin.min = 0.05,
MCMC.effectiveSize.burnin.max = 0.5,
MCMC.effectiveSize.burnin.nmin = 16,
MCMC.effectiveSize.burnin.nmax = 128,
MCMC.effectiveSize.burnin.PC = FALSE,
MCMC.effectiveSize.burnin.scl = 1024,
MCMC.effectiveSize.order.max = NULL,
MCMC.maxedges = Inf,
MCMC.packagenames = c(),
MCMC.runtime.traceplot = FALSE,
network.output = "network",
term.options = NULL,
parallel = 0,
parallel.type = NULL,
parallel.version.check = TRUE,
parallel.inherit.MT = FALSE,
...
)
control.simulate.ergm(
MCMC.burnin = NULL,
MCMC.interval = NULL,
MCMC.scale = 1,
MCMC.prop = NULL,
MCMC.prop.weights = NULL,
MCMC.prop.args = NULL,
MCMC.batch = NULL,
MCMC.effectiveSize = NULL,
MCMC.effectiveSize.damp = 10,
MCMC.effectiveSize.maxruns = 1000,
MCMC.effectiveSize.burnin.pval = 0.2,
MCMC.effectiveSize.burnin.min = 0.05,
MCMC.effectiveSize.burnin.max = 0.5,
MCMC.effectiveSize.burnin.nmin = 16,
MCMC.effectiveSize.burnin.nmax = 128,
MCMC.effectiveSize.burnin.PC = FALSE,
MCMC.effectiveSize.burnin.scl = 1024,
MCMC.effectiveSize.order.max = NULL,
MCMC.maxedges = Inf,
MCMC.packagenames = NULL,
MCMC.runtime.traceplot = FALSE,
network.output = "network",
term.options = NULL,
parallel = 0,
parallel.type = NULL,
parallel.version.check = TRUE,
parallel.inherit.MT = FALSE,
...
)
```

`MCMC.burnin` |
Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number. |

`MCMC.interval` |
Number of proposals between sampled statistics. |

`MCMC.prop` |
Specifies the proposal (directly) and/or
a series of "hints" about the structure of the model being
sampled. The specification is in the form of a one-sided formula
with hints separated by A common and default "hint" is |

`MCMC.prop.weights` |
Specifies the proposal
distribution used in the MCMC Metropolis-Hastings algorithm. Possible
choices depending on selected |

`MCMC.prop.args` |
An alternative, direct way of specifying additional arguments to proposal. |

`MCMC.batch` |
if not 0 or |

`MCMC.effectiveSize` , `MCMC.effectiveSize.damp` , `MCMC.effectiveSize.maxruns` , `MCMC.effectiveSize.burnin.pval` , `MCMC.effectiveSize.burnin.min` , `MCMC.effectiveSize.burnin.max` , `MCMC.effectiveSize.burnin.nmin` , `MCMC.effectiveSize.burnin.nmax` , `MCMC.effectiveSize.burnin.PC` , `MCMC.effectiveSize.burnin.scl` , `MCMC.effectiveSize.order.max` |
Set After each run, the returned statistics are mapped to the
estimating function scale, then an exponential decay model is fit
to the scaled statistics to find that burn-in which would reduce
the difference between the initial values of statistics and their
equilibrium values by a factor of A Geweke diagnostic is then run, after thinning the sample to
If The effective size of the post-burn-in sample is computed via
\insertCiteVaFl15m;textualergm, and compared to the target
effective size. If it is not matched, the MCMC run is resumed,
with the additional draws needed linearly extrapolated but
weighted in favor of the baseline Lastly, if |

`MCMC.maxedges` |
The maximum number of edges that may occur during the MCMC sampling. If this number is exceeded at any time, sampling is stopped immediately. |

`MCMC.packagenames` |
Names of packages in which to look for change statistic functions in addition to those autodetected. This argument should not be needed outside of very strange setups. |

`MCMC.runtime.traceplot` |
Logical: If |

`network.output` |
R class with which to output networks. The options are "network" (default) and "edgelist.compressed" (which saves space but only supports networks without vertex attributes) |

`term.options` |
A list of additional arguments to be passed to term initializers. See |

`parallel` |
Number of threads in which to run the sampling. Defaults to 0 (no parallelism). See the entry on parallel processing for details and troubleshooting. |

`parallel.type` |
API to use for parallel processing. Supported values
are |

`parallel.version.check` |
Logical: If TRUE, check that the version of ergm running on the slave nodes is the same as that running on the master node. |

`parallel.inherit.MT` |
Logical: If TRUE, slave nodes and
processes inherit the |

`...` |
A dummy argument to catch deprecated or mistyped control parameters. |

`MCMC.scale` |
For |

This function is only used within a call to the ERGM `simulate()`

function. See the Usage section in `simulate.ergm()`

for
details.

A list with arguments as components.

`simulate.ergm()`

, `simulate.formula()`

.
`control.ergm()`

performs a similar function for
`ergm()`

; `control.gof()`

performs a similar function
for `gof()`

.

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