R: Draw from the distribution of an Exponential Random Graph...

simulate.ergmm {latentnet} | R Documentation

## Draw from the distribution of an Exponential Random Graph Mixed Model

### Description

If passed a `ergmm` fit object, `simulate` is used to simulate networks from the posterior of an exponetial random graph mixed model fit. Alternatively, a `ergmm.model` can be passed to simulate based on a particular parametr configuration.

### Usage

    ## S3 method for class 'ergmm'
    simulate(object, nsim = 1, seed = NULL, ...)

    ## S3 method for class 'ergmm.model'
    simulate(object, nsim = 1, seed = NULL, par, prior = list(), ...)

### Arguments

`object` | either an object of class `ergmm` for posterior simulation, or an object of class `ergmm.model` for a specific model.
`nsim` | number of networks to draw (independently)
`seed` | random seed to use; defaults to using the current state of the random number generator
`...` | Additional arguments. Currently unused.
`par` | a list with the parameter configuration based on which to simulate
`prior` | a list with the prior distribution parameters that deviate from their defaults

### Details

A sample of networks is randomly drawn from the specified model. If a needed value of `par` is missing, it is generated from its prior distribution.

### Value

If `nsim = 1`, `simulate` returns an object of class `network`. Otherwise, an object of class `network.series` that is a list consisting of the following elements:

`\$formula` | The formula used to generate the sample.
`\$networks` | A list of the generated networks.

### See Also

`ergmm`, `network`, `print.network`

### Examples

    #
    # Fit a short MCMC run: just the MCMC.
    #
    data(sampson)
    gest <- ergmm(samplike ~ euclidean(d=2,G=3),
                  control=ergmm.control(burnin=100,interval=5,sample.size=100),tofit="mcmc")
    #
    # Draw from the posterior
    #
    g.sim <- simulate(gest)
    plot(g.sim)
    #
    # Draw from the first draw from the posterior
    #
    g.sim <- with(gest,simulate(model,par=sample[[1]],prior=prior))
    plot(g.sim)

[Package latentnet version 2.10.5 Index]