R: Auxiliary for Controlling ERGMM Fitting

control.ergmm {latentnet} | R Documentation

## Auxiliary for Controlling ERGMM Fitting

### Description

Auxiliary function as user interface for `ergmm` fitting. Typically only used when calling `ergmm`. It is used to set parameters that affect the sampling but do not affect the posterior distribution.

### Usage

    control.ergmm(
      sample.size = 4000,
      burnin = 10000,
      interval = 10,
      threads = 1,
      kl.threads = 1,
      mle.maxit = 100,
      Z.delta = 0.6,
      RE.delta = 0.6,
      group.deltas = 0.4,
      pilot.runs = 4,
      pilot.factor = 0.8,
      pilot.discard.first = 0.5,
      target.acc.rate = 0.234,
      backoff.threshold = 0.05,
      backoff.factor = 0.2,
      accept.all = FALSE,
      store.burnin = FALSE,
      refine.user.start = TRUE
    )

    ergmm.control(
      sample.size = 4000,
      burnin = 10000,
      interval = 10,
      threads = 1,
      kl.threads = 1,
      mle.maxit = 100,
      Z.delta = 0.6,
      RE.delta = 0.6,
      group.deltas = 0.4,
      pilot.runs = 4,
      pilot.factor = 0.8,
      pilot.discard.first = 0.5,
      target.acc.rate = 0.234,
      backoff.threshold = 0.05,
      backoff.factor = 0.2,
      accept.all = FALSE,
      store.burnin = FALSE,
      refine.user.start = TRUE
    )

### Arguments

`sample.size` | The number of draws to be taken from the posterior distribution.
`burnin` | The number of initial MCMC iterations to be discarded.
`interval` | The number of iterations between consecutive draws.
`threads` | The number of chains to run. If greater than 1, package `snowFT` is used to take advantage of any multiprocessing or distributed computing capabilities that may be available. Currently, only PVM (via `rpvm`) has been tested. Note, also, that PVM daemon needs to be started before the package is loaded.
`kl.threads` | If greather than 1, uses an experimental parallelized label-switching algorithm. This is not guaranteed to work.
`mle.maxit` | Maximum number of iterations for computing the starting values, posterior modes, MLEs, MKL estimates, etc..
`Z.delta` | Standard deviation of the proposal for the jump in the individual latent space position, or its starting value for the tuner.
`RE.delta` | Standard deviation of the proposal for the jump in the individual random effects values, or its starting value for the tuner.
`group.deltas` | A scalar, a vector, or a matrix of an appropriate size, giving the initial proposal structure for the “group proposal” of a jump in covariate coefficients, scaling of latent space positions, and a shift in random ffects. If a matrix of an appropriate size is given, it is used as a matrix of coefficients for a correlated proposal. If a vector is given, an independent proposal is used with the corresponding elements being proposal standard deviations. If a scalar is given, it is used as a multiplier for an initial heuristic for the proposal structure. It is usually best to leave this argument alone and let the adaptive sampling be used.
`pilot.runs` | Number of pilot runs into which to split the burn-in period. After each pilot run, the proposal standard deviations and coefficients `Z.delta`, `RE.delta`, and `group.deltas` are reevaluated. If set to `0`, disables adaptive sampling, and only makes a single burn-in run.
`pilot.factor` | Initial value for the factor by which the coefficients gotten by a Choletsky decomposition of the pilot sample covariance matrix are multiplied.
`pilot.discard.first` | Proportion of draws from the pilot run to discard for estimating acceptance rate and group proposal covariance.
`target.acc.rate` | Taget acceptance rate for the proposals used. After a pilot run, the proposal variances are adjusted upward if the acceptance rate is above this, and downward if below.
`backoff.threshold` | If a pilot run's acceptance rate is below this, redo it with drastically reduced proposal standard deviation. Set to `0` to disable this behavior.
`backoff.factor` | Factor by which to multiply the relevant proposal standard deviation if its acceptance rate fell below the backoff threshold.
`accept.all` | Forces all proposals to be accepted unconditionally. Use only in debugging proposal distributions!
`store.burnin` | If `TRUE`, the samples from the burnin are also stored and returned, to be used in MCMC diagnostics.
`refine.user.start` | If `TRUE`, the values passed to `ergmm` in the `user.start` argument can be updated by the mode-finding algorithm.

### Value

A list with the arguments as components.

### See Also

`ergmm`

### Examples

    data(sampson)
    ## Shorter run than default.
    samp.fit<-ergmm(samplike~euclidean(d=2,G=3)+rreceiver,
    control=ergmm.control(burnin=1000,sample.size= 2000,interval=5))

[Package latentnet version 2.10.5 Index]