R: Auxiliary for Controlling Separable Temporal ERGM Fitting

control.stergm {tergm} | R Documentation

## Auxiliary for Controlling Separable Temporal ERGM Fitting

### Description

Auxiliary function as user interface for fine-tuning 'stergm' fitting.

### Usage

    control.stergm(init.form = NULL, init.diss = NULL,
      init.method = NULL, force.main = FALSE,
      MCMC.prop.weights.form = "default", MCMC.prop.args.form = NULL,
      MCMC.prop.weights.diss = "default", MCMC.prop.args.diss = NULL,
      MCMC.init.maxedges = 20000, MCMC.init.maxchanges = 20000,
      MCMC.packagenames = c(), CMLE.MCMC.burnin = 1024 * 16,
      CMLE.MCMC.interval = 1024, CMLE.control = NULL,
      CMLE.control.form = control.ergm(init = init.form, MCMC.burnin =
      CMLE.MCMC.burnin, MCMC.interval = CMLE.MCMC.interval, MCMC.prop.weights =
      MCMC.prop.weights.form, MCMC.prop.args = MCMC.prop.args.form,
      MCMC.init.maxedges = MCMC.init.maxedges, MCMC.packagenames =
      MCMC.packagenames, parallel = parallel, parallel.type = parallel.type,
      parallel.version.check = parallel.version.check, force.main =
      force.main), CMLE.control.diss = control.ergm(init = init.diss,
      MCMC.burnin = CMLE.MCMC.burnin, MCMC.interval = CMLE.MCMC.interval,
      MCMC.prop.weights = MCMC.prop.weights.diss, MCMC.prop.args =
      MCMC.prop.args.diss, MCMC.init.maxedges = MCMC.init.maxedges,
      MCMC.packagenames = MCMC.packagenames, parallel = parallel, parallel.type
      = parallel.type, parallel.version.check = parallel.version.check,
      force.main = force.main), CMLE.NA.impute = c(),
      CMLE.term.check.override = FALSE,
      EGMME.main.method = c("Gradient-Descent"),
      EGMME.MCMC.burnin.min = 1000, EGMME.MCMC.burnin.max = 1e+05,
      EGMME.MCMC.burnin.pval = 0.5, EGMME.MCMC.burnin.add = 1,
      MCMC.burnin = NULL, MCMC.burnin.mul = NULL, SAN.maxit = 4,
      SAN.nsteps.times = 8, SAN.control = control.san(term.options =
      term.options, SAN.maxit = SAN.maxit, SAN.prop.weights =
      MCMC.prop.weights.form, SAN.prop.args = MCMC.prop.args.form,
      SAN.init.maxedges = MCMC.init.maxedges, SAN.max.maxedges = Inf,
      SAN.nsteps = round(sqrt(EGMME.MCMC.burnin.min * EGMME.MCMC.burnin.max)) *
      SAN.nsteps.times, SAN.packagenames = MCMC.packagenames, parallel =
      parallel, parallel.type = parallel.type, parallel.version.check =
      parallel.version.check), SA.restarts = 10, SA.burnin = 1000,
      SA.plot.progress = FALSE, SA.max.plot.points = 400,
      SA.plot.stats = FALSE, SA.init.gain = 0.1, SA.gain.decay = 0.5,
      SA.runlength = 25, SA.interval.mul = 2, SA.init.interval = 500,
      SA.min.interval = 20, SA.max.interval = 500, SA.phase1.minruns = 4,
      SA.phase1.tries = 20, SA.phase1.jitter = 0.1,
      SA.phase1.max.q = 0.1, SA.phase1.backoff.rat = 1.05,
      SA.phase2.levels.max = 40, SA.phase2.levels.min = 4,
      SA.phase2.max.mc.se = 0.001, SA.phase2.repeats = 400,
      SA.stepdown.maxn = 200, SA.stepdown.p = 0.05, SA.stop.p = 0.1,
      SA.stepdown.ct = 5, SA.phase2.backoff.rat = 1.1, SA.keep.oh = 0.5,
      SA.keep.min.runs = 8, SA.keep.min = 0, SA.phase2.jitter.mul = 0.2,
      SA.phase2.maxreljump = 4, SA.guard.mul = 4, SA.par.eff.pow = 1,
      SA.robust = FALSE, SA.oh.memory = 1e+05, SA.refine = c("mean",
      "linear", "none"), SA.se = TRUE, SA.phase3.samplesize.runs = 10,
      SA.restart.on.err = TRUE, term.options = NULL, seed = NULL,
      parallel = 0, parallel.type = NULL, parallel.version.check = TRUE)

### Arguments

`init.form, init.diss` | numeric or `NA` vector equal in length to the number of parameters in the formation/dissolution model or `NULL` (the default); the initial values for the estimation and coefficient offset terms. If `NULL` is passed, all of the initial values are computed using the method specified by `control$init.method`. If a numeric vector is given, the elements of the vector are interpreted as follows: Elements corresponding to terms enclosed in `offset()` are used as the fixed offset coefficients. These should match the offset values given in `offset.coef.form`. Elements that do not correspond to offset terms and are not `NA` are used as starting values in the estimation. Initial values for the elements that are `NA` are fit using the method specified by `control$init.method`. Passing `control.ergm(init=coef(prev.fit))` can be used to "resume" an uncoverged `stergm` run, but see `enformulate.curved`.
`init.method` | Estimation method used to acquire initial values for estimation. If `NULL` (the default), the initial values are computed using the edges dissolution approximation (Carnegie et al.) when appropriate. If set to "zeros", the initial values are set to zeros.
`force.main` | Logical: If TRUE, then force MCMC-based estimation method, even if the exact MLE can be computed via maximum pseudolikelihood estimation.
`MCMC.prop.weights.form, MCMC.prop.weights.diss` | Specifies the method to allocate probabilities of being proposed to dyads in the formation/dissolution phase. Defaults to `"default"`, which picks a reasonable default for the specified constraint. Possible values include `"TNT"`, `"random"`, though not all values may be used with all possible constraints.
`MCMC.prop.args.form, MCMC.prop.args.diss` | An alternative, direct way of specifying additional arguments to the proposal in the formation/dissolution phase.
`MCMC.init.maxedges` | Maximum number of edges for which to allocate space.
`MCMC.init.maxchanges` | Maximum number of changes in dynamic network simulation for which to allocate space.
`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.
`CMLE.MCMC.burnin` | Maximum number of Metropolis-Hastings steps per phase (formation and dissolution) per time step used in CMLE fitting.
`CMLE.MCMC.interval` | Number of Metropolis-Hastings steps between successive draws when running MCMC MLE.
`CMLE.control` | A convenience argument for specifying both `CMLE.control.form` and `CMLE.control.diss` at once. See `control.ergm`.
`CMLE.control.form, CMLE.control.diss` | Control parameters used to fit the CMLE for the formation/dissolution ERGM. See `control.ergm`.
`CMLE.NA.impute` | In STERGM CMLE, missing dyads in transitioned-to networks are accommodated using methods of Handcock and Gile (2009), but a similar approach to transitioned-from networks requires much more complex methods that are not, currently, implemented. `CMLE.NA.impute` controls how missing dyads in transitioned-from networks are be imputed. See argument `imputers` of `impute.network.list` for details. By default, no imputation is performed, and the fitting stops with an error if any transitioned-from networks have missing dyads.
`CMLE.term.check.override` | The method `stergm{stergm}` uses at this time to fit a series of more than two networks requires certain assumptions to be made about the ERGM terms being used, which are tested before a fit is attempted. This test sometimes fails despite the model being amenable to fitting, so setting this option to `TRUE` overrides the tests.
`EGMME.main.method` | Estimation method used to find the Equilibrium Generalized Method of Moments estimator. Currently only "Gradient-Descent" is implemented.
`EGMME.MCMC.burnin.min, EGMME.MCMC.burnin.max,` | Number of Metropolis-Hastings steps per phase (formation and dissolution) per time step used in EGMME fitting. By default, this is determined adaptively by keeping track of increments in the Hamming distance between the transitioned-from network and the network being sampled (formation network or dissolution network). Once `EGMME.MCMC.burnin.min` steps have elapsed, the increments are tested against 0, and when their average number becomes statistically indistinguishable from 0 (with the p-value being greater than `EGMME.MCMC.burnin.pval`), or `EGMME.MCMC.burnin.max` steps are proposed, whichever comes first, the simulation is stopped after an additional `EGMME.MCMC.burnin.add` times the number of elapsed steps had been taken. (Stopping immediately would bias the sampling.) To use a fixed number of steps, set both `EGMME.MCMC.burnin.min` and `EGMME.MCMC.burnin.max` to the desired number of steps.
`EGMME.MCMC.burnin.pval, EGMME.MCMC.burnin.add` | Number of Metropolis-Hastings steps per phase (formation and dissolution) per time step used in EGMME fitting. By default, this is determined adaptively by keeping track of increments in the Hamming distance between the transitioned-from network and the network being sampled (formation network or dissolution network). Once `EGMME.MCMC.burnin.min` steps have elapsed, the increments are tested against 0, and when their average number becomes statistically indistinguishable from 0 (with the p-value being greater than `EGMME.MCMC.burnin.pval`), or `EGMME.MCMC.burnin.max` steps are proposed, whichever comes first, the simulation is stopped after an additional `EGMME.MCMC.burnin.add` times the number of elapsed steps had been taken. (Stopping immediately would bias the sampling.) To use a fixed number of steps, set both `EGMME.MCMC.burnin.min` and `EGMME.MCMC.burnin.max` to the desired number of steps.
`MCMC.burnin, MCMC.burnin.mul` | No longer used. See `EGMME.MCMC.burnin.min`, `EGMME.MCMC.burnin.max`, `EGMME.MCMC.burnin.pval`, `EGMME.MCMC.burnin.pval`, `EGMME.MCMC.burnin.add` and `CMLE.MCMC.burnin` and `CMLE.MCMC.interval`.
`SAN.maxit` | When `target.stats` argument is passed to `ergm()`, the maximum number of attempts to use `san` to obtain a network with statistics close to those specified.
`SAN.nsteps.times` | Multiplier for `SAN.nsteps` relative to `MCMC.burnin`. This lets one control the amount of SAN burn-in (arguably, the most important of SAN parameters) without overriding the other SAN.control defaults.
`SAN.control` | SAN control parameters. See `control.san`
`SA.restarts` | Maximum number of times to restart a failed optimization process.
`SA.burnin` | Number of time steps to advance the starting network before beginning the optimization.
`SA.plot.progress, SA.plot.stats` | Logical: Plot information about the fit as it proceeds. If `SA.plot.progress==TRUE`, plot the trajectories of the parameters and target statistics as the optimization progresses. If `SA.plot.stats==TRUE`, plot a heatmap reprsenting correlations of target statistics and a heatmap representing the estimated gradient. Do NOT use these with non-interactive plotting devices like `pdf`. (In fact, it will refuse to do that with a warning.)
`SA.max.plot.points` | If `SA.plot.progress==TRUE`, the maximum number of time points to be plotted. Defaults to 400. If more iterations elapse, they will be thinned to at most 400 before plotting.
`SA.init.gain` | Initial gain, the multiplier for the parameter update size. If the process initially goes crazy beyond recovery, lower this value.
`SA.gain.decay` | Gain decay factor.
`SA.runlength` | Number of parameter trials and updates per C run.
`SA.interval.mul` | The number of time steps between updates of the parameters is set to be this times the mean duration of extant ties.
`SA.init.interval` | Initial number of time steps between updates of the parameters.
`SA.min.interval, SA.max.interval` | Upper and lower bounds on the number of time steps between updates of the parameters.
`SA.phase1.minruns` | Number of runs during Phase 1 for estimating the gradient, before every gradient update.
`SA.phase1.tries` | Number of runs trying to find a reasonable parameter and network configuration.
`SA.phase1.jitter` | Initial jitter standard deviation of each parameter.
`SA.phase1.max.q` | Q-value (false discovery rate) that a gradient estimate must obtain before it is accepted (since sign is what is important).
`SA.phase1.backoff.rat, SA.phase2.backoff.rat` | If the run produces this relative increase in the approximate objective function, it will be backed off.
`SA.phase2.levels.min, SA.phase2.levels.max` | Range of gain levels (subphases) to go through.
`SA.phase2.max.mc.se` | Approximate precision of the estimates that must be attained before stopping.
`SA.phase2.repeats, SA.stepdown.maxn,` | A gain level may be repeated multiple times (up to `SA.phase2.repeats`) if the optimizer detects that the objective function is improving or the estimating equations are not centered around 0, so slowing down the parameters at that point is counterproductive. To detect this it looks at the the window controlled by `SA.keep.oh`, thinning objective function values to get `SA.stepdown.maxn`, and 1) fitting a GLS model for a linear trend, with AR(2) autocorrelation and 2) conductiong an approximate Hotelling's T^2 test for equality of estimating equation values to 0. If there is no significance for either at `SA.stepdown.p` `SA.stepdown.ct` runs in a row, the gain level (subphase) is allowed to end. Otherwise, the process continues at the same gain level.
`SA.stepdown.p, SA.stepdown.ct` | A gain level may be repeated multiple times (up to `SA.phase2.repeats`) if the optimizer detects that the objective function is improving or the estimating equations are not centered around 0, so slowing down the parameters at that point is counterproductive. To detect this it looks at the the window controlled by `SA.keep.oh`, thinning objective function values to get `SA.stepdown.maxn`, and 1) fitting a GLS model for a linear trend, with AR(2) autocorrelation and 2) conductiong an approximate Hotelling's T^2 test for equality of estimating equation values to 0. If there is no significance for either at `SA.stepdown.p` `SA.stepdown.ct` runs in a row, the gain level (subphase) is allowed to end. Otherwise, the process continues at the same gain level.
`SA.stop.p` | At the end of each gain level after the minimum, if the precision is sufficiently high, the relationship between the parameters and the targets is tested for evidence of local nonlinearity. This is the p-value used. If that test fails to reject, a Phase 3 run is made with the new parameter values, and the estimating equations are tested for difference from 0. If this test fails to reject, the optimization is finished. If either of these tests rejects, at `SA.stop.p`, optimization is continued for another gain level.
`SA.keep.oh, SA.keep.min, SA.keep.min.runs` | Parameters controlling how much of optimization history to keep for gradient and covariance estimation. A history record will be kept if it's at least one of the following: Among the last `SA.keep.oh` (a fraction) of all runs. Among the last `SA.keep.min` (a count) records. From the last `SA.keep.min.runs` (a count) optimization runs.
`SA.phase2.jitter.mul` | Jitter standard deviation of each parameter is this value times its standard deviation without jitter.
`SA.phase2.maxreljump` | To keep the optimization from "running away" due to, say, a poor gradient estimate building on itself, if a magnitude of change (Mahalanobis distance) in parameters over the course of a run divided by average magnitude of change for recent runs exceeds this, the change is truncated to this amount times the average for recent runs.
`SA.guard.mul` | The multiplier for the range of parameter and statistics values to compute the guard width.
`SA.par.eff.pow` | Because some parameters have much, much greater effects than others, it improves numerical conditioning and makes estimation more stable to rescale the kth estimating function by s_k = (∑_{i=1}^{q} G_{i,k}^2/V_{i,i})^{-p/2}, where G_{i,k} is the estimated gradient of the ith target statistics with respect to kth parameter. This parameter sets the value of p: `0` for no rescaling, `1` (default) for scaling by root-mean-square normalized gradient, and greater values for greater penalty.
`SA.robust` | Whether to use robust linear regression (for gradients) and covariance estimation.
`SA.oh.memory` | Absolute maximum number of data points per thread to store in the full optimization history.
`SA.refine` | Method, if any, used to refine the point estimate at the end: "linear" for linear interpolation, "mean" for average, and "none" to use the last value.
`SA.se` | Logical: If TRUE (the default), get an MCMC sample of statistics at the final estimate and compute the covariance matrix (and hence standard errors) of the parameters. This sample is stored and can also be used by `mcmc.diagnostics.stergm` to assess convergence.
`SA.phase3.samplesize.runs` | This many optimization runs will be used to determine whether the optimization has converged and to estimate the standard errors.
`SA.restart.on.err` | Logical: if `TRUE` (the default) an error somewhere in the optimization process will cause it to restart with a smaller gain value. Otherwise, the process will stop. This is mainly used for debugging
`term.options` | A list of additional arguments to be passed to term initializers. It can also be set globally via `option(ergm.term=list(...))`.
`seed` | Seed value (integer) for the random number generator. See `set.seed`
`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 `"MPI"` and `"PSOCK"`. Defaults to using the `parallel` package default.
`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.

### Details

This function is only used within a call to the `stergm` function. See the `usage` section in `stergm` for details.

### Value

A list with arguments as components.

### References

- Boer, P., Huisman, M., Snijders, T.A.B., and Zeggelink, E.P.H. (2003), StOCNET User's Manual. Version 1.4.

- Firth (1993), Bias Reduction in Maximum Likelihood Estimates. Biometrika, 80: 27-38.

- Hunter, D. R. and M. S. Handcock (2006), Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15: 565-583.

- Hummel, R. M., Hunter, D. R., and Handcock, M. S. (2010), A Steplength Algorithm for Fitting ERGMs, Penn State Department of Statistics Technical Report.

### See Also

`stergm`. The `control.simulate.stergm` function performs a similar function for `simulate.stergm`.

[Package tergm version 3.6.1 Index]