stergm: Separable Temporal Exponential Family Random Graph Models...

In tergm: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models

Separable Temporal Exponential Family Random Graph Models (Deprecated)

Description

stergm is used for finding Separable Temporal ERGMs' (STERGMs) Conditional MLE (CMLE) (Krivitsky and Handcock, 2014) and Equilibrium Generalized Method of Moments Estimator (EGMME) (Krivitsky, 2009). This function is deprecated in favor of tergm(), whose special case it is, and may be removed in a future version.

Usage

stergm(
  nw,
  formation,
  dissolution,
  constraints = ~.,
  estimate,
  times = NULL,
  offset.coef.form = NULL,
  offset.coef.diss = NULL,
  targets = NULL,
  target.stats = NULL,
  eval.loglik = NVL(getOption("tergm.eval.loglik"), getOption("ergm.eval.loglik")),
  control = control.stergm(),
  verbose = FALSE,
  ...,
  SAN.offsets = NULL
)

Arguments

nw	A network object (for EGMME); or networkDynamic object, a network.list object, or a list containing networks (for CMLE and CMPLE). stergm understands the lasttoggle "API".
formation, dissolution	One-sided ergm-style formulas for the formation and dissolution models, respectively. In stergm, the dissolution formula is parameterized in terms of tie persistence: negative coefficients imply lower rates of persistence and postive coefficients imply higher rates. The dissolution effects are simply the negation of these coefficients.
constraints	A formula specifying one or more constraints on the support of the distribution of the networks being modeled. Multiple constraints may be given, separated by “+” and “-” operators. See ergmConstraint for the detailed explanation of their semantics and also for an indexed list of the constraints visible to the ergm package. The default is to have no constraints except those provided through the ergmlhs API. Together with the model terms in the formula and the reference measure, the constraints define the distribution of networks being modeled. It is also possible to specify a proposal function directly either by passing a string with the function's name (in which case, arguments to the proposal should be specified through the MCMC.prop.args argument to the relevant control function, or by giving it on the LHS of the hints formula to MCMC.prop argument to the control function. This will override the one chosen automatically. Note that not all possible combinations of constraints and reference measures are supported. However, for relatively simple constraints (i.e., those that simply permit or forbid specific dyads or sets of dyads from changing), arbitrary combinations should be possible.
estimate	One of "EGMME" for Equilibrium Generalized Method of Moments Estimation, based on a single network with some temporal information and making an assumption that it is a product of a STERGM process running to its stationary (equilibrium) distribution; "CMLE" for Conditional Maximum Likelihood Estimation, modeling a transition between two networks, or "CMPLE" for Conditional Maximum PseudoLikelihood Estimation, using MPLE instead of MLE. CMPLE is extremely inaccurate at this time.
times	For CMLE and CMPLE estimation, times or indexes at which the networks whose transition is to be modeled are observed. Default to c(0,1) if nw is a networkDynamic and to 1:length(nw) (all transitions) if nw is a network.list or a list. Unused for EGMME. Note that at this time, the selected time points will be treated as temporally adjacent. Irregularly spaced time series are not supported at this time.
offset.coef.form	Numeric vector to specify offset formation parameters.
offset.coef.diss	Numeric vector to specify offset dissolution parameters.
targets	One-sided ergm-style formula specifying statistics whose moments are used for the EGMME. Unused for CMLE and CMPLE. Targets is required for EGMME estimation. It may contain any valid ergm terms. Any offset terms are used only during the preliminary SAN run; they are removed automatically for the EGMME proper. If targets is specified as a character (one of "formation" and "dissolution") then the function .extract.fd.formulae is used to determine the corresponding formula; the user should be aware of its behavior and limitations.
target.stats	A vector specifying the values of the targets statistics that EGMME will try to match. Defaults to the statistics of nw. Unused for CMLE and CMPLE.
eval.loglik	Whether or not to calculate the log-likelihood of a CMLE STERGM fit. See ergm for details. Can be set globally via option(tergm.eval.loglik=...), falling back to getOption("ergm.eval.loglik") if not set.
control	A list of control parameters for algorithm tuning. Constructed using control.stergm. Remapped to control.tergm.
verbose	A logical or an integer to control the amount of progress and diagnostic information to be printed. FALSE/0 produces minimal output, with higher values producing more detail. Note that very high values (5+) may significantly slow down processing.
...	Additional arguments, to be passed to lower-level functions.
SAN.offsets	Offset coefficients (if any) to use during the SAN run.

Details

The stergm function uses a pair of formulas, formation and dissolution to model tie-dynamics. The dissolution formula, however, is parameterized in terms of tie persistence: negative coefficients imply lower rates of persistence and postive coefficients imply higher rates. The dissolution effects are simply the negation of these coefficients, but the discrepancy between the terminology and interpretation has always been unfortunate, and we have fixed this in the new tergm function.

If you are making the transition from old stergm to new tergm, note that the dissolution formula in stergm maps to the new Persist() operator in the tergm function, NOT the Diss() operator.

Value

stergm returns an object of class tergm; see tergm() for details and methods.

References

Krivitsky P.N. and Handcock M.S. (2014) A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society, Series B, 76(1): 29-46. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12014")}

Krivitsky, P.N. (2012). Modeling of Dynamic Networks based on Egocentric Data with Durational Information. Pennsylvania State University Department of Statistics Technical Report, 2012(2012-01). https://web.archive.org/web/20170830053722/https://stat.psu.edu/research/technical-report-files/2012-technical-reports/TR1201A.pdf

See Also

ergm, network, \

tergm documentation built on May 31, 2023, 8:29 p.m.