tergm: Temporal Exponential-Family Random Graph Models

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

Temporal Exponential-Family Random Graph Models

Description

tergm() fits Temporal ERGMs' (TERGMs) and Separable Temporal ERGMs' (STERGMs) Conditional MLE (CMLE) (Krivitsky and Handcock, 2010) and Equilibrium Generalized Method of Moments Estimator (EGMME) (Krivitsky, 2009).

Usage

tergm(
  formula,
  constraints = ~.,
  estimate,
  times = NULL,
  offset.coef = NULL,
  targets = NULL,
  target.stats = NULL,
  SAN.offsets = NULL,
  eval.loglik = NVL(getOption("tergm.eval.loglik"), getOption("ergm.eval.loglik")),
  control = control.tergm(),
  verbose = FALSE,
  ...,
  basis = eval_lhs.formula(formula)
)

Arguments

formula	an ERGM formula.
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 TERGM 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. This argument is mandatory if nw is a networkDynamic and defaults to 1:length(nw) (all transitions) if nw is a network.list or a list. Ignored when estimating EGMME or if LHS is already a NetSeries. Note that at this time, the selected time points will be treated as temporally adjacent. Irregluarly spaced time series are not supported at this time.
offset.coef	Numeric vector to specify offset 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.
SAN.offsets	Offset coefficients (if any) to use during the SAN run.
eval.loglik	Whether or not to calculate the log-likelihood of a CMLE TERGM 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.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.
basis	optional network data overriding the left hand side of formula

Value

tergm() returns an object of class tergm that inherits from ergm and has the usual methods (coef.ergm(), summary.ergm(), mcmc.diagnostics(), etc.) implemented for it. Note that gof() only works for the CMLE method.

References

Krackhardt, D and Handcock, MS (2006) Heider vs Simmel: Emergent features in dynamic structures. ICML Workshop on Statistical Network Analysis. Springer, Berlin, Heidelberg, 2006.

Hanneke S, Fu W, and Xing EP (2010). Discrete Temporal Models of Social Networks. Electronic Journal of Statistics, 2010, 4, 585-605. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/09-EJS548")}

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://arxiv.org/abs/2203.06866

See Also

network, networkDynamic, and NetSeries() for the data structures, ergm() and ergmTerm for model specification, package vignette browseVignettes(package='tergm') for a short demonstration, the Statnet web site https://statnet.org/workshop-tergm/ for a tutorial

Examples

## Not run: 
# EGMME Example
par(ask=FALSE)
n<-30
g0<-network.initialize(n,dir=FALSE)

#                     edges, degree(1), mean.age
target.stats<-c(      n*1/2,    n*0.6,        20)

dynfit<-tergm(g0 ~ Form(~edges + degree(1)) + Diss(~edges),
               targets = ~edges+degree(1)+mean.age,
               target.stats=target.stats, estimate="EGMME",
               control=control.tergm(SA.plot.progress=TRUE))

par(ask=TRUE)
mcmc.diagnostics(dynfit)
summary(dynfit)

## End(Not run)

# CMLE Example
data(samplk)

# Fit a transition from Time 1 to Time 2
samplk12 <- tergm(list(samplk1, samplk2)~
                  Form(~edges+mutual+transitiveties+cyclicalties)+
                  Diss(~edges+mutual+transitiveties+cyclicalties),
                  estimate="CMLE")

mcmc.diagnostics(samplk12)
summary(samplk12)

samplk12.gof <- gof(samplk12)

samplk12.gof

plot(samplk12.gof)

plot(samplk12.gof, plotlogodds=TRUE)

# Fit a transition from Time 1 to Time 2 and from Time 2 to Time 3 jointly
samplk123 <- tergm(list(samplk1, samplk2, samplk3)~
                   Form(~edges+mutual+transitiveties+cyclicalties)+
                   Diss(~edges+mutual+transitiveties+cyclicalties),
                   estimate="CMLE")

mcmc.diagnostics(samplk123)
summary(samplk123)

tergm documentation built on Oct. 8, 2024, 5:09 p.m.