stergm  R Documentation 
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.
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
)
nw 
A

formation, dissolution 
Onesided 
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
The default is to have no constraints except those provided through
the 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
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 
offset.coef.form 
Numeric vector to specify offset formation parameters. 
offset.coef.diss 
Numeric vector to specify offset dissolution parameters. 
targets 
Onesided 
target.stats 
A vector specifying the values of the 
eval.loglik 
Whether or not to calculate the loglikelihood
of a CMLE STERGM fit. See 
control 
A list of control parameters for algorithm tuning.
Constructed using 
verbose 
A logical or an integer to control the amount of
progress and diagnostic information to be printed. 
... 
Additional arguments, to be passed to lowerlevel functions. 
SAN.offsets 
Offset coefficients (if any) to use during the SAN run. 
The stergm
function uses a pair of formulas, formation
and
dissolution
to model tiedynamics. 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.
stergm
returns an object of class tergm
;
see tergm()
for details and methods.
Krivitsky P.N. and Handcock M.S. (2014) A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society, Series B, 76(1): 2946. \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(201201). https://web.archive.org/web/20170830053722/https://stat.psu.edu/research/technicalreportfiles/2012technicalreports/TR1201A.pdf
ergm, network, \
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