Description Usage Arguments Details Value Methods (by generic) References See Also Examples
stergm
is used for finding Separable Temporal ERGMs' (STERGMs)
Conditional MLE (CMLE) (Krivitsky and Handcock, 2010) and Equilibrium
Generalized Method of Moments Estimator (EGMME) (Krivitsky, 2009).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## S3 method for class 'stergm'
coef(object, ...)
## S3 method for class 'stergm'
coefficients(object, ...)
## S3 method for class 'stergm'
print(x, digits = max(3, getOption("digits")  3), ...)
stergm(nw, formation, dissolution, constraints = ~., estimate,
times = NULL, offset.coef.form = NULL, offset.coef.diss = NULL,
targets = NULL, target.stats = NULL, eval.loglik = TRUE,
control = control.stergm(), verbose = FALSE, ...)
## S3 method for class 'stergm'
summary(object, ...)

object 
A 
... 
Additional arguments, to be passed to lowerlevel functions. 
x 
A 
digits 
Significant digits for coefficients 
nw 
A

formation, dissolution 
Onesided 
constraints 
A onesided formula specifying one or more constraints on
the support of the distribution of the networks being modeled, using syntax
similar to the It is also possible to specify a proposal function directly by passing a
string with the function's name. In that case, arguments to the proposal
should be specified through the The default is See the ERGM constraints documentation for the constraints implemented in the ergm package. Other packages may add their own constraints. For STERGMs in particular, the constraints apply to the postformation and the postdissolution network, rather than the final network. This means, for example, that if the degree of all vertices is constrained to be less than or equal to three, and a vertex begins a time step with three edges, then, even if one of its edges is dissolved during its time step, it won't be able to form another edge until the next time step. This behavior may change in the future. Note that not all possible combinations of constraints are supported. 
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 
logical or integer; if TRUE or positive, the program will print out progress information. Higher values result in more output. 
Model Terms See ergm
and ergmterms
for
details. At this time, only linear ERGM terms are allowed.
For a brief demonstration, please see the tergm package vignette:
browseVignettes(package='tergm')
A more detailed tutorial is avalible on the statnet wiki: http://statnet.csde.washington.edu/workshops/SUNBELT/current/tergm/tergm_tutorial.pdf
For more usage examples, see the wiki page at https://statnet.csde.washington.edu/trac/wiki/tergmUsage
coef
and coefficients
methods return parameter
estimates extracted from object
in the form of a list with
two elements: formation
, a vector of formation
coefficients and dissolution
, a vector of dissolution
coefficients.
stergm
returns an object of class stergm
that is a list consisting of the following elements:
formation, dissolution 
Formation and dissolution formulas, respectively. 
targets 
The targets formula. 
target.stats 
The target statistics. 
estimate 
The type of estimate. 
opt.history 
A matrix containing the full trace of the EGMME optimization process: coefficients tried and target statistics simulated. 
sample 
An 
covar 
The full estimated variancecovariance matrix of the parameter estimates for EGMME. (Note that although the CMLE formation parameter estimates are independent of the dissolution parameter estimates due to the separability assumption, this is not necessarily the case for EGMME.) 
formation.fit, dissolution.fit 
For CMLE and CMPLE, 
network 
For

control 
The control parameters used to fit the model. 
See the method print.stergm
for details on how an
stergm
object is printed. Note that the method
summary.stergm
returns a summary of the relevant parts of the
stergm
object in concise summary format.
coef
: Extract parameter estimates.
coefficients
: An alias for the coef
method.
print
: Print the parameter estimates.
summary
: Print the summary of the formation and the
dissolution model fits.
Krivitsky PN, Handcock MS (2010). A Separable Model for Dynamic Networks. http://arxiv.org/abs/1011.1937
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). http://stat.psu.edu/research/technicalreportfiles/2012technicalreports/modelingofdynamicnetworksbasedonegocentricdatawithdurationalinformation
ergm, network, %v%, %n%, ergmterms
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  # 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<stergm(g0,formation = ~edges+degree(1), dissolution = ~edges,
targets = ~edges+degree(1)+mean.age,
target.stats=target.stats, estimate="EGMME",
control=control.stergm(SA.plot.progress=TRUE))
par(ask=TRUE)
mcmc.diagnostics(dynfit)
summary(dynfit)
# CMLE Example
data(samplk)
# Fit a transition from Time 1 to Time 2
samplk12 < stergm(list(samplk1, samplk2),
formation=~edges+mutual+transitiveties+cyclicalties,
dissolution=~edges+mutual+transitiveties+cyclicalties,
estimate="CMLE")
mcmc.diagnostics(samplk12)
summary(samplk12)
# Fit a transition from Time 1 to Time 2 and from Time 2 to Time 3 jointly
samplk123 < stergm(list(samplk1, samplk2, samplk3),
formation=~edges+mutual+transitiveties+cyclicalties,
dissolution=~edges+mutual+transitiveties+cyclicalties,
estimate="CMLE")
mcmc.diagnostics(samplk123)
summary(samplk123)

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