Description Usage Arguments Details Value 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 17  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 onesided formula specifying one or more constraints on
the support of the distribution of the networks being modeled, using syntax
similar to the The default is See the ERGM constraints documentation for the constraints implemented in the ergm package. Other packages may add their own constraints. 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. 
... 
Additional arguments, to be passed to lowerlevel functions. 
SAN.offsets 
Offset coefficients (if any) to use during the SAN run. 
This function is included for backwards compatibility, and users are
encouraged to use the new tergm
family of functions instead.
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.
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 available on the statnet wiki: https://statnet.org/Workshops/tergm/tergm_tutorial.html
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. 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, \
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 38 39 40 41  ## 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<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)
## End(Not run)
# 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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