tergm {tergm}R Documentation

Temporal Exponential-Family Random Graph Models

Description

tergm is used for finding 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,
  ...
)

Arguments

formula

an ERGM formula.

constraints

A one-sided formula specifying one or more constraints on the support of the distribution of the networks being modeled, using syntax similar to the formula argument. Multiple constraints may be given, separated by “+” operators. Together with the model terms in the formula and the reference measure, the constraints define the distribution of networks being modeled.

The default is ~., for an unconstrained model.

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 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. 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. 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

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 lower-level functions.

Details

Model Terms See ergm and ergm-terms for details. At this time, only linear ERGM terms are allowed.

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. 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. 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). http://stat.psu.edu/research/technical-report-files/2012-technical-reports/modeling-of-dynamic-networks-based-on-egocentric-data-with-durational-information

See Also

ergm(), network(), %v%, %n%, ergm-terms

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)
[Package tergm version 4.0.2 Index]