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Estimates statistical network models using the exponential random graph modeling (ERGM) framework with extensions for dynamic/temporal models (STERGM).
netest(
nw,
formation,
target.stats,
coef.diss,
constraints,
coef.form = NULL,
edapprox = TRUE,
set.control.ergm,
set.control.stergm,
verbose = FALSE
)The edges dissolution approximation method is described in Carnegie et al. This approximation requires that the dissolution coefficients are known, that the formation model is being fit to cross-sectional data conditional on those dissolution coefficients, and that the terms in the dissolution model are a subset of those in the formation model. Under certain additional conditions, the formation coefficients of a STERGM model are approximately equal to the coefficients of that same model fit to the observed cross-sectional data as an ERGM, minus the corresponding coefficients in the dissolution model. The approximation thus estimates this ERGM (which is typically much faster than estimating a STERGM) and subtracts the dissolution coefficients.
The conditions under which this approximation best hold are when there are
few relational changes from one time step to another; i.e. when either average
relational durations are long, or density is low, or both. Conveniently,
these are the same conditions under which STERGM estimation is slowest. Note
that the same approximation is also used to obtain starting values for the
STERGM estimate when the latter is being conducted. The estimation does not
allow for calculation of standard errors, p-values, or likelihood for the
formation model; thus, this approach is of most use when the main goal of
estimation is to drive dynamic network simulations rather than to conduct
inference on the formation model. The user is strongly encouraged to examine
the behavior of the resulting simulations to confirm that the approximation
is adequate for their purposes. For an example, see the vignette for the
package tergm.
The ergm and stergm functions allow control settings for the
model fitting process. When fitting a STERGM directly (setting edapprox
to FALSE), control parameters may be passed to the stergm
function with the set.control.stergm argument in netest. The
controls should be input through the control.stergm() function, with
the available parameters listed in the control.stergm help page
in the tergm package.
When fitting a STERGM indirectly (setting edapprox to TRUE),
control settings may be passed to the ergm function using
set.control.ergm in netest. The controls should be input through
the control.ergm() function, with the available parameters listed in the
control.ergm help page in the ergm
package. An example is below.
netest is a wrapper function for the ergm and stergm
functions that estimate static and dynamic network models, respectively.
Network model estimation is the first step in simulating a stochastic network
epidemic model in EpiModel. The output from netest is a
necessary input for running the epidemic simulations in netsim.
With a fitted network model, one should always first proceed to model
diagnostics, available through the netdx function, to check
model fit. A detailed description of fitting these models, along with examples,
may be found in the
Basic Network Models
tutorial.
Krivitsky PN, Handcock MS. "A separable model for dynamic networks." JRSS(B). 2014; 76.1:29-46.
Carnegie NB, Krivitsky PN, Hunter DR, Goodreau SM. An approximation method for improving dynamic network model fitting. Journal of Computational and Graphical Statistics. 2014; 24(2): 502-519.
Jenness SM, Goodreau SM and Morris M. EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks. Journal of Statistical Software. 2018; 84(8): 1-47.
Use netdx to diagnose the fitted network model, and
netsim to simulate epidemic spread over a simulated
dynamic network consistent with the model fit.
# NOT RUN {
# Initialize a network of 100 nodes
nw <- network_initialize(n = 100)
# Set formation formula
formation <- ~edges + concurrent
# Set target statistics for formation
target.stats <- c(50, 25)
# Obtain the offset coefficients
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 10)
# Estimate the STERGM using the edges dissolution approximation
est <- netest(nw, formation, target.stats, coef.diss,
set.control.ergm = control.ergm(MCMC.burnin = 1e5,
MCMC.interval = 1000))
est
# To estimate the STERGM directly, use edapprox = FALSE
# est2 <- netest(nw, formation, target.stats, coef.diss, edapprox = FALSE)
# }
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