Trims formula environments from the netest object.
Optionally converts the newnetwork element of the
netest object to a networkLite class, and removes
the fit element (if present) from the netest
object.
Usage
trim_netest(
object,
as.networkLite = TRUE,
keep.fit = FALSE,
keep = character(0)
)Value
A netest object with formula environments trimmed, optionally with the
newnetwork element converted to a networkLite and the
fit element removed.
Details
With larger, more complex network structures with epidemic models, it is
generally useful to reduce the memory footprint of the fitted TERGM model
object (estimated with netest). This utility function removes
all but the bare essentials needed for simulating a network model with
netsim.
The function always trims the environments of object$constraints and
object$coef.diss$dissolution.
When both edapprox = TRUE and nested.edapprox = TRUE in the
netest call, also trims the environments of object$formula
and object$formation.
When both edapprox = TRUE and nested.edapprox = FALSE in the
netest call, also trims the environments of object$formula,
environment(object$formation)$formation, and
environment(object$formation)$dissolution.
When edapprox = FALSE in the netest call, also trims the
environments of object$formation,
environment(object$formula)$formation and
environment(object$formula)$dissolution.
By default all objects are removed from these trimmed environments. Specific
objects may be retained by passing their names as the keep argument.
For the output of trim_netest to be usable in netsim
simulation, any objects referenced in the formulas should be included in the
keep argument.
If as.networkLite = TRUE, converts object$newnetwork to a
networkLite object. If keep.fit = FALSE, removes fit (if
present) from object.
Examples
nw <- network_initialize(n = 100)
formation <- ~edges + concurrent
target.stats <- c(50, 25)
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 10)
est <- netest(nw, formation, target.stats, coef.diss,
set.control.ergm = control.ergm(MCMC.burnin = 1e5,
MCMC.interval = 1000))
#> Starting maximum pseudolikelihood estimation (MPLE):
#> Obtaining the responsible dyads.
#> Evaluating the predictor and response matrix.
#> Maximizing the pseudolikelihood.
#> Finished MPLE.
#> Starting Monte Carlo maximum likelihood estimation (MCMLE):
#> Iteration 1 of at most 60:
#> 1
#> Optimizing with step length 1.0000.
#> The log-likelihood improved by 0.7827.
#> Estimating equations are not within tolerance region.
#> Iteration 2 of at most 60:
#> 1
#> Optimizing with step length 1.0000.
#> The log-likelihood improved by 0.1112.
#> Estimating equations are not within tolerance region.
#> Iteration 3 of at most 60:
#> 1
#> Optimizing with step length 1.0000.
#> The log-likelihood improved by 0.0299.
#> Convergence test p-value: < 0.0001.
#> Converged with 99% confidence.
#> Finished MCMLE.
#> This model was fit using MCMC. To examine model diagnostics and check
#> for degeneracy, use the mcmc.diagnostics() function.
print(object.size(est), units = "KB")
#> 413.9 Kb
est.small <- trim_netest(est)
print(object.size(est.small), units = "KB")
#> 57.6 Kb