Dynamic Network Model Diagnostics

Runs dynamic diagnostics on an ERGM/STERGM estimated with netest.

Usage

netdx(
  x,
  nsims = 1,
  dynamic = TRUE,
  nsteps,
  nwstats.formula = "formation",
  set.control.ergm = control.simulate.formula(),
  set.control.tergm = control.simulate.formula.tergm(MCMC.maxchanges = Inf),
  sequential = TRUE,
  keep.tedgelist = FALSE,
  keep.tnetwork = FALSE,
  verbose = TRUE,
  ncores = 1,
  skip.dissolution = FALSE
)

Arguments

x: An EpiModel object of class netest.
nsims: Number of simulations to run.
dynamic: If TRUE, runs dynamic diagnostics. If FALSE and the netest object was fit with the Edges Dissolution approximation method, simulates from the static ERGM fit.
nsteps: Number of time steps per simulation (dynamic simulations only).
nwstats.formula: A right-hand sided ERGM formula with the network statistics of interest. The default is the formation formula of the network model contained in x.
set.control.ergm: Control arguments passed to ergm's simulate_formula.network (see details).
set.control.tergm: Control arguments passed to tergm's simulate_formula.network (see details).
sequential: For static diagnostics (dynamic=FALSE): if FALSE, each of the nsims simulated Markov chains begins at the initial network; if TRUE, the end of one simulation is used as the start of the next.
keep.tedgelist: If TRUE, keep the timed edgelist generated from the dynamic simulations. Returned in the form of a list of matrices, with one entry per simulation. Accessible at $edgelist.
keep.tnetwork: If TRUE, keep the full networkDynamic objects from the dynamic simulations. Returned in the form of a list of nD objects, with one entry per simulation. Accessible at $network.
verbose: If TRUE, print progress to the console.
ncores: Number of processor cores to run multiple simulations on, using the foreach and doParallel implementations.
skip.dissolution: If TRUE, skip over the calculations of duration and dissolution stats in netdx.

Value

A list of class netdx.

Details

The netdx function handles dynamic network diagnostics for network models fit with the netest function. Given the fitted model, netdx simulates a specified number of dynamic networks for a specified number of time steps per simulation. The network statistics in nwstats.formula are saved for each time step. Summary statistics for the formation model terms, as well as dissolution model and relational duration statistics, are then calculated and can be accessed when printing or plotting the netdx object. See print.netdx and plot.netdx for details on printing and plotting.

Control Arguments

Models fit with the full STERGM method in netest (setting the edapprox argument to FALSE) require only a call to tergm's simulate_formula.network. Control parameters for those simulations may be set using set.control.tergm in netdx. The parameters should be input through the control.simulate.formula.tergm function, with the available parameters listed in the tergm::control.simulate.formula.tergm help page in the tergm package.

Models fit with the ERGM method with the edges dissolution approximation (setting edapprox to TRUE) require a call first to ergm's simulate_formula.network for simulating an initial network, and second to tergm's simulate_formula.network for simulating that static network forward through time. Control parameters may be set for both processes in netdx. For the first, the parameters should be input through the control.simulate.formula() function, with the available parameters listed in the ergm::control.simulate.formula help page in the ergm package. For the second, parameters should be input through the control.simulate.formula.tergm() function, with the available parameters listed in the tergm::control.simulate.formula.tergm help page in the tergm package. An example is shown below.

Examples

if (FALSE) { # \dontrun{
# Network initialization and model parameterization
nw <- network_initialize(n = 100)
formation <- ~edges
target.stats <- 50
coef.diss <- dissolution_coefs(dissolution = ~ offset(edges), duration = 25)

# Estimate the model
est <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)

# Static diagnostics on the ERGM fit
dx1 <- netdx(est,
  nsims = 1e4, dynamic = FALSE,
  nwstats.formula = ~ edges + meandeg + concurrent
)
dx1
plot(dx1, method = "b", stats = c("edges", "concurrent"))

# Dynamic diagnostics on the STERGM approximation
dx2 <- netdx(est,
  nsims = 5, nsteps = 500,
  nwstats.formula = ~ edges + meandeg + concurrent,
  set.control.ergm = control.simulate.formula(MCMC.burnin = 1e6)
)
dx2
plot(dx2, stats = c("edges", "meandeg"), plots.joined = FALSE)
plot(dx2, type = "duration")
plot(dx2, type = "dissolution", qnts.col = "orange2")
plot(dx2, type = "dissolution", method = "b", col = "bisque")

# Dynamic diagnostics on a more complex model
nw <- network_initialize(n = 1000)
nw <- set_vertex_attribute(nw, "neighborhood", rep(1:10, 100))
formation <- ~edges + nodematch("neighborhood", diff = TRUE)
target.stats <- c(800, 45, 81, 24, 16, 32, 19, 42, 21, 24, 31)
coef.diss <- dissolution_coefs(dissolution = ~offset(edges) +
                    offset(nodematch("neighborhood", diff = TRUE)),
                    duration = c(52, 58, 61, 55, 81, 62, 52, 64, 52, 68, 58))
est2 <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)
dx3 <- netdx(est2, nsims = 5, nsteps = 100)
print(dx3)
plot(dx3)
plot(dx3, type = "duration", plots.joined = TRUE, qnts = 0.2, legend = TRUE)
plot(dx3, type = "dissolution", mean.smooth = FALSE, mean.col = "red")
} # }