tergm (version 3.3.1)

tergm.godfather: A function to apply a given series of changes to a network.

Description

Gives the network a series of timed proposals it can't refuse. Returns the statistics of the network, and, optionally, the final network.

Usage

tergm.godfather(formula, changes = NULL, toggles = changes[, -4, drop = FALSE],
  start = NULL, end = NULL, end.network = FALSE, stats.start = FALSE, verbose = FALSE,
  control = control.tergm.godfather())

Arguments

formula

An summary.formula-style formula, with either a network or a networkDynamic as the LHS

changes

A matrix with four columns: time, tail, head, and new value, describing the changes to be made. Can only be used if LHS of formula is not a networkDynamic.

toggles

A matrix with three columns: time, tail, and head, giving the dyads which had changed. Can only be used if LHS of formula is not a networkDynamic.

start

Time from which to start applying changes. Note that the first set of changes will take effect at start+1. Defaults to the time point 1 before the earliest change passed.

end

Time from which to finish applying changes. Defaults to the last time point at which a change occurs.

end.network

Whether to return a network that results. Defaults to FALSE.

stats.start

Whether to return the network statistics at start (before any changes are applied) as the first row of the statistics matrix. Defaults to FALSE, to produce output similar to that of

verbose

Whether to print progress messages.

control

A control list generated by control.tergm.godfather. So far, there is only one argument: GF.init.maxedges.mul, to control how much space is allocated for the edgelis

Value

• If end.network==FALSE (the default), an mcmc object with the requested network statistics associed with the network series produced by applying the specified changes. Its mcmc attributes encode the timing information: so start(out) gives the time point associated with the first row returned, and end(out) out the last. The "thinning interval" is always 1.

  If end.network==TRUE, return a network object with lasttoggle "extension", representing the final network, with a matrix of statistics described in the previous paragraph attached to it as an attr-style attribute "stats".

See Also

simulate.stergm, simulate.network, simulate.networkDynamic

Examples

g1 <- network.initialize(10, dir=FALSE)
g1[1,2] <- 1
g1[3,4] <- 1
g1 %n% "time" <- 0
g1 %n% "lasttoggle" <- -1-rgeom(network.dyadcount(g1),1/4)

dc <- matrix(rnorm(100),10,10); dc <- dc+t(dc)

# Simulate a network, tracking its statistics.
simnet <- simulate(g1, formation=~edges, dissolution=~edges, coef.form=-1, coef.diss=1,
                   time.slices=50, monitor=~degree(1)+mean.age+degree.mean.age(1)+
                                            mean.age(log=TRUE)+degree.mean.age(1,log=TRUE)+
                                            degrange(1,3)+mean.age+degrange.mean.age(1,3)+
                                            mean.age(log=TRUE)+degrange.mean.age(1,3,log=TRUE)+
                                            edge.ages+edgecov(dc)+edgecov.ages(dc),
                   output="networkDynamic")

sim.stats <- attr(simnet, "stats")

print(head(sim.stats))
sim.stats <- as.matrix(sim.stats)

# Replay the simulation using a networkDynamic, monitoring a potentially different set of
# statistics (but same in this case).
gf1.stats <- tergm.godfather(simnet~degree(1)+mean.age+degree.mean.age(1)+
                                    mean.age(log=TRUE)+degree.mean.age(1,log=TRUE)+
                                    degrange(1,3)+mean.age+degrange.mean.age(1,3)+
                                    mean.age(log=TRUE)+degrange.mean.age(1,3,log=TRUE)+
                                    edge.ages+edgecov(dc)+edgecov.ages(dc),
                             start=0, end=50)

print(head(gf1.stats))
gf1.stats <- as.matrix(gf1.stats)

# Replay the simulation using the initial network + list of changes.

gf2.stats <- tergm.godfather(g1~degree(1)+mean.age+degree.mean.age(1)+
                                mean.age(log=TRUE)+degree.mean.age(1,log=TRUE)+
                                degrange(1,3)+mean.age+degrange.mean.age(1,3)+
                                mean.age(log=TRUE)+degrange.mean.age(1,3,log=TRUE)+
                                edge.ages+edgecov(dc)+edgecov.ages(dc),
                             start=0, end=50, changes=attr(simnet,"changes"))

print(head(gf2.stats))
gf2.stats <- as.matrix(gf2.stats)

# We can also compare them to the network statistics summarized.
summ.stats <- summary(simnet~degree(1)+mean.age+degree.mean.age(1)+
                             mean.age(log=TRUE)+degree.mean.age(1,log=TRUE)+
                             degrange(1,3)+mean.age+degrange.mean.age(1,3)+
                             mean.age(log=TRUE)+degrange.mean.age(1,3,log=TRUE)+
                             edge.ages+edgecov(dc)+edgecov.ages(dc), at=1:50)

print(head(summ.stats))

tol <- sqrt(.Machine$double.eps)
# If they aren't all identical, we are in trouble.
stopifnot(all.equal(sim.stats,gf1.stats),
          all.equal(sim.stats,gf2.stats),
          all.equal(sim.stats,summ.stats))