tergm uses a Metropolis-Hastings (MH) algorithm provided by
ergm to control the behavior of the Markov Chain Monte Carlo
(MCMC) for sampling networks. The MCMC chain is intended to step around the
sample space of possible networks, selecting a network at regular intervals
to evaluate the statistics in the model. For each MCMC step, n
(n=1 in the simple case) toggles are proposed to change the dyad(s)
to the opposite value. The probability of accepting the proposed change is
determined by the MH acceptance ratio. The role of the different MH methods
tergm is to vary how the sets of dyads are
selected for toggle proposals. This is used in some cases to improve the
performance (speed and mixing) of the algorithm, and in other cases to
constrain the sample space.
The user may specify proposals directly if need be, but is encouraged to utilize the hints and constraints UI instead.
A version of
TNT appropriate for CMLE fitting,
with proposals stratified both by discordance status and edge status. The argument
ref specifies the data relative to which discordance status is defined.
A temporal version of
TNT, with approximately
discordance_fraction of proposed toggles being made on the set of discordant dyads,
1 - discordance_fraction of proposed toggles being TNT proposals from
the network. The value of
discordance_fraction can be set by the user as a proposal argument,
and defaults to
A temporal version of
BDStratTNT. Within each
strat mixing type, approximately 50% of proposed toggles are made on
discordant dyads, and approximately 50% of proposed toggles are
TNT proposals from the network, all subject to the bounded degree
and mixing type constraints. The degree bound constraint is imposed
on the instantaneous network state
(rather than the temporal operator networks).
Proposal arguments are the same as for
and should be passed in via the
blocks constraints and
Goodreau SM, Handcock MS, Hunter DR, Butts CT, Morris M (2008a). A statnet Tutorial. Journal of Statistical Software, 24(8). https://www.jstatsoft.org/v24/i08/.
Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics.
Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008b). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software, 24(3). https://www.jstatsoft.org/v24/i03/.
Krivitsky PN (2012). Exponential-Family Random Graph Models for Valued Networks. Electronic Journal of Statistics, 2012, 6, 1100-1128. doi: 10.1214/12-EJS696
Morris M, Handcock MS, Hunter DR (2008). Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects. Journal of Statistical Software, 24(4). https://www.jstatsoft.org/v24/i04/.
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