ergmHint: MCMC Hints for Exponential-Family Random Graph Models
In ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

ergmHint

R Documentation

MCMC Hints for Exponential-Family Random Graph Models

Description

This page describes how to provide to the ergm's MCMC algorithms information about the sample space. Hints can also be searched via search.ergmHints, and help for an individual hint can be obtained with ⁠ergmHint?<hint>⁠ or help("<hint>-ergmHint").

“Hints” for MCMC

\ERGMspec

It is often the case that there is additional information available about the distribution of networks being modelled. For example, you may be aware that the network is sparse or that there are strata among the dyads. “Hints”, typically passed on the right-hand side of MCMC.prop and obs.MCMC.prop arguments to control.ergm(), control.simulate.ergm(), and others, allow this information to be provided. By default, hint sparse is in effect.

Unlike constraints, model terms, and reference distributions, “hints” do not affect the specification of the model. That is, regardless of what “hints” may or may not be in effect, the sample space and the probabilities within it are the same. However, “hints” may affect the MCMC proposal distribution used by the samplers.

Note that not all proposals support all “hints”: and if the most suitable proposal available cannot incorporate a particular “hint”, a warning message will be printed.

“Hints” use the same underlying API as constraints, and, if present, %ergmlhs% attributes constraints and constraints.obs will be substituted in its place.

Hints available to the package

The following hints are known to ergm at this time:

\ergmCSS \Sexpr[results=rd,stage=render]{ergm:::.formatIndexHtml(ergm:::.buildTermsDataframe("ergmHint"))}

References

Goodreau SM, Handcock MS, Hunter DR, Butts CT, Morris M (2008a). A statnet Tutorial. Journal of Statistical Software, 24(8). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.i03")}
Karwa V, Krivitsky PN, and Slavkovi\'c AB (2016). Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models. Journal of the Royal Statistical Society, Series C, 66(3): 481-500. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssc.12185")}
Krivitsky PN (2012). Exponential-Family Random Graph Models for Valued Networks. Electronic Journal of Statistics, 6, 1100-1128. \Sexpr[results=rd]{tools:::Rd_expr_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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.i04")}