R/data.R

In ergm.count: Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges

#  File R/data.R in package ergm.count, part of the
#  Statnet suite of packages for network analysis, https://statnet.org .
#
#  This software is distributed under the GPL-3 license.  It is free,
#  open source, and has the attribution requirements (GPL Section 7) at
#  https://statnet.org/attribution .
#
#  Copyright 2008-2022 Statnet Commons
################################################################################
#' Karate club social network of Zachary (1977)
#'
#'
#' Zachary (1977) reported observations of social relations in a university
#' karate club, with membership that varied between 50 and 100, of whom 34
#' individuals: 32 ordinary club members and officers, the club president
#' ("John A."), and the part-time instructor ("Mr. Hi"); consistently
#' interacted outside of the club. Over the course of the study, the club
#' divided into two factions, and, ultimately, split into two clubs, one led by
#' Hi and the other by John and the original club's officers. The split was
#' driven by a disagreement over whether Hi could unilaterally change the level
#' of compensation for his services.
#'
#' Zachary identifies the faction with which each of the 34 actors was aligned
#' and how strongly and reports, for each pair of actors, the count of social
#' contexts in which they interacted. The 8 contexts recorded were \itemize{
#' \item academic classes at the university; \item Hi's private karate studio
#' in his night classes; \item Hi's private karate studio where he taught on
#' weekends; \item student-teaching at Hi's studio; \item the university
#' rathskeller (bar) located near the karate club; \item a bar located near the
#' university campus; \item open karate tournaments in the area; and \item
#' intercollegiate karate tournaments. }
#'
#' The highest number of contexts of interaction for a pair of individuals that
#' was observed was 7.
#'
#'
#' @name zach
#' @docType data
#' @format The data are represented as a \code{\link{network}} object, with an
#' edge attribute \code{contexts}, giving the number of contexts of interaction
#' for that pair of actors. In addition, the following vertex attributes are
#' provided: \describe{ \item{list("club")}{the club in which the actor ended
#' up;}\item{:}{the club in which the actor ended up;}
#' \item{list("faction")}{faction alignment of the actor as recorded by
#' Zachary}\item{:}{faction alignment of the actor as recorded by Zachary}
#' \item{list("faction.id")}{faction alignment coded numerically, as \eqn{-2}
#' (strongly Mr. Hi's), \eqn{-1} (weakly Mr. Hi's), \eqn{0} (neutral), \eqn{+1}
#' (weakly John's), and \eqn{+2} (strongly John's);} \item{list("role")}{role
#' of the actor in the network (Instructor, Member, or President)} }
#' @references Zachary, WW (1977). An Information Flow Model for Conflict and
#' Fission in Small Groups. \emph{Journal of Anthropological Research}, 33(4),
#' 452-473.
#' @source Zachary, WW (1977). An Information Flow Model for Conflict and
#' Fission in Small Groups. \emph{Journal of Anthropological Research}, 33(4),
#' 452-473.
#'
#' Sociomatrix in machine-readable format was retrieved from
#' \url{http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm}.
#' @keywords datasets
#' @examples
#'
#' data(zach)
#'
#' oldpal <- palette()
#' palette(gray((1:8)/8))
#' plot(zach, vertex.col="role", displaylabels=TRUE, edge.col="contexts")
#' palette(oldpal)
#'
#' \donttest{
#' # Fit a binomial-reference ERGM.
#'
#' zach.fit1 <- ergm(zach~nonzero+sum+nodefactor("role",base=2)+absdiffcat("faction.id"),
#'                   response="contexts", reference=~Binomial(8))
#'
#' mcmc.diagnostics(zach.fit1)
#'
#' summary(zach.fit1)
#' }
#' \dontrun{
#' # This is much slower.
#' zach.fit2 <- ergm(zach~nonzero+sum+nodefactor("role",base=2)+transitiveties,
#'                   response="contexts", reference=~Binomial(8),
#'                   eval.loglik=FALSE)
#'
#' mcmc.diagnostics(zach.fit2)
#'
#' summary(zach.fit2)
#' }
#'
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