Added Swiss co-author data

DESCRIPTION

@@ -1,9 +1,9 @@
 Package: btergm
-Version: 1.10.9
-Date: 2023-04-14
+Version: 1.10.11
+Date: 2023-10-05
 Title: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
 Authors@R: c(person(given = "Philip", family = "Leifeld", email = "philip.leifeld@essex.ac.uk", role = c("aut", "cre")), person(given = c("Skyler", "J."), family = "Cranmer", email = "cranmer.12@osu.edu", role = "ctb"), person(given = c("Bruce", "A."), family = "Desmarais", email = "bdesmarais@psu.edu", role = "ctb"))
-Description: Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>. If the suggested speedglm cannot be found on CRAN, you can find an old version in the GitHub mirror of CRAN at <https://github.com/cran/speedglm>.
+Description: Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.
 URL: https://github.com/leifeld/btergm
 Encoding: UTF-8
 Imports: 

R/ch_coauthor.R

@@ -0,0 +1,216 @@
+#' Swiss political science co-authorship network 2013
+#'
+#' Swiss political science co-authorship network 2013
+#'
+#' The Swiss political science co-authorship network 2013 dataset contains the
+#' co-authorship network of all political scientists at Swiss universities and
+#' research institutes in late 2013. The data are described in Leifeld and
+#' Ingold (2016) and Leifeld (2018). The data contained here include
+#' postdoctoral and professorial researchers but not PhD students, as in Leifeld
+#' (2018), without the PhD researchers included in Leifeld and Ingold (2016).
+#' For the full dataset, see the replication archive at DOI
+#' \doi{10.7910/DVN/85SK1M}.
+#'
+#' Leifeld and Ingold (2016) summarize the data collection strategy as follows:
+#' \emph{"Data gathering took place between July and December 2013. A single
+#' coder pursued a three-step coding procedure: he first created a list of all
+#' relevant university departments and research institutes that host political
+#' scientists in Switzerland, then he browsed the websites of these institutes
+#' and entered all researchers along with several details about them into a
+#' database, including their seniority status (predoctoral, postdoctoral, or
+#' professor) and the URL of their publication list (either the CV, the
+#' institutional website, a private homepage, or several of those items in order
+#' to get a complete publication profile of each person). After entering all
+#' researchers of an institute, the coder went through the researchers'
+#' publication lists and entered the following pieces of information for each
+#' publication into the database: the reporting author, the names of all
+#' co-authors, the title of the publication, the year, the name of the journal
+#' or book in which the publication appeared (if applicable), the names of all
+#' editors (if applicable), and a classification of the type of publication
+#' (academic journal, book chapter, monograph, edited volume, other). Most
+#' publications are relatively recent, but the earliest publications in the
+#' database date back to the 1960s. After completing these three steps, data
+#' entered at the beginning was double-checked in order to avoid bias due to new
+#' publications that may have shown up during the coding time period. This
+#' procedure is the best one can do in terms of completeness, but it should be
+#' clear that it crucially depends on the accuracy of the self-reported
+#' bibliographic information. For example, if a researcher did not update his or
+#' her CV or list of publications for the previous six months, those most recent
+#' publications only had a chance to enter the database if the co-authors listed
+#' the publication on their website. In some relatively rare cases, all authors
+#' failed to report recent updates, and this may cause minor inaccuracies in the
+#' network dataset, mostly affecting very recent publications in 2013 because
+#' there is, on average, a reporting lag."}
+#'
+#' Based on the collected publication data, a co-authorship network matrix with
+#' 156 nodes was created. In addition to this matrix, the dataset here contains
+#' node attribute data (institutional affiliations, location, demographics,
+#' language shares, publication type shares) and relational covariates
+#' (geographical distance, similarity in terms of the share of English articles,
+#' and topic similarity) as described in Leifeld (2018). The dataset can be used
+#' to replicate Leifeld (2018), but only approximately due to changes in the
+#' estimation routine in the \pkg{ergm} package since the article was published.
+#'
+#' @name coauthor
+#'
+#' @aliases ch_coauthor ch_coaut ch_dist100km ch_en_article_sim ch_nodeattr
+#'   ch_topicsim
+#'
+#' @docType data
+#'
+#' @format
+#' \describe{
+#' \item{\code{ch_coaut}}{is an undirected, weighted 156 x 156 adjancency matrix
+#' indicating how many publications political scientist in Switzerland shared
+#' with each other as reported in late 2013, including only postdoctoral and
+#' professorial political scientists affiliated with research institutes or
+#' universities. The exact edge weight should be treated with caution because
+#' some publications were counted multiple times because they were reported by
+#' multiple co-authors. The diagonal contains the number of publications of
+#' the respective author. Leifeld and Ingold (2016) describe the data collection
+#' process in more detail.}
+#'
+#' \item{\code{ch_nodeattr}}{is a data frame with node attributes/variables for
+#' the 156 researchers, in the same alphabetical row order as the network
+#' matrix. The first twelve columns with column labels starting with "inst_" are
+#' affiliations with different institutions (1 = affiliated; 0 = no
+#' affiliation). The next seven columns with column labels starting with "city_"
+#' contain the locations of the researchers' institutional affiliations. The
+#' "phdyear" column contains the self-reported year of obtaining the PhD, and
+#' the "birthyear" column contains the self-reported or publicly available year
+#' of birth; these two variables contain many missing values. The "status"
+#' column indicates whether a researcher was listed as a professor or as having
+#' postdoctoral or other non-professorial status at the time. "chairtitle" is
+#' the name of the chair or research group the researcher reported to be a
+#' member of. "num_publications" is the total number of publications,
+#' "num_articles" the number of journal articles among them, "num_books" the
+#' number of books among them, "share_articles" the percentage of journal
+#' articles among the publications, and "share_books" the percentage of
+#' monographs and edited volumes among the publications. The four columns with
+#' column names starting with "lang_" contain the relative shares of English,
+#' French, German, Italian, and other languages among the publications of the
+#' researcher. The column "share_en_articles" contains the percentage of English
+#' journal articles among all publications of the researcher. "male" is a dummy
+#' variable indicating whether the author is male (1) or female (0). The
+#' variables contained here are described in Leifeld (2018).}
+#'
+#' \item{\code{ch_dist100km}}{is a 156 x 156 matrix containing the geographical
+#' distance between any two researchers measured in units of 100km (for a
+#' reasonable scaling of coefficients in a statistical model), computed over the
+#' latitude and longitude of their main institutional affiliations. The measure
+#' is included in Leifeld (2018).}
+#'
+#' \item{\code{ch_en_article_sim}}{is a 156 x 156 matrix containing the
+#' similarity between any two researchers in terms of the share of their work
+#' that is published in English and as journal articles. Values closer to 1.0
+#' indicate that two researchers were similar in their language and publication
+#' type portfolio while values closer to 0 indicate that they were relatively
+#' dissimilar. Only extra-dyadic publications were counted in establishing this
+#' similarity. I.e., if researcher A and B co-authored, their joint publications
+#' were not included in establishing their English article share similarity.
+#' This was done to reduce endogeneity/reverse causality when modeling
+#' co-authorship as a function of English article share similarity. The measure
+#' is described in Leifeld (2018).}
+#'
+#' \item{\code{ch_topicsim}}{is a 156 x 156 topic similarity matrix for the
+#' researchers. Topic similarities were computed by taking into account all
+#' words in the publication titles of any two researchers, excluding the
+#' publications they published as co-authors (i.e., only extra-dyadic
+#' publications, to reduce endogeneity/reverse causality in modeling
+#' co-authorship ties as a function of topic similarity). Topic similarity was
+#' established by computing the cosine similarity between the tf-idf scores for
+#' the title words of any two researchers (i.e., a vector space model). Leifeld
+#' (2018) contains more details on this procedure.}
+#' }
+#'
+#' @references
+#' Leifeld, Philip (2018): Polarization in the Social Sciences: Assortative
+#' Mixing in Social Science Collaboration Networks is Resilient to
+#' Interventions. \emph{Physica A: Statistical Mechanics and its Applications}
+#' 507: 510--523. \doi{10.1016/j.physa.2018.05.109}. Full replication data:
+#' \doi{10.7910/DVN/85SK1M}.
+#'
+#' Leifeld, Philip and Karin Ingold (2016): Co-authorship Networks in Swiss
+#' Political Research. \emph{Swiss Political Science Review} 22(2): 264--287.
+#' \doi{10.1111/spsr.12193}.
+#'
+#' @source The data were collected from public information online. The full
+#' data collection details are described in Leifeld and Ingold (2016).
+#'
+#' @keywords datasets
+#'
+#' @examples
+#' \dontrun{
+#' # Replication code for the full Swiss co-authorship ERGM in Leifeld (2018).
+#' # Note that the estimates can only be reproduced approximately due to
+#' # internal changes in the ergm package.
+#'
+#' library("network")
+#' library("ergm")
+#'
+#' data("ch_coauthor")
+#'
+#' # set up network object with node attributes
+#' ch_nw <- network(ch_coaut, directed = FALSE)
+#' set.vertex.attribute(ch_nw, "frequency", ch_nodeattr$num_publications)
+#' set.vertex.attribute(ch_nw, "status", as.character(ch_nodeattr$status))
+#' set.vertex.attribute(ch_nw, "male", ch_nodeattr$male)
+#' set.vertex.attribute(ch_nw, "share_en_articles",
+#'                      ch_nodeattr$share_en_articles)
+#'
+#' # create same affiliation matrix
+#' ch_inst_indices <- which(grepl("^inst_.+", colnames(ch_nodeattr)))
+#' ch_same_affiliation <- as.matrix(ch_nodeattr[, ch_inst_indices]) %*%
+#'   t(ch_nodeattr[, ch_inst_indices])
+#'
+#' # create same chair matrix
+#' ch_nodeattr$chairtitle[ch_nodeattr$chairtitle == ""] <- NA
+#' ch_same_chair <- matrix(0, nrow = nrow(ch_same_affiliation),
+#'                         ncol = ncol(ch_same_affiliation))
+#' for (i in 1:length(ch_nodeattr$chairtitle)) {
+#'   for (j in 1:length(ch_nodeattr$chairtitle)) {
+#'     if (i != j &&
+#'         !is.na(ch_nodeattr$chairtitle[i]) &&
+#'         !is.na(ch_nodeattr$chairtitle[j]) &&
+#'         ch_nodeattr$chairtitle[i] == ch_nodeattr$chairtitle[j] &&
+#'         ch_same_affiliation[i, j] == TRUE) {
+#'       ch_same_chair[i, j] <- 1
+#'     }
+#'   }
+#' }
+#' rownames(ch_same_chair) <- rownames(ch_same_affiliation)
+#' colnames(ch_same_chair) <- colnames(ch_same_affiliation)
+#'
+#' # create supervision matrix (same chair + affiliation + mixed seniority)
+#' ch_supervision <- ch_same_affiliation *
+#'   ch_same_chair *
+#'   matrix(ch_nodeattr$status == "professor",
+#'          nrow = nrow(ch_same_chair),
+#'          ncol = ncol(ch_same_chair),
+#'          byrow = FALSE) *
+#'   matrix(ch_nodeattr$status != "professor",
+#'          nrow = nrow(ch_same_chair),
+#'          ncol = ncol(ch_same_chair),
+#'          byrow = TRUE)
+#'
+#' # ERGM estimation
+#' ch_model <- ergm(ch_nw ~
+#'                    edges +
+#'                    gwesp(0.3, fixed = TRUE) +
+#'                    gwdegree(0.4, fixed = TRUE) +
+#'                    nodecov("frequency") +
+#'                    nodefactor("status") +
+#'                    nodefactor("male") +
+#'                    nodematch("male") +
+#'                    edgecov(ch_dist100km) +
+#'                    edgecov(ch_same_affiliation) +
+#'                    edgecov(ch_same_chair) +
+#'                    edgecov(ch_supervision) +
+#'                    edgecov(ch_topicsim) +
+#'                    nodecov("share_en_articles") +
+#'                    edgecov(ch_en_article_sim),
+#'                  control = control.ergm(MCMLE.termination = "Hummel",
+#'                                         MCMLE.effectiveSize = NULL))
+#' summary(ch_model)  # corresponds Column 1 in Table 3 in Leifeld (2018)
+#' }
+NULL