R/data.R

In remstimate: Optimization Frameworks for Tie-Oriented and Actor-Oriented Relational Event Models

#' @title Actor-Oriented Relational Event History
#'
#' @description A randomly generated sequence of relational events with 5 actors and 100 events. The event sequence is generated by following an actor-oriented modeling approach (for more information on the algorithm used for the generation, refer to \code{help(topic = remulateActor, package = "remulate")} or \code{?remulate::remulateActor}).
#'
#'
#' @name ao_data
#' @docType data
#'
#'
#' @format \code{ao_data} is a list object containing the following objects:
#'\describe{
#'    \item{\code{edgelist}}{a \code{data.frame} with the raw simulated edgelist. The columns of the \code{data.frame} are:
#'            \describe{
#'                \item{\code{time}}{the timestamp indicating the time at which each event occurred}
#'                \item{\code{actor1}}{the actor that generated the relational event}
#'                \item{\code{actor2}}{the actor that received the relational event}
#'            }}
#'    \item{\code{seed}}{the seed value used in \code{remulate::remulateActor()} for generating the event sequence}
#'    \item{\code{true.pars}}{a list of two vectors named \code{"rate_model"} and \code{"choice_model"}, each containing the values of the parameters used in the generation of the event sequence}
#'     }
#'
#' @usage 
#' 
#' data(ao_data)
#'
#' @examples
#' 
#' # (1) load the data into the workspace
#' data(ao_data)
#' 
#' # (2) process event sequence with \code{remify}
#' ao_reh <- remify::remify(edgelist = ao_data$edgelist, model = "actor")
#' 
#' # (3) define linear predictor and claculate stastistcs with \code{remstats} package
#' 
#' ## linear predictor for the rate model
#' rate_model <- ~ 1 + remstats::indegreeSender()
#' 
#' ## linear predictror for the choice model
#' choice_model <- ~ remstats::inertia() + remstats::reciprocity()
#' 
#' ## calculate statistics
#' ao_reh_stats <- remstats::remstats(reh = ao_reh, sender_effects = rate_model, 
#' receiver_effects = choice_model)
#' 
#' # (4) estimate model using method = "MLE" and print out summary
#' 
#' ## estimate model
#' mle_ao <- remstimate::remstimate(reh = ao_reh, stats = ao_reh_stats, method = "MLE")
#' 
#' ## print out a summary of the estimation
#' summary(mle_ao)
#'
"ao_data"

#' @title Tie-Oriented Relational Event History
#'
#' @description A randomly generated sequence of relational events with 5 actors and 100 events. The event sequence is generated by following a tie-oriented modeling approach (for more information run on console \code{help(topic = remulateTie, package = "remulate")} or \code{?remulate::remulateTie}).
#'
#'
#' @name tie_data
#' @docType data
#'
#'
#' @format \code{tie_data} is a list object containing the following objects:
#'\describe{
#'    \item{\code{edgelist}}{a \code{data.frame} with the raw simulated edgelist. The columns of the \code{data.frame} are:
#'            \describe{
#'                \item{\code{time}}{the timestamp indicating the time at which each event occurred}
#'                \item{\code{actor1}}{the actor that generated the relational event}
#'                \item{\code{actor2}}{the actor that received the relational event}
#'            }}
#'    \item{\code{seed}}{the seed value used in \code{remulate::remulateTie()} for generating the event sequence}
#'    \item{\code{true.pars}}{ a vector containing the values of the parameters used in the generation of the event sequence}
#'     }
#'
#' @usage 
#' 
#' data(tie_data)
#'
#' @examples
#' 
#' # (1) load the data into the workspace
#' data(tie_data)
#' 
#' # (2) process event sequence with \code{remify}
#' tie_reh <- remify::remify(edgelist = tie_data$edgelist, model = "tie")
#' 
#' # (3) define linear predictor and claculate stastistcs with \code{remstats} package
#' 
#' ## linear predictor
#' tie_model <- ~ 1 + remstats::indegreeSender() + remstats::inertia() + remstats::reciprocity() 
#'
#' ## calculate statistics
#' tie_reh_stats <- remstats::remstats(reh = tie_reh, tie_effects = tie_model)
#' 
#' # (4) estimate model using method = "MLE" and print out summary
#' 
#' ## estimate model 
#' mle_tie <- remstimate::remstimate(reh = tie_reh, stats = tie_reh_stats, method = "MLE")
#' 
#' ## print out a summary of the estimation
#' summary(mle_tie)
#'
"tie_data"