% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sim.data.FCGGUM.R
\name{sim.data.FCGGUM}
\alias{sim.data.FCGGUM}
\title{Simulate Data from the Forced-Choice GGUM Model}
\usage{
sim.data.FCGGUM(
  N.person = 1000,
  N.block = 10,
  I.block = 2,
  D = 3,
  fc.type = "RANK",
  control = NULL
)
}
\arguments{
\item{N.person}{Integer; number of persons (default: 1000).}

\item{N.block}{Integer; number of forced-choice blocks (default: 10).}

\item{I.block}{Integer; items per block (default: 2).}

\item{D}{Integer; number of latent dimensions (default: 3, must be >= 2).}

\item{fc.type}{Character; \code{"RANK"} (default), \code{"MOLE"}, or
\code{"PICK"}.}

\item{control}{Optional list with entries \code{Q.matrix},
\code{block.items}, and \code{Corr}.}
}
\value{
An object of class \code{"data.FCGGUM"} containing \code{data},
\code{response}, \code{theta}, \code{par}, \code{Q.matrix},
\code{block.items}, \code{Corr}, \code{patterns}, \code{patterns.total},
\code{prob}, \code{mask}, and data-generating arguments.
}
\description{
Generates forced-choice ranking data using the GGUM ideal-point model as the
item-level endorsement process. Within-block rankings follow the same
sequential Luce/Plackett mechanism as \code{\link{model.FCGGUM}}: the
utility for each statement is the logit of its binary GGUM endorsement
probability.
}
\examples{
set.seed(123)
sim <- sim.data.FCGGUM(N.person = 20, N.block = 3, I.block = 2,
                       D = 2, fc.type = "RANK")
str(sim$data)
head(sim$response)
dim(sim$prob)

}
\seealso{
\code{\link{fit.FCGGUM}}, \code{\link{model.FCGGUM}}
}
