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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/drm.R
\name{rpf.drm}
\alias{rpf.drm}
\title{Create a dichotomous response model}
\usage{
rpf.drm(factors = 1, multidimensional = TRUE, poor = FALSE)
}
\arguments{
\item{factors}{the number of factors}
\item{multidimensional}{whether to use a multidimensional model.
Defaults to \code{TRUE}.}
\item{poor}{if TRUE, use the traditional parameterization of
the 1d model instead of the slope-intercept parameterization}
}
\value{
an item model
}
\description{
For slope vector a, intercept c, pseudo-guessing parameter g,
upper bound u, and latent ability vector theta, the response probability
function is
\deqn{\mathrm P(\mathrm{pick}=0|a,c,g,u,\theta) = 1- \mathrm P(\mathrm{pick}=1|a,c,g,u,\theta)
}{P(pick=0|a,c,g,u,th) = 1-P(pick=1|a,c,g,u,th)}
\deqn{\mathrm P(\mathrm{pick}=1|a,c,g,u,\theta) = g+(u-g)\frac{1}{1+\exp(-(a\theta + c))}
}{P(pick=1|a,c,g,u,th) = g+(u-g)/(1+exp(-(a th + c)))}
}
\details{
The pseudo-guessing and upper bound parameter are specified in
logit units (see \code{\link{logit}}).
For discussion on the choice of priors see Cai, Yang, and
Hansen (2011, p. 246).
}
\examples{
spec <- rpf.drm()
rpf.prob(spec, rpf.rparam(spec), 0)
}
\references{
Cai, L., Yang, J. S., & Hansen, M. (2011). Generalized
Full-Information Item Bifactor Analysis. \emph{Psychological
Methods, 16}(3), 221-248.
}
\seealso{
Other response model:
\code{\link{rpf.gpcmp}()},
\code{\link{rpf.grmp}()},
\code{\link{rpf.grm}()},
\code{\link{rpf.lmp}()},
\code{\link{rpf.mcm}()},
\code{\link{rpf.nrm}()}
}
\concept{response model}
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