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\encoding{UTF-8}
\name{person.parameter}
\alias{person.parameter}
\alias{person.parameter.eRm}
\alias{summary.ppar}
\alias{print.ppar}
\alias{plot.ppar}
\alias{coef.ppar}
\alias{logLik.ppar}
\alias{print.logLik.ppar}
\alias{confint.ppar}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Estimation of Person Parameters}
\description{Maximum likelihood estimation of the person parameters with spline
interpolation for non-observed and 0/full responses. Extraction of information criteria such
as AIC, BIC, and cAIC based on unconditional log-likelihood.}
\usage{
\method{person.parameter}{eRm}(object)
\method{summary}{ppar}(object, ...)
\method{print}{ppar}(x, ...)
\method{plot}{ppar}(x, xlab = "Person Raw Scores",
ylab = "Person Parameters (Theta)", main = NULL, ...)
\method{coef}{ppar}(object, extrapolated = TRUE, ...)
\method{logLik}{ppar}(object, ...)
\method{confint}{ppar}(object, parm, level = 0.95, ...)
}
\arguments{
\item{object}{Object of class \code{'eRm'} in \code{person.parameter} and object of class \code{ppar} in \code{IC}.}
Arguments for \code{print} and \code{plot} methods:
\item{x}{Object of class \code{ppar}.}
\item{xlab}{Label of the x-axis.}
\item{ylab}{Label of the y-axis.}
\item{main}{Title of the plot.}
\item{...}{Further arguments to be passed to or from other methods. They are ignored in this function.}
Arguments for the \code{coef} method:
\item{extrapolated}{either returns extrapolated values for raw scores 0 and k or sets them \code{NA}}
Arguments for \code{confint}:
\item{parm}{Parameter specification (ignored).}
\item{level}{Alpha-level.}
}
\details{If the data set contains missing values, person parameters are estimated
for each missing value subgroup.
}
\value{
The function \code{person.parameter} returns an object of class \code{ppar} containing:
\item{loglik}{Log-likelihood of the collapsed data (for faster estimation persons with the same raw score are collapsed).}
\item{npar}{Number of parameters.}
\item{niter}{Number of iterations.}
\item{thetapar}{Person parameter estimates.}
\item{se.theta}{Standard errors of the person parameters.}
\item{hessian}{Hessian matrix.}
\item{theta.table}{Matrix with person parameters (ordered according to original data)
including NA pattern group.}
\item{pers.ex}{Indices with persons excluded due to 0/full raw score}
\item{X.ex}{Data matrix with persons excluded}
\item{gmemb}{NA group membership vector (0/full persons excluded)}
The function \code{coef} returns a vector of the person parameter estimates for each person (i.e., the first column
of \code{theta.table}).
The function \code{logLik} returns an object of class \code{loglik.ppar} containing:
\item{loglik}{Log-likelihood of the collapsed data (see above).}
\item{df}{Degrees of freedom.}
}
\references{
Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations,
Recent Developements, and Applications. Springer.
Mair, P., and Hatzinger, R. (2007). Extended Rasch modeling: The \pkg{eRm} package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1-20.
Mair, P., and Hatzinger, R. (2007). CML based estimation of extended Rasch models with the \pkg{eRm} package in R. Psychology Science, 49, 26-43.
}
\author{Patrick Mair, Reinhold Hatzinger}
%\note{}
\seealso{
\code{\link{itemfit.ppar}},\code{\link{personfit.ppar}}
}
\examples{
#Person parameter estimation of a rating scale model
res <- RSM(rsmdat)
pres <- person.parameter(res)
pres
summary(pres)
plot(pres)
#Person parameter estimation for a Rasch model with missing values
res <- RM(raschdat2, se = FALSE) #Rasch model without standard errors
pres <- person.parameter(res)
pres #person parameters
summary(pres)
logLik(pres) #log-likelihood of person parameter estimation
}
\keyword{models}
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