%% File Name: IRT.mle.Rd
%% File Version: 0.20

\name{IRT.mle}
\alias{IRT.mle}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Person Parameter Estimation
}
\description{
Computes the maximum likelihood estimate (MLE),
weighted likelihood estimate (WLE) and maximum aposterior
estimate (MAP) of ability in unidimensional item response models
(Penfield & Bergeron, 2005; Warm, 1989). Item response functions can be
defined by the user.
}

\usage{
IRT.mle(data, irffct, arg.list, theta=rep(0,nrow(data)), type="MLE",
     mu=0, sigma=1, maxiter=20, maxincr=3, h=0.001, convP=1e-04,
     maxval=9, progress=TRUE)
}

%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{data}{
Data frame with item responses
}
\item{irffct}{
User defined item response (see Examples). Arguments must be
specified in \code{arg.list}. The function must contain \code{theta}
and \code{ii} (item index) as arguments.
}
  \item{theta}{
Initial ability estimate
}
  \item{arg.list}{
List of arguments for \code{irffct}.
}
  \item{type}{Type of ability estimate. It can be \code{"MLE"} (the default),
        \code{"WLE"} or \code{"MAP"}.
}
\item{mu}{Mean of normal prior distribution (for \code{type="MAP"})}
\item{sigma}{Standard deviation of normal prior distribution (for \code{type="MAP"})}
  \item{maxiter}{
Maximum number of iterations
}
  \item{maxincr}{
Maximum increment
}
  \item{h}{
Numerical differentiation parameter
}
  \item{convP}{
Convergence criterion
}
  \item{maxval}{
Maximum ability value to be estimated
}
  \item{progress}{
Logical indicating whether iteration progress should be displayed
}
}

%\details{
%%  ~~ If necessary, more details than the description above ~~
%}


\value{
Data frame with estimated abilities (\code{est}) and its standard error
(\code{se}).
}

\references{
Penfield, R. D., & Bergeron, J. M. (2005). Applying a weighted
maximum likelihood latent trait estimator to the generalized
partial credit model. \emph{Applied Psychological Measurement},
\bold{29}, 218-233.

Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory.
\emph{Psychometrika}, \bold{54}, 427-450.
}

\author{
Alexander Robitzsch
}

%\note{
%%  ~~further notes~~
%}

%% ~Make other sections like Warning with \section{Warning }{....} ~

\seealso{
See also the \pkg{PP} package for further person parameter
estimation methods.
}

\examples{
\dontrun{
#############################################################################
# EXAMPLE 1: Generalized partial credit model
#############################################################################

data(data.ratings1)
dat <- data.ratings1

# estimate model
mod1 <- sirt::rm.facets( dat[, paste0( "k",1:5) ], rater=dat$rater,
             pid=dat$idstud, maxiter=15)
# extract dataset and item parameters
data <- mod1$procdata$dat2.NA
a <- mod1$ipars.dat2$a
b <- mod1$ipars.dat2$b
theta0 <- mod1$person$EAP
# define item response function for item ii
calc.pcm <- function( theta, a, b, ii ){
    K <- ncol(b)
    N <- length(theta)
    matrK <- matrix( 0:K, nrow=N, ncol=K+1, byrow=TRUE)
    eta <- a[ii] * theta * matrK - matrix( c(0,b[ii,]), nrow=N, ncol=K+1, byrow=TRUE)
    eta <- exp(eta)
    probs <- eta / rowSums(eta, na.rm=TRUE)
    return(probs)
}
arg.list <- list("a"=a, "b"=b )

# MLE
abil1 <- sirt::IRT.mle( data, irffct=calc.pcm, theta=theta0, arg.list=arg.list )
str(abil1)
# WLE
abil2 <- sirt::IRT.mle( data, irffct=calc.pcm, theta=theta0, arg.list=arg.list, type="WLE")
str(abil2)
# MAP with prior distribution N(.2, 1.3)
abil3 <- sirt::IRT.mle( data, irffct=calc.pcm, theta=theta0, arg.list=arg.list,
              type="MAP", mu=.2, sigma=1.3 )
str(abil3)

#############################################################################
# EXAMPLE 2: Rasch model
#############################################################################

data(data.read)
dat <- data.read
I <- ncol(dat)

# estimate Rasch model
mod1 <- sirt::rasch.mml2( dat )
summary(mod1)

# define item response function
irffct <- function( theta, b, ii){
    eta <- exp( theta - b[ii] )
    probs <- eta / ( 1 + eta )
    probs <- cbind( 1 - probs, probs )
    return(probs)
}
# initial person parameters and item parameters
theta0 <- mod1$person$EAP
arg.list <- list( "b"=mod1$item$b  )

# estimate WLE
abil <- sirt::IRT.mle( data=dat, irffct=irffct, arg.list=arg.list,
            theta=theta0, type="WLE")
# compare with wle.rasch function
theta <- sirt::wle.rasch( dat, b=mod1$item$b )
cbind( abil[,1], theta$theta, abil[,2], theta$se.theta )

#############################################################################
# EXAMPLE 3: Ramsay quotient model
#############################################################################

data(data.read)
dat <- data.read
I <- ncol(dat)

# estimate Ramsay model
mod1 <- sirt::rasch.mml2( dat, irtmodel="ramsay.qm" )
summary(mod1)
# define item response function
irffct <- function( theta, b, K, ii){
    eta <- exp( theta / b[ii] )
    probs <- eta / ( K[ii] + eta )
    probs <- cbind( 1 - probs, probs )
    return(probs)
}
# initial person parameters and item parameters
theta0 <- exp( mod1$person$EAP )
arg.list <- list( "b"=mod1$item2$b, "K"=mod1$item2$K )
# estimate MLE
res <- sirt::IRT.mle( data=dat, irffct=irffct, arg.list=arg.list, theta=theta0,
            maxval=20, maxiter=50)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.

\keyword{Person parameters}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
