% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/irtQ-package.R
\docType{package}
\name{irtQ-package}
\alias{irtQ-package}
\title{irtQ: Unidimensional Item Response Theory Modeling}
\description{
Fit unidimensional item response theory (IRT) models to a mixture of dichotomous and polytomous data,
calibrate online item parameters (i.e., pretest and operational items), estimate examinees' abilities,
and  provide useful functions related to unidimensional IRT such as IRT model-data fit evaluation and
differential item functioning analysis.

For the item parameter estimation, the marginal maximum likelihood estimation via the expectation-maximization (MMLE-EM) algorithm
(Bock & Aitkin, 1981) is used. Also, the fixed item parameter calibration (FIPC) method (Kim, 2006) and
the fixed ability parameter calibration (FAPC) method, (Ban, Hanson, Wang, Yi, & Harris, 2001; stocking, 1988),
often called Stocking's Method A, are provided. For the ability estimation, several popular scoring methods (e.g., ML, EAP, and MAP)
are implemented.

In addition, there are many useful functions related to IRT analyses such as evaluating IRT model-data fit,
analyzing differential item functioning (DIF), importing item and/or ability parameters from popular IRT software,
running flexMIRT (Cai, 2017) through R, generating simulated data, computing the conditional distribution of observed scores
using the Lord-Wingersky recursion formula, computing item and test information functions, computing item and test characteristic
curve functions, and plotting item and test characteristic curves and item and test information functions.

\tabular{ll}{ Package: \tab irtQ\cr Version: \tab 0.1.1\cr Date: \tab
2023-05-08\cr Depends: \tab R (>= 4.1)\cr License: \tab GPL (>= 2)\cr }
}
\details{
Following five sections describe a) how to implement the online item calibration using FIPC, a) how to implement the online item
calibration using Method A, b) two illustrations of the online calibration, and c) IRT Models used in \pkg{irtQ} package.
}
\section{Online item calibration with the fixed item parameter calibration method (e.g., Kim, 2006)}{


The fixed item parameter calibration (FIPC) is one of useful online item calibration methods for computerized adaptive testing (CAT)
to put the parameter estimates of pretest items on the same scale of operational item parameter estimates without post hoc
linking/scaling (Ban, Hanson, Wang, Yi, & Harris, 2001; Chen & Wang, 2016). In FIPC, the operational item parameters are fixed to
estimate the characteristic of the underlying latent variable prior distribution when calibrating the pretest items. More specifically,
the underlying latent variable prior distribution of the operational items is estimated during the calibration of the pretest
items to put the item parameters of the pretest items on the scale of the operational item parameters (Kim, 2006). In the \pkg{irtQ}
package, FIPC is implemented with two main steps:

\enumerate{
  \item Prepare a response data set and the item metadata of the fixed (or operational) items.
  \item Implement FIPC to estimate the item parameters of pretest items using the \code{\link{est_irt}} function.
}

\describe{
  \item{1. Preparing a data set}{
  To run the \code{\link{est_irt}} function, it requires two data sets:

    \enumerate{
      \item Item metadata set (i.e., model, score category, and item parameters. see the desciption of the argument \code{x} in the function \code{\link{est_irt}}).
      \item Examinees' response data set for the items. It should be a matrix format where a row and column indicate the examinees and the items, respectively.
      The order of the columns in the response data set must be exactly the same as the order of rows of the item metadata.
    }
  }

  \item{2. Estimating the pretest item parameters}{
  When FIPC is implemented in \code{\link{est_irt}} function, the pretest item parameters are estimated by fixing the operational item parameters. To estimate the item
  parameters, you need to provide the item metadata in the argument \code{x} and the response data in the argument \code{data}.

  It is worthwhile to explain about how to prepare the item metadata set in the argument \code{x}. A specific form of a data frame should be used for
  the argument \code{x}. The first column should have item IDs, the second column should contain the number of score categories of the items, and the third
  column should include IRT models. The available IRT models are "1PLM", "2PLM", "3PLM", and "DRM" for dichotomous items, and "GRM" and "GPCM" for polytomous
  items. Note that "DRM" covers all dichotomous IRT models (i.e, "1PLM", "2PLM", and "3PLM") and "GRM" and "GPCM" represent the graded response model and
  (generalized) partial credit model, respectively. From the fourth column, item parameters should be included. For dichotomous items, the fourth, fifth,
  and sixth columns represent the item discrimination (or slope), item difficulty, and item guessing parameters, respectively. When "1PLM" or "2PLM" is
  specified for any items in the third column, NAs should be inserted for the item guessing parameters. For polytomous items, the item discrimination (or slope)
  parameters should be contained in the fourth column and the item threshold (or step) parameters should be included from the fifth to the last columns.
  When the number of categories differs between items, the empty cells of item parameters should be filled with NAs. See `est_irt` for more details about
  the item metadata.

  Also, you should specify in the argument \code{fipc = TRUE} and a specific FIPC method in the argument \code{fipc.method}. Finally, you should provide
  a vector of the location of the items to be fixed in the argument \code{fix.loc}. For more details about implementing FIPC, see the
  description of the function \code{\link{est_irt}}.

  When implementing FIPC, you can estimate both the emprical histogram and the scale of latent variable prior distribution by setting \code{EmpHist = TRUE}.
  If \code{EmpHist = FALSE}, the normal prior distribution is used during the item parameter estimation and the scale of the normal prior distribution is
  updated during the EM cycle.

  The \code{\link{est_item}} function requires a vector of the number of score categories for the items in the argument \code{cats}. For example, a dichotomous item has
  two score categories. If a single numeric value is specified, that value will be recycled across all items. If NULL and all items are binary items
  (i.e., dichotomous items), it assumes that all items have two score categories.

  If necessary, you need to specify whether prior distributions of item slope and guessing parameters (only for the IRT 3PL model) are used in the arguments of
  \code{use.aprior} and \code{use.gprior}, respectively. If you decide to use the prior distributions, you should specify what distributions will be used for the prior
  distributions in the arguments of \code{aprior} and \code{gprior}, respectively. Currently three probability distributions of Beta, Log-normal, and Normal
  distributions are available.

  In addition, if the response data include missing values, you must indicate the missing value in argument \code{missing}.

  Once the \code{\link{est_irt}} function has been implemented, you'll get a list of several internal objects such as the item parameter estimates,
  standard error of the parameter estimates.
  }
}
}

\section{Online item calibration with the fixed ability parameter calibration method (e.g., Stocking, 1988)}{

In CAT, the fixed ability parameter calibration (FAPC), often called Stocking's Method A, is the relatively simplest
and most straightforward online calibration method, which is the maximum likelihood estimation of the item parameters
given the proficiency estimates. In CAT, FAPC can be used to put the parameter estimates of pretest items on
the same scale of operational item parameter estimates and recalibrate the operational items to evaluate the parameter
drifts of the operational items (Chen & Wang, 2016; Stocking, 1988). Also, FAPC is known to result in accurate, unbiased
item parameters calibration when items are randomly rather than adaptively administered to examinees, which occurs most
commonly with pretest items (Ban, Hanson, Wang, Yi, & Harris, 2001; Chen & Wang, 2016). Using \pkg{irtQ} package,
the FAPC is implemented to calibrate the items with two main steps:

\enumerate{
  \item Prepare a data set for the calibration of item parameters (i.e., item response data and ability estimates).
  \item Implement the FAPC to estimate the item parameters using the \code{\link{est_item}} function.
}

\describe{
  \item{1. Preparing a data set}{
  To run the \code{\link{est_item}} function, it requires two data sets:

    \enumerate{
      \item Examinees' ability (or proficiency) estimates. It should be in the format of a numeric vector.
      \item response data set for the items. It should be in the format of matrix where a row and column indicate
    the examinees and the items, respectively. The order of the examinees in the response data set must be exactly the same as that of the examinees' ability estimates.
    }
  }

  \item{2. Estimating the pretest item parameters}{
  The \code{\link{est_item}} function estimates the pretest item parameters given the proficiency estimates. To estimate the item parameters,
  you need to provide the response data in the argument \code{data} and the ability estimates in the argument \code{score}.

  Also, you should provide a string vector of the IRT models in the argument \code{model} to indicate what IRT model is used to calibrate each item.
  Available IRT models are "1PLM", "2PLM", "3PLM", and "DRM" for dichotomous items, and "GRM" and "GPCM" for polytomous items. "GRM" and "GPCM" represent
  the graded response model and (generalized) partial credit model, respectively. Note that "DRM" is considered as "3PLM" in this function. If a single
  character of the IRT model is specified, that model will be recycled across all items.

  The \code{\link{est_item}} function requires a vector of the number of score categories for the items in the argument \code{cats}. For example, a dichotomous item has
  two score categories. If a single numeric value is specified, that value will be recycled across all items. If NULL and all items are binary items
  (i.e., dichotomous items), it assumes that all items have two score categories.

  If necessary, you need to specify whether prior distributions of item slope and guessing parameters (only for the IRT 3PL model) are used in the arguments of
  \code{use.aprior} and \code{use.gprior}, respectively. If you decide to use the prior distributions, you should specify what distributions will be used for the prior
  distributions in the arguments of \code{aprior} and \code{gprior}, respectively. Currently three probability distributions of Beta, Log-normal, and Normal
  distributions are available.

  In addition, if the response data include missing values, you must indicate the missing value in argument \code{missing}.

  Once the \code{\link{est_item}} function has been implemented, you'll get a list of several internal objects such as the item parameter estimates,
  standard error of the parameter estimates.
  }
}
}

\section{Three examples of R script}{


The example code below shows how to implement the online calibration and how to evaluate the IRT model-data fit:\preformatted{
##---------------------------------------------------------------
# Attach the packages
library(irtQ)

##----------------------------------------------------------------------------
# 1. The example code below shows how to prepare the data sets and how to
#    implement the fixed item parameter calibration (FIPC):
##----------------------------------------------------------------------------

## Step 1: prepare a data set
## In this example, we generated examinees' true proficiency parameters and simulated
## the item response data using the function "simdat".

## import the "-prm.txt" output file from flexMIRT
flex_sam <- system.file("extdata", "flexmirt_sample-prm.txt", package = "irtQ")

# select the item metadata
x <- bring.flexmirt(file=flex_sam, "par")$Group1$full_df

# generate 1,000 examinees' latent abilities from N(0.4, 1.3)
set.seed(20)
score <- rnorm(1000, mean=0.4, sd=1.3)

# simulate the response data
sim.dat <- simdat(x=x, theta=score, D=1)

## Step 2: Estimate the item parameters
# fit the 3PL model to all dichotmous items, fit the GRM model to all polytomous data,
# fix the five 3PL items (1st - 5th items) and three GRM items (53th to 55th items)
# also, estimate the empirical histogram of latent variable
fix.loc <- c(1:5, 53:55)
(mod.fix1 <- est_irt(x=x, data=sim.dat, D=1, use.gprior=TRUE,
                    gprior=list(dist="beta", params=c(5, 16)), EmpHist=TRUE, Etol=1e-3,
                    fipc=TRUE, fipc.method="MEM", fix.loc=fix.loc))
summary(mod.fix1)

# plot the estimated empirical histogram of latent variable prior distribution
(emphist <- getirt(mod.fix1, what="weights"))
plot(emphist$weight ~ emphist$theta, xlab="Theta", ylab="Density")


##----------------------------------------------------------------------------
# 2. The example code below shows how to prepare the data sets and how to estimate
#    the item parameters using the fixed abilit parameter calibration (FAPC):
##----------------------------------------------------------------------------

## Step 1: prepare a data set
## In this example, we generated examinees' true proficiency parameters and simulated
## the item response data using the function "simdat". Because, the true
## proficiency parameters are not known in reality, however, the true proficiencies
## would be replaced with the proficiency estimates for the calibration.

# import the "-prm.txt" output file from flexMIRT
flex_sam <- system.file("extdata", "flexmirt_sample-prm.txt", package = "irtQ")

# select the item metadata
x <- bring.flexmirt(file=flex_sam, "par")$Group1$full_df

# modify the item metadata so that some items follow 1PLM, 2PLM and GPCM
x[c(1:3, 5), 3] <- "1PLM"
x[c(1:3, 5), 4] <- 1
x[c(1:3, 5), 6] <- 0
x[c(4, 8:12), 3] <- "2PLM"
x[c(4, 8:12), 6] <- 0
x[54:55, 3] <- "GPCM"

# generate examinees' abilities from N(0, 1)
set.seed(23)
score <- rnorm(500, mean=0, sd=1)

# simulate the response data
data <- simdat(x=x, theta=score, D=1)

## Step 2: Estimate the item parameters
# 1) item parameter estimation: constrain the slope parameters of the 1PLM to be equal
(mod1 <- est_item(x, data, score, D=1, fix.a.1pl=FALSE, use.gprior=TRUE,
                 gprior=list(dist="beta", params=c(5, 17)), use.startval=FALSE))
summary(mod1)

# 2) item parameter estimation: fix the slope parameters of the 1PLM to 1
(mod2 <- est_item(x, data, score, D=1, fix.a.1pl=TRUE, a.val.1pl=1, use.gprior=TRUE,
                 gprior=list(dist="beta", params=c(5, 17)), use.startval=FALSE))
summary(mod2)

# 3) item parameter estimation: fix the guessing parameters of the 3PLM to 0.2
(mod3 <- est_item(x, data, score, D=1, fix.a.1pl=TRUE, fix.g=TRUE, a.val.1pl=1, g.val=.2,
                 use.startval=FALSE))
summary(mod3)


}
}

\section{IRT Models}{


In the \pkg{irtQ} package, both dichotomous and polytomous IRT models are available.
For dichotomous items, IRT one-, two-, and three-parameter logistic models (1PLM, 2PLM, and 3PLM) are used.
For polytomous items, the graded response model (GRM) and the (generalized) partial credit model (GPCM) are used.
Note that the item discrimination (or slope) parameters should be fixed to 1 when the partial credit model is fit to data.

In the following, let \eqn{Y} be the response of an examinee with latent ability \eqn{\theta} on an item and suppose that there
are \eqn{K} unique score categories for each polytomous item.

\describe{
  \item{IRT 1-3PL models}{
    For the IRT 1-3PL models, the probability that an examinee with \eqn{\theta} provides a correct answer for an item is given by,
     \deqn{P(Y = 1|\theta) = g + \frac{(1 - g)}{1 + exp(-Da(\theta - b))},}
    where \eqn{a} is the item discrimination (or slope) parameter, \eqn{b} represents the item difficulty parameter,
    \eqn{g} refers to the item guessing parameter. \eqn{D} is a scaling factor in IRT models to make the logistic function
    as close as possible to the normal ogive function when \eqn{D = 1.702}. When the 1PLM is used, \eqn{a} is either fixed to a constant
    value (e.g., \eqn{a=1}) or constrained to have the same value across all 1PLM item data. When the IRT 1PLM or 2PLM is fit to data,
    \eqn{g = 0} is set to 0.
  }
  \item{GRM}{
    For the GRM, the probability that an examinee with latent ability \eqn{\theta} responds to score category \eqn{k} (\eqn{k=0,1,...,K})
    of an item is a given by,
    \deqn{P(Y = k | \theta) = P^{*}(Y \ge k | \theta) - P^{*}(Y \ge k + 1 | \theta),}
    \deqn{P^{*}(Y \ge k | \theta) = \frac{1}{1 + exp(-Da(\theta - b_{k}))}, and}
    \deqn{P^{*}(Y \ge k + 1 | \theta) = \frac{1}{1 + exp(-Da(\theta - b_{k+1}))}, }

    where \eqn{P^{*}(Y \ge k | \theta} refers to the category boundary (threshold) function for score category \eqn{k} of an item
    and its formula is analogous to that of 2PLM. \eqn{b_{k}} is the difficulty (or threshold) parameter for category boundary
    \eqn{k} of an item. Note that \eqn{P(Y = 0 | \theta) = 1 - P^{*}(Y \ge 1 | \theta)}
    and \eqn{P(Y = K-1 | \theta) = P^{*}(Y \ge K-1 | \theta)}.
  }
  \item{GPCM}{
    For the GPCM, the probability that an examinee with latent ability \eqn{\theta} responds to score category \eqn{k} (\eqn{k=0,1,...,K})
    of an item is a given by,
     \deqn{P(Y = k | \theta) = \frac{exp(\sum_{v=0}^{k}{Da(\theta - b_{v})})}{\sum_{h=0}^{K-1}{exp(\sum_{v=0}^{h}{Da(\theta - b_{v})})}},}
    where \eqn{b_{v}} is the difficulty parameter for category boundary \eqn{v} of an item. In other contexts, the difficulty parameter \eqn{b_{v}}
    can also be parameterized as \eqn{b_{v} = \beta - \tau_{v}}, where \eqn{\beta} refers to the location (or overall difficulty) parameter
    and \eqn{\tau_{jv}} represents a threshold parameter for score category \eqn{v} of an item. In the \pkg{irtQ} package, \eqn{K-1} difficulty
    parameters are necessary when an item has \eqn{K} unique score categories because \eqn{b_{0}=0}. When a partial credit model is fit to data, \eqn{a}
    is fixed to 1.
   }

}
}

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}
\author{
Hwanggyu Lim \email{hglim83@gmail.com}
}
\keyword{package}
