This function computes the probability of correct answers for one or more items for a given set of theta values
using the IRT 1PL, 2PL, and 3PL models.

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 examine the IRT model-data fit on item-level in different ways
as well as provide useful functions related to unidimensional IRT models.
For the item parameter estimation, the marginal maximum likelihood estimation
via the expectation-maximization (MMLE-EM) algorithm
(Bock & Aitkin (1981) <doi:10.1007/BF02294168>) is used.
For the online calibration, the fixed item parameter calibration method
(Kim (2006) <doi:10.1111/j.1745-3984.2006.00021.x>) and
the fixed ability parameter calibration method
(Ban, Hanson, Wang, Yi, & Harris (2011) <doi:10.1111/j.1745-3984.2001.tb01123.x>)
are provided. For the ability estimation, several popular scoring methods
(e.g., MLE, EAP, and MAP) are implemented. In terms of assessing the IRT
model-data fit, one of distinguished features of this package is that it
gives not only well-known item fit statistics (e.g., chi-square (X2),
likelihood ratio chi-square (G2), infit and oufit statistics, and
S-X2 statistic (Ames & Penfield (2015) <doi:10.1111/emip.12067>))
but also graphical displays to look at residuals between the observed
data and model-based predictions
(Hambleton, Swaminathan, & Rogers (1991, ISBN:9780803936478)).
In addition, there are many useful functions such as analyzing differential item
functioning, computing asymptotic variance-covariance matrices of item parameter
estimates (Li & Lissitz (2004) <doi:10.1111/j.1745-3984.2004.tb01109.x>),
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 (Lord & Wingersky (1984)
<doi:10.1177/014662168400800409>), computing the loglikelihood of individual
items, computing the loglikelihood of abilities, 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. See Lim and Wells (2022) <doi:10.18637/jss.v103.i12> for more details.

Hwanggyu Lim

irtplay

Unidimensional Item Response Theory Modeling

drm function

<dl><dt>theta</dt>
<dd>A vector of ability values.</dd>
<dt>a</dt>
<dd>A vector of item discrimination (or slope) parameters.</dd>
<dt>b</dt>
<dd>A vector of item difficulty (or threshold) parameters.</dd>
<dt>g</dt>
<dd>A vector of item guessing parameters.</dd>
<dt>D</dt>
<dd>A scaling factor in IRT models to make the logistic function as close as possible to the normal ogive function (if set to 1.7).
Default is 1.</dd></dl>

Arguments

Hwanggyu Lim <a href="/link/hglim83%40gmail.com?package=irtplay&version=1.6.5" data-mini-rdoc="irtplay::hglim83@gmail.com">hglim83@gmail.com</a>

Author

Dichotomous Response Model Probabilities — drm

<dl>

<dt>theta</dt>
<dd>A vector of ability values.</dd>


<dt>a</dt>
<dd>A vector of item discrimination (or slope) parameters.</dd>


<dt>b</dt>
<dd>A vector of item difficulty (or threshold) parameters.</dd>


<dt>g</dt>
<dd>A vector of item guessing parameters.</dd>


<dt>D</dt>
<dd>A scaling factor in IRT models to make the logistic function as close as possible to the normal ogive function (if set to 1.7).
Default is 1.</dd>

</dl>

Hwanggyu Lim <a href='mailto:hglim83@gmail.com'>hglim83@gmail.com</a>

drm: Dichotomous Response Model Probabilities

Description

Usage

Value

Arguments

Author

Details

See Also

Examples