mirt v1.33.2

Multidimensional Item Response Theory

Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory models can be estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item and test functioning as well as modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, and several other discrete latent variable models, including mixture and zero-inflated response models, are supported.

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mirt

Multidimensional item response theory in R.

Description

Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm. Exploratory and confirmatory models can be estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item functioning and modeling item and person covariates.

Examples and evaluated help files are available on the wiki

Various examples and worked help files have been compiled using the knitr package to generate HTML output, and are available on the package wiki. User contributions are welcome!

Installing from source

It's recommended to use the development version of this package since it is more likely to be up to date than the version on CRAN. To install this package from source:

1) Obtain recent gcc, g++, and gfortran compilers. Windows users can install the Rtools suite while Mac users will have to download the necessary tools from the Xcode suite and its related command line tools (found within Xcode's Preference Pane under Downloads/Components); most Linux distributions should already have up to date compilers (or if not they can be updated easily). Windows users should include the checkbox option of installing Rtools to their path for easier command line usage.

2) Install the devtools package (if necessary). In R, paste the following into the console:

install.packages('devtools')

3) Load the devtools package (requires version 1.4+) and install from the Github source code.

library('devtools')
install_github('philchalmers/mirt')

Installing from source via git

If the devtools approach does not work on your system, then you can download and install the repository directly.

1) Obtain recent gcc, g++, and gfortran compilers (see above instructions).

2) Install the git command line tools.

3) Open a terminal/command-line tool. The following code will download the repository code to your computer, and install the package directly using R tools (Windows users may also have to add R and git to their path)

git clone https://github.com/philchalmers/mirt
R CMD INSTALL mirt

Special Mac OS X Installation Instructions

In some reported cases XCode does not install the appropriate gfortran compilers in the correct location, therefore they have to be installed manually instead. This is accomplished by inputing the following instructions into the terminal:

curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

Licence

This package is free and open source software, licensed under GPL (>= 3).

Bugs and Questions

Bug reports are always welcome and the preferred way to address these bugs is through the Github 'issues'. Feel free to submit issues or feature requests on the site, and I'll address them ASAP. Also, if you have any questions about the package, or IRT in general, then feel free to create a 'New Topic' in the mirt-package Google group. Cheers!

Functions in mirt

Name Description
MixedClass-class Class "MixedClass"
LSAT7 Description of LSAT7 data
LSAT6 Description of LSAT6 data
DiscreteClass-class Class "DiscreteClass"
SIBTEST Simultaneous Item Bias Test (SIBTEST)
createGroup Create a user defined group-level object with correct generic functions
Science Description of Science data
M2 Compute the M2 model fit statistic
MDIFF Compute multidimensional difficulty index
anova-method Compare nested models with likelihood-based statistics
MixtureClass-class Class "MixtureClass"
SingleGroupClass-class Class "SingleGroupClass"
bfactor Full-Information Item Bi-factor and Two-Tier Analysis
coef-method Extract raw coefs from model object
boot.mirt Calculate bootstrapped standard errors for estimated models
boot.LR Parametric bootstrap likelihood-ratio test
MDISC Compute multidimensional discrimination index
createItem Create a user defined item with correct generic functions
DRF Differential Response Functioning statistics
RMSD_DIF RMSD effect size statistic to quantify category-level DIF
DTF Differential test functioning statistics
Bock1997 Description of Bock 1997 data
expand.table Expand summary table of patterns and frequencies
extract.item Extract an item object from mirt objects
DIF Differential item functioning statistics
fixedCalib Fixed-item calibration method
expected.item Function to calculate expected value of item
extract.mirt Extract various elements from estimated model objects
SAT12 Description of SAT12 data
fixef Compute latent regression fixed effect expected values
mdirt Multidimensional discrete item response theory
MultipleGroupClass-class Class "MultipleGroupClass"
PLCI.mirt Compute profiled-likelihood (or posterior) confidence intervals
key2binary Score a test by converting response patterns to binary data
empirical_ES Empirical effect sizes based on latent trait estimates
itemplot Displays item surface and information plots
expected.test Function to calculate expected test score
averageMI Collapse values from multiple imputation draws
areainfo Function to calculate the area under a selection of information curves
thetaComb Create all possible combinations of vector input
plot,MultipleGroupClass,missing-method Plot various test-implied functions from models
poly2dich Change polytomous items to dichotomous item format
iteminfo Function to calculate item information
empirical_plot Function to generate empirical unidimensional item and test plots
itemfit Item fit statistics
testinfo Function to calculate test information
extract.group Extract a group from a multiple group mirt object
mirt-package Full information maximum likelihood estimation of IRT models.
numerical_deriv Compute numerical derivatives
deAyala Description of deAyala data
imputeMissing Imputing plausible data for missing values
lagrange Lagrange test for freeing parameters
likert2int Convert ordered Likert-scale responses (character or factors) to integers
itemGAM Parametric smoothed regression lines for item response probability functions
draw_parameters Draw plausible parameter instantiations from a given model
mirt.model Specify model information
summary-method Summary of model object
simdata Simulate response patterns
mirt Full-Information Item Factor Analysis (Multidimensional Item Response Theory)
personfit Person fit statistics
print.mirt_list Print generic for customized list console output
logLik-method Extract log-likelihood
print.mirt_matrix Print generic for customized matrix console output
empirical_rxx Function to calculate the empirical (marginal) reliability
probtrace Function to calculate probability trace lines
randef Compute posterior estimates of random effect
estfun.AllModelClass Extract Empirical Estimating Functions
fscores Compute factor score estimates (a.k.a, ability estimates, latent trait estimates, etc)
print-method Print the model objects
print.mirt_df Print generic for customized data.frame console output
remap.distance Remap item categories to have integer distances of 1
marginal_rxx Function to calculate the marginal reliability
read.mirt Translate mirt parameters into suitable structure for plink package
gen.difficulty Generalized item difficulty summaries
wald Wald statistics for mirt models
residuals-method Compute model residuals
mod2values Convert an estimated mirt model to a data.frame
show-method Show model object
mixedmirt Mixed effects modeling for MIRT models
mirtCluster Define a parallel cluster object to be used in internal functions
multipleGroup Multiple Group Estimation
traditional2mirt Convert traditional IRT metric into slope-intercept form used in mirt
vcov-method Extract parameter variance covariance matrix
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