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mirt (version 1.15)

Multidimensional Item Response Theory

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 and test functioning as well as modelling item and person covariates.

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Version

Install

install.packages('mirt')

Monthly Downloads

5,793

Version

1.15

License

GPL (>= 3)

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Repository

https://github.com/philchalmers/mirt

Homepage

https://github.com/philchalmers/mirt/wiki

Maintainer

Phil Chalmers

Last Published

January 21st, 2016

Functions in mirt (1.15)

Description of SAT12 data

Full-Information Item Bi-factor and Two-Tier Analysis

Compute factor score estimates (a.k.a, ability estimates, latent trait estimates, etc)

Description of Bock 1997 data

Function to calculate expected value of item

Function to generate empirical unidimensional item and test plots

Full information maximum likelihood estimation of IRT models.

MixedClass-class

Class "MixedClass"

Item fit statistics

Parametric smoothed regression lines for item response probability functions

Simulate response patterns

Calculate bootstrapped standard errors for estimated models

Function to calculate the marginal reliability

Description of Science data

Compute multidimensional difficulty index

Compare nested models with likelihood-based statistics

Compute posterior estimates of random effect

Score a test by converting response patterns to binary data

Plot various test-implied functions from models

Displays item surface and information plots

Differential item functioning statistics

Compute profiled-likelihood (or posterior) confidence intervals

Compute latent regression fixed effect expected values

Extract a group from a multiple group mirt object

Multidimensional discrete item response theory

Simultaneous Item Bias Test (SIBTEST)

Print the model objects

DiscreteClass-class

Class "DiscreteClass"

Description of LSAT7 data

Description of LSAT6 data

Function to calculate item information

Function to calculate the empirical (marginal) reliability

Extract various elements from estimated model objects

Description of deAyala data

Function to calculate probability trace lines

Collapse values from multiple imputation draws

Full-Information Item Factor Analysis (Multidimensional Item Response Theory)

Imputing plausible data for missing values

Define a parallel cluster object to be used in internal functions

Show model object

Mixed effects modeling for MIRT models

Multiple Group Estimation

Wald statistics for mirt models

Differential test functioning statistics

Function to calculate test information

Function to calculate expected test score

Specify model loadings

Compute multidimensional discrimination index

Extract an item object from mirt objects

Expand summary table of patterns and frequencies

Convert an estimated mirt model to a data.frame

MultipleGroupClass-class

Class "MultipleGroupClass"

SingleGroupClass-class

Class "SingleGroupClass"

Create a user defined item with correct generic functions

residuals-method

Compute model residuals

Summary of model object

Compute the M2 model fit statistic

Extract raw coefs from model object

Person fit statistics