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mirt (version 1.13)

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 functioning and modelling item and person covariates.

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Install

install.packages('mirt')

Monthly Downloads

9,054

Version

1.13

License

GPL (>= 3)

Issues

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Repository

https://github.com/philchalmers/mirt

Homepage

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

Maintainer

Phil Chalmers

Last Published

September 9th, 2015

Functions in mirt (1.13)

Compare nested models with likelihood-based statistics

Person fit statistics

Convert an estimated mirt model to a data.frame

Displays item surface and information plots

Collapse values from multiple imputation draws

Print the model objects

Compute multidimensional difficulty index

Show model object

Function to calculate expected test score

Description of Bock 1997 data

MultipleGroupClass-class

Class "MultipleGroupClass"

Compute profiled-likelihood (or posterior) confidence intervals

Description of LSAT7 data

Description of SAT12 data

Specify model loadings

Differential item functioning statistics

Compute multidimensional discrimination index

Function to calculate item information

Description of Science data

Differential test functioning statistics

Item fit statistics

Calculate bootstrapped standard errors for estimated models

Parametric smoothed regression lines for item response probability functions

Description of deAyala data

Compute the M2 model fit statistic

SingleGroupClass-class

Class "SingleGroupClass"

DiscreteClass-class

Class "DiscreteClass"

Create a user defined item with correct generic functions

Extract raw coefs from model object

Function to calculate expected value of item

Extract an item object from mirt objects

Compute latent regression fixed effect expected values

Compute posterior estimates of random effect

MixedClass-class

Class "MixedClass"

Full information maximum likelihood estimation of IRT models.

Score a test by converting response patterns to binary data

Mixed effects modeling for MIRT models

Multidimensional discrete item response theory

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

Function to calculate test information

Function to calculate probability trace lines

Summary of model object

residuals-method

Compute model residuals

Imputing plausible data for missing values

Description of LSAT6 data

Define a parallel cluster object to be used in internal functions

Simulate response patterns

Plot various test-implied functions from models

Extract a group from a multiple group mirt object

Expand summary table of patterns and frequencies

Function to calculate the marginal reliability

Wald statistics for mirt models

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

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

Multiple Group Estimation