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mirt (version 1.1)

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

7,762

Version

1.1

License

GPL (>= 2)

Issues

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Repository

https://github.com/philchalmers/mirt

Maintainer

Phil Chalmers

Last Published

December 20th, 2013

Functions in mirt (1.1)

Calculate bootstrapped standard errors for estimated models

Item fit statistics

Description of Bock 1997 data

Methods for Function fscores

Create a user defined item with correct generic functions

ExploratoryClass-class

Class "ExploratoryClass"

Differential item functioning tests

ConfirmatoryClass-class

Class "ConfirmatoryClass"

Expand summary table of patterns and frequencies

MultipleGroupClass-class

Class "MultipleGroupClass"

Compute Extra Model Fit Indices

Multiple Group Estimation

Function to calculate expected value of item

Convert an estimated mirt model to special data.frame

SingleGroupClass-class

Class "SingleGroupClass"

Description of LSAT6 data

Compute profiled-likelihood confidence intervals

Wald test for mirt models

Monte Carlo Log-Likelihood Calculation

Person fit statistics

Compute fitted values

Description of deAyala data

Description of LSAT7 data

Print the model objects

Specify model loadings

Function to calculate item information

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

Compute random effects

DiscreteClass-class

Class "DiscreteClass"

Description of Science data

Mixed effects modeling for MIRT models

Differential test functioning statistics

Parametric smoothed regression lines for item response probability functions

Compare nested models with likelihood-based statistics

Plot various test implied functions from models

Extract raw coefs from model object

Extract an item object from mirt objects

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

Description of SAT12 data

Full information maximum likelihood estimation of IRT models.

Displays item surface and information plots

MixedClass-class

Class "MixedClass"

Compute multidimensional discrimination index

Imputing plausible data for missing values

Translate mirt parameters for plink package

Compute multidimensional difficulty index

Define a parallel cluster object to be used in internal functions

Function to calculate probability trace lines

Summary of model object

Extract a group from a multiple group mirt object

Simulate response patterns

Function to calculate the marginal reliability

Function to calculate expected test score

residuals-method

Compute model residuals

Compute the M2 model fit statistic

Function to calculate test information

Compute latent regression fixed effect expected values

Multidimensional discrete item response theory

Score a test by converting response patterns to binary data

Show model object

Collapse values from multiple imputation draws

Simultaneous Item Bias Test (SIBTEST)

Function to calculate the empirical (marginal) reliability

Extract various elements from estimated model objects

Function to calculate the area under a selection of information curves

Lagrange test for freeing parameters

Extract log-likelihood

Function to generate empirical unidimensional item and test plots

Extract parameter variance covariance matrix

numerical_deriv

Compute numerical derivatives

Empirical effect sizes based on latent trait estimates