Package index • mirt

All functions

ASVAB: Description of ASVAB data

Bock1997: Description of Bock 1997 data

DIF(): Differential item functioning statistics

DRF(): Differential Response Functioning statistics

DTF(): Differential test functioning statistics

DiscreteClass-class: Class "DiscreteClass"

LSAT6: Description of LSAT6 data

LSAT7: Description of LSAT7 data

M2(): Compute the M2 model fit statistic

MDIFF(): Compute multidimensional difficulty index

MDISC(): Compute multidimensional discrimination index

MixedClass-class: Class "MixedClass"

MixtureClass-class: Class "MixtureClass"

MultipleGroupClass-class: Class "MultipleGroupClass"

PLCI.mirt(): Compute profiled-likelihood (or posterior) confidence intervals

RCI(): Model-based Reliable Change Index

RMSD_DIF(): RMSD effect size statistic to quantify category-level DIF

SAT12: Description of SAT12 data

SIBTEST(): (Generalized) Simultaneous Item Bias Test (SIBTEST)

SLF: Social Life Feelings Data

Science: Description of Science data

SingleGroupClass-class: Class "SingleGroupClass"

anova(<SingleGroupClass>): Compare nested models with likelihood-based statistics

areainfo(): Function to calculate the area under a selection of information curves

averageMI(): Collapse values from multiple imputation draws

bfactor(): Full-Information Item Bi-factor and Two-Tier Analysis

boot.LR(): Parametric bootstrap likelihood-ratio test

boot.mirt(): Calculate bootstrapped standard errors for estimated models

coef(<SingleGroupClass>): Extract raw coefs from model object

createGroup(): Create a user defined group-level object with correct generic functions

createItem(): Create a user defined item with correct generic functions

deAyala: Description of deAyala data

draw_parameters(): Draw plausible parameter instantiations from a given model

empirical_ES(): Empirical effect sizes based on latent trait estimates

empirical_plot(): Function to generate empirical unidimensional item and test plots

empirical_rxx(): Function to calculate the empirical (marginal) reliability

estfun.AllModelClass(): Extract Empirical Estimating Functions

expand.table(): Expand summary table of patterns and frequencies

expected.item(): Function to calculate expected value of item

expected.test(): Function to calculate expected test score

extract.group(): Extract a group from a multiple group mirt object

extract.item(): Extract an item object from mirt objects

extract.mirt(): Extract various elements from estimated model objects

fixedCalib(): Fixed-item calibration method

fixef(): Compute latent regression fixed effect expected values

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

gen.difficulty(): Generalized item difficulty summaries

imputeMissing(): Imputing plausible data for missing values

itemGAM() plot(<itemGAM>): Parametric smoothed regression lines for item response probability functions

itemfit(): Item fit statistics

iteminfo(): Function to calculate item information

itemplot(): Displays item surface and information plots

itemstats(): Generic item summary statistics

key2binary(): Score a test by converting response patterns to binary data

lagrange(): Lagrange test for freeing parameters

likert2int(): Convert ordered Likert-scale responses (character or factors) to integers

logLik(<SingleGroupClass>): Extract log-likelihood

marginal_rxx(): Function to calculate the marginal reliability

mdirt(): Multidimensional discrete item response theory

mirt-package: Full information maximum likelihood estimation of IRT models.

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

mirt.model(): Specify model information

mirtCluster(): Define a parallel cluster object to be used in internal functions

mixedmirt(): Mixed effects modeling for MIRT models

mod2values(): Convert an estimated mirt model to a data.frame

multipleGroup(): Multiple Group Estimation

numerical_deriv(): Compute numerical derivatives

personfit(): Person fit statistics

plot(<MultipleGroupClass>,<missing>) plot(<SingleGroupClass>,<missing>): Plot various test-implied functions from models

poly2dich(): Change polytomous items to dichotomous item format

print(<SingleGroupClass>): Print the model objects

print(<mirt_df>): Print generic for customized data.frame console output

print(<mirt_list>): Print generic for customized list console output

print(<mirt_matrix>): Print generic for customized matrix console output

probtrace(): Function to calculate probability trace lines

randef(): Compute posterior estimates of random effect

read.mirt(): Translate mirt parameters into suitable structure for plink package

remap.distance(): Remap item categories to have integer distances of 1

residuals(<SingleGroupClass>): Compute model residuals

reverse.score(): Reverse score one or more items from a response matrix

secondOrderTest(): Second-order test of convergence

show(<SingleGroupClass>): Show model object

simdata(): Simulate response patterns

summary(<SingleGroupClass>): Summary of model object

testinfo(): Function to calculate test information

thetaComb(): Create all possible combinations of vector input

traditional2mirt(): Convert traditional IRT metric into slope-intercept form used in mirt

vcov(<SingleGroupClass>): Extract parameter variance covariance matrix

wald(): Wald statistics for mirt models