Function reference • sirt

All functions

automatic.recode(): Automatic Method of Finding Keys in a Dataset with Raw Item Responses

brm.sim() brm.irf(): Functions for the Beta Item Response Model

btm() summary(<btm>) predict(<btm>) btm_sim(): Extended Bradley-Terry Model

categorize() decategorize(): Categorize and Decategorize Variables in a Data Frame

ccov.np(): Nonparametric Estimation of Conditional Covariances of Item Pairs

cfa_meas_inv(): Estimation of a Unidimensional Factor Model under Full and Partial Measurement Invariance

class.accuracy.rasch(): Classification Accuracy in the Rasch Model

conf.detect() summary(<conf.detect>): Confirmatory DETECT and polyDETECT Analysis

data.activity.itempars: Item Parameters Cultural Activities

data.befki data.befki_resp: BEFKI Dataset (Schroeders, Schipolowski, & Wilhelm, 2015)

data.big5 data.big5.qgraph: Dataset Big 5 from qgraph Package

data.bs07a: Datasets from Borg and Staufenbiel (2007)

data.eid.kap4 data.eid.kap5 data.eid.kap6 data.eid.kap7: Examples with Datasets from Eid and Schmidt (2014)

data.ess2005: Dataset European Social Survey 2005

data.g308: C-Test Datasets

data.inv4gr: Dataset for Invariance Testing with 4 Groups

data.liking.science: Dataset 'Liking For Science'

data.long: Longitudinal Dataset

data.lsem01 data.lsem02 data.lsem03: Datasets for Local Structural Equation Models / Moderated Factor Analysis

data.math: Dataset Mathematics

data.mcdonald.act15 data.mcdonald.LSAT6 data.mcdonald.rape: Some Datasets from McDonald's Test Theory Book

data.mixed1: Dataset with Mixed Dichotomous and Polytomous Item Responses

data.ml1 data.ml2: Multilevel Datasets

data.noharmExC data.noharm18: Datasets for NOHARM Analysis

data.pars1.rasch data.pars1.2pl: Item Parameters for Three Studies Obtained by 1PL and 2PL Estimation

data.pirlsmissing: Dataset from PIRLS Study with Missing Responses

data.pisaMath: Dataset PISA Mathematics

data.pisaPars: Item Parameters from Two PISA Studies

data.pisaRead: Dataset PISA Reading

data.pw01: Datasets for Pairwise Comparisons

data.ratings1 data.ratings2 data.ratings3: Rating Datasets

data.raw1: Dataset with Raw Item Responses

data.read: Dataset Reading

data.reck21 data.reck61DAT1 data.reck61DAT2 data.reck73C1a data.reck73C1b data.reck75C2 data.reck78ExA data.reck79ExB: Datasets from Reckase' Book Multidimensional Item Response Theory

data.si01 data.si02 data.si03 data.si04 data.si05 data.si06 data.si07 data.si08 data.si09 data.si10: Some Example Datasets for the sirt Package

data.timss: Dataset TIMSS Mathematics

data.timss07.G8.RUS: TIMSS 2007 Grade 8 Mathematics and Science Russia

data.trees: Dataset Used in Stoyan, Pommerening and Wuensche (2018)

data.wide2long(): Converting a Data Frame from Wide Format in a Long Format

detect.index(): Calculation of the DETECT and polyDETECT Index

dif.logistic.regression(): Differential Item Functioning using Logistic Regression Analysis

dif.strata.variance(): Stratified DIF Variance

dif.variance(): DIF Variance

dirichlet.mle(): Maximum Likelihood Estimation of the Dirichlet Distribution

dirichlet.simul(): Simulation of a Dirichlet Distributed Vectors

dmlavaan(): Comparing Regression Parameters of Different lavaan Models Fitted to the Same Dataset

eigenvalues.manymatrices(): Computation of Eigenvalues of Many Symmetric Matrices

equating.rasch.jackknife(): Jackknife Equating Error in Generalized Logistic Rasch Model

equating.rasch(): Equating in the Generalized Logistic Rasch Model

expl.detect(): Exploratory DETECT Analysis

f1d.irt(): Functional Unidimensional Item Response Model

fit.isop() fit.adisop(): Fitting the ISOP and ADISOP Model for Frequency Tables

fuzcluster() summary(<fuzcluster>): Clustering for Continuous Fuzzy Data

fuzdiscr(): Estimation of a Discrete Distribution for Fuzzy Data (Data in Belief Function Framework)

gom.em() summary(<gom>) anova(<gom>) logLik(<gom>) IRT.irfprob(<gom>) IRT.likelihood(<gom>) IRT.posterior(<gom>) IRT.modelfit(<gom>) summary(<IRT.modelfit.gom>): Discrete (Rasch) Grade of Membership Model

gom.jml(): Grade of Membership Model (Joint Maximum Likelihood Estimation)

greenyang.reliability(): Reliability for Dichotomous Item Response Data Using the Method of Green and Yang (2009)

invariance.alignment() summary(<invariance.alignment>) invariance_alignment_constraints() summary(<invariance_alignment_constraints>) invariance_alignment_simulate() invariance_alignment_cfa_config(): Alignment Procedure for Linking under Approximate Invariance

IRT.mle(): Person Parameter Estimation

isop.dich() isop.poly() summary(<isop>) plot(<isop>): Fit Unidimensional ISOP and ADISOP Model to Dichotomous and Polytomous Item Responses

isop.scoring(): Scoring Persons and Items in the ISOP Model

isop.test() summary(<isop.test>): Testing the ISOP Model

latent.regression.em.raschtype() latent.regression.em.normal() summary(<latent.regression>): Latent Regression Model for the Generalized Logistic Item Response Model and the Linear Model for Normal Responses

lavaan2mirt(): Converting a lavaan Model into a mirt Model

lc.2raters() summary(<lc.2raters>): Latent Class Model for Two Exchangeable Raters and One Item

likelihood.adjustment(): Adjustment and Approximation of Individual Likelihood Functions

linking.haberman() summary(<linking.haberman>) linking.haberman.lq() summary(<linking.haberman.lq>) linking_haberman_itempars_prepare() linking_haberman_itempars_convert() L0_polish(): Linking in the 2PL/Generalized Partial Credit Model

linking.haebara() summary(<linking.haebara>): Haebara Linking of the 2PL Model for Multiple Studies

linking.robust() summary(<linking.robust>) plot(<linking.robust>): Robust Linking of Item Intercepts

lq_fit() lq_fit_estimate_power() dexppow() rexppow(): Fit of a \(L_q\) Regression Model

lsdm() summary(<lsdm>) plot(<lsdm>): Least Squares Distance Method of Cognitive Validation

lsem.estimate() summary(<lsem>) plot(<lsem>) lsem.MGM.stepfunctions() lsem_local_weights() lsem.bootstrap(): Local Structural Equation Models (LSEM)

lsem.permutationTest() summary(<lsem.permutationTest>) plot(<lsem.permutationTest>): Permutation Test for a Local Structural Equation Model

lsem.test(): Test a Local Structural Equation Model Based on Bootstrap

marginal.truescore.reliability(): True-Score Reliability for Dichotomous Data

rowMaxs.sirt() rowMins.sirt() rowCumsums.sirt() colCumsums.sirt() rowIntervalIndex.sirt() rowKSmallest.sirt() rowKSmallest2.sirt(): Some Matrix Functions

mcmc.2pno.ml(): Random Item Response Model / Multilevel IRT Model

mcmc.2pno(): MCMC Estimation of the Two-Parameter Normal Ogive Item Response Model

mcmc.2pnoh(): MCMC Estimation of the Hierarchical IRT Model for Criterion-Referenced Measurement

mcmc.3pno.testlet(): 3PNO Testlet Model

mcmc.list.descriptives(): Computation of Descriptive Statistics for a mcmc.list Object

mcmclist2coda(): Write Coda File from an Object of Class mcmc.list

mcmc_coef() mcmc_vcov() mcmc_confint() mcmc_summary() mcmc_plot() mcmc_derivedPars() mcmc_WaldTest() summary(<mcmc_WaldTest>): Some Methods for Objects of Class mcmc.list

mcmc_Rhat(): Computation of the Rhat Statistic from a Single MCMC Chain

md.pattern.sirt(): Response Pattern in a Binary Matrix

mgsem(): Estimation of Multiple-Group Structural Equation Models

mirt.specify.partable(): Specify or modify a Parameter Table in mirt

mirt.wrapper.coef() mirt_summary() mirt.wrapper.posterior() IRT.likelihood(<SingleGroupClass>) IRT.likelihood(<MultipleGroupClass>) IRT.posterior(<SingleGroupClass>) IRT.posterior(<MultipleGroupClass>) IRT.expectedCounts(<SingleGroupClass>) IRT.expectedCounts(<MultipleGroupClass>) IRT.irfprob(<SingleGroupClass>) IRT.irfprob(<MultipleGroupClass>) mirt.wrapper.fscores() mirt.wrapper.itemplot(): Some Functions for Wrapping with the mirt Package

mle.pcm.group(): Maximum Likelihood Estimation of Person or Group Parameters in the Generalized Partial Credit Model

modelfit.sirt() modelfit.cor.poly() IRT.modelfit(<sirt>): Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations

monoreg.rowwise() monoreg.colwise(): Monotone Regression for Rows or Columns in a Matrix

nedelsky.sim() nedelsky.latresp() nedelsky.irf(): Functions for the Nedelsky Model

noharm.sirt() summary(<noharm.sirt>): NOHARM Model in R

np.dich(): Nonparametric Estimation of Item Response Functions

parmsummary_extend(): Includes Confidence Interval in Parameter Summary Table

pbivnorm2(): Cumulative Function for the Bivariate Normal Distribution

pcm.conversion(): Conversion of the Parameterization of the Partial Credit Model

pcm.fit(): Item and Person Fit Statistics for the Partial Credit Model

person.parameter.rasch.copula(): Person Parameter Estimation of the Rasch Copula Model (Braeken, 2011)

personfit.stat(): Person Fit Statistics for the Rasch Model

pgenlogis() genlogis.moments(): Calculation of Probabilities and Moments for the Generalized Logistic Item Response Model

plausible.value.imputation.raschtype(): Plausible Value Imputation in Generalized Logistic Item Response Model

plot(<mcmc.sirt>): Plot Function for Objects of Class mcmc.sirt

plot(<np.dich>): Plot Method for Object of Class np.dich

polychoric2() sirt_rcpp_polychoric2(): Polychoric Correlation

prior_model_parse(): Parsing a Prior Model

prmse.subscores.scales(): Proportional Reduction of Mean Squared Error (PRMSE) for Subscale Scores

prob.guttman() summary(<prob.guttman>) anova(<prob.guttman>) logLik(<prob.guttman>) IRT.irfprob(<prob.guttman>) IRT.likelihood(<prob.guttman>) IRT.posterior(<prob.guttman>): Probabilistic Guttman Model

Q3(): Estimation of the \(Q_3\) Statistic (Yen, 1984)

Q3.testlet(): \(Q_3\) Statistic of Yen (1984) for Testlets

qmc.nodes(): Calculation of Quasi Monte Carlo Integration Points

R2conquest() summary(<R2conquest>) read.show() read.show.term() read.show.regression() read.pv() read.multidimpv() read.pimap(): Running ConQuest From Within R

R2noharm.EAP(): EAP Factor Score Estimation

R2noharm.jackknife() summary(<R2noharm.jackknife>): Jackknife Estimation of NOHARM Analysis

R2noharm() summary(<R2noharm>): Estimation of a NOHARM Analysis from within R

rasch.copula2() rasch.copula3() summary(<rasch.copula2>) summary(<rasch.copula3>) anova(<rasch.copula2>) anova(<rasch.copula3>) logLik(<rasch.copula2>) logLik(<rasch.copula3>) IRT.likelihood(<rasch.copula2>) IRT.likelihood(<rasch.copula3>) IRT.posterior(<rasch.copula2>) IRT.posterior(<rasch.copula3>): Multidimensional IRT Copula Model

rasch.evm.pcm() summary(<rasch.evm.pcm>) coef(<rasch.evm.pcm>) vcov(<rasch.evm.pcm>): Estimation of the Partial Credit Model using the Eigenvector Method

rasch.jml.biascorr(): Bias Correction of Item Parameters for Joint Maximum Likelihood Estimation in the Rasch model

rasch.jml.jackknife1(): Jackknifing the IRT Model Estimated by Joint Maximum Likelihood (JML)

rasch.jml() summary(<rasch.jml>): Joint Maximum Likelihood (JML) Estimation of the Rasch Model

rasch.mirtlc() summary(<rasch.mirtlc>) anova(<rasch.mirtlc>) logLik(<rasch.mirtlc>) IRT.irfprob(<rasch.mirtlc>) IRT.likelihood(<rasch.mirtlc>) IRT.posterior(<rasch.mirtlc>) IRT.modelfit(<rasch.mirtlc>) summary(<IRT.modelfit.rasch.mirtlc>): Multidimensional Latent Class 1PL and 2PL Model

rasch.mml2() summary(<rasch.mml>) plot(<rasch.mml>) anova(<rasch.mml>) logLik(<rasch.mml>) IRT.irfprob(<rasch.mml>) IRT.likelihood(<rasch.mml>) IRT.posterior(<rasch.mml>) IRT.modelfit(<rasch.mml>) IRT.expectedCounts(<rasch.mml>) summary(<IRT.modelfit.rasch.mml>): Estimation of the Generalized Logistic Item Response Model, Ramsay's Quotient Model, Nonparametric Item Response Model, Pseudo-Likelihood Estimation and a Missing Data Item Response Model

rasch.pairwise.itemcluster(): Pairwise Estimation of the Rasch Model for Locally Dependent Items

rasch.pairwise() summary(<rasch.pairwise>): Pairwise Estimation Method of the Rasch Model

rasch.pml3() summary(<rasch.pml>): Pairwise Marginal Likelihood Estimation for the Probit Rasch Model

rasch.prox(): PROX Estimation Method for the Rasch Model

rasch.va(): Estimation of the Rasch Model with Variational Approximation

reliability.nonlinearSEM(): Estimation of Reliability for Confirmatory Factor Analyses Based on Dichotomous Data

resp_groupwise(): Creates Group-Wise Item Response Dataset

rinvgamma2() dinvgamma2(): Inverse Gamma Distribution in Prior Sample Size Parameterization

rm.facets() summary(<rm.facets>) anova(<rm.facets>) logLik(<rm.facets>) IRT.irfprob(<rm.facets>) IRT.factor.scores(<rm.facets>) IRT.likelihood(<rm.facets>) IRT.posterior(<rm.facets>) IRT.modelfit(<rm.facets>) summary(<IRT.modelfit.rm.facets>) rm_proc_data(): Rater Facets Models with Item/Rater Intercepts and Slopes

rm.sdt() summary(<rm.sdt>) plot(<rm.sdt>) anova(<rm.sdt>) logLik(<rm.sdt>) IRT.factor.scores(<rm.sdt>) IRT.irfprob(<rm.sdt>) IRT.likelihood(<rm.sdt>) IRT.posterior(<rm.sdt>) IRT.modelfit(<rm.sdt>) summary(<IRT.modelfit.rm.sdt>): Hierarchical Rater Model Based on Signal Detection Theory (HRM-SDT)

rmvn() ruvn(): Simulation of a Multivariate Normal Distribution with Exact Moments

scale_group_means() predict_scale_group_means(): Scaling of Group Means and Standard Deviations

sia.sirt(): Statistical Implicative Analysis (SIA)

sim.qm.ramsay(): Simulate from Ramsay's Quotient Model

sim.rasch.dep(): Simulation of the Rasch Model with Locally Dependent Responses

sim.raschtype(): Simulate from Generalized Logistic Item Response Model

rasch.conquest() rasch.pml2() testlet.yen.q3() yen.q3(): Defunct sirt Functions

sirt-package sirt: Supplementary Item Response Theory Models

bounds_parameters() dimproper() ginverse_sym() hard_thresholding() soft_thresholding() pow() tracemat() sirt_matrix2() sirt_colMeans() sirt_colSDs() sirt_colMins() sirt_colMaxs() sirt_colMedians() sirt_sum_norm() sirt_dnorm_discrete() sirt_rbind_fill() sirt_fisherz() sirt_antifisherz() sirt_abs_smooth() sirt_permutations() sirt_attach_list_elements() sirt_optimizer() sirt_summary_print_objects() sirt_summary_print_package_rsession() sirt_summary_print_package() sirt_summary_print_rsession() sirt_summary_print_call() print_digits() sirt_rcpp_discrete_inverse() move_variables_df(): Utility Functions in sirt

sirt_eigenvalues(): First Eigenvalues of a Symmetric Matrix

smirt() summary(<smirt>) anova(<smirt>) logLik(<smirt>) IRT.irfprob(<smirt>) IRT.likelihood(<smirt>) IRT.posterior(<smirt>) IRT.modelfit(<smirt>) summary(<IRT.modelfit.smirt>): Multidimensional Noncompensatory, Compensatory and Partially Compensatory Item Response Model

stratified.cronbach.alpha(): Stratified Cronbach's Alpha

summary(<mcmc.sirt>): Summary Method for Objects of Class mcmc.sirt

tam2mirt(): Converting a fitted TAM Object into a mirt Object

testlet.marginalized(): Marginal Item Parameters from a Testlet (Bifactor) Model

tetrachoric2(): Tetrachoric Correlation Matrix

truescore.irt(): Conversion of Trait Scores \(\theta\) into True Scores \(\tau ( \theta )\)

unidim.test.csn(): Test for Unidimensionality of CSN

wle.rasch.jackknife(): Standard Error Estimation of WLE by Jackknifing

wle.rasch(): Weighted Likelihood Estimation of Person Abilities

xxirt() summary(<xxirt>) print(<xxirt>) anova(<xxirt>) coef(<xxirt>) logLik(<xxirt>) vcov(<xxirt>) confint(<xxirt>) IRT.expectedCounts(<xxirt>) IRT.factor.scores(<xxirt>) IRT.irfprob(<xxirt>) IRT.likelihood(<xxirt>) IRT.posterior(<xxirt>) IRT.modelfit(<xxirt>) summary(<IRT.modelfit.xxirt>) IRT.se(<xxirt>) xxirt_hessian() xxirt_sandwich_pml(): User Defined Item Response Model

xxirt_createDiscItem() xxirt_createParTable() xxirt_modifyParTable(): Create Item Response Functions and Item Parameter Table

xxirt_createThetaDistribution(): Creates a User Defined Theta Distribution