MDIFF
(Package: mirt) :
Compute multidimensional difficulty index
Returns a matrix containing the MDIFF values (Reckase, 2009). Only suppored for items of class 'dich' and 'graded'. Note that the logistic intercept parameters are divided by 1.702 to match the normal ogive metric.
averageMI
(Package: mirt) :
Collapse values from multiple imputation draws
This function computes updated parameter and standard error estimates using multiple imputation methodology. Given a set of parameter estimates and their associated standard errors the function returns the weighted average of the overall between and within variability due to the multiple imputations according to Rubin's (1987) methodology.
itemfit calculates the Zh values from Drasgow, Levine and Williams (1985), χ^2 and G^2 values for unidimensional models, and S-X2 statistics for unidimensional and multidimensional models (Kang & Chen, 2007; Orlando & Thissen, 2000). For Rasch, partial credit, and rating scale models infit and outfit statistics are also produced. Poorly fitting items should be inspected with the empirical plots/tables for unidimensional models, otherwise itemGAM can be used to diagnose where the functional form of the IRT model was misspecified.
expand.table
(Package: mirt) :
Expand summary table of patterns and frequencies
The expand.table function expands a summary table of unique response patterns to a full sized data-set. The response frequencies must be on the rightmost column of the input data.
● Data Source:
CranContrib
● Keywords: data
● Alias: expand.table
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Computes MAP, EAP, ML (Embretson & Reise, 2000), EAP for sum-scores (Thissen et al., 1995), or WLE (Warm, 1989) factor scores with a multivariate normal prior distribution using equally spaced quadrature. EAP scores for models with more than three factors are generally not recommended since the integration grid becomes very large, resulting in slower estimation and less precision if the quadpts are too low. Therefore, MAP scores should be used instead of EAP scores for higher dimensional models. Multiple imputation variants are possible for each estimator if a parameter information matrix was computed, which are useful if the sample size/number of items were small. As well, if the model contained latent regression predictors this information will be used in computing MAP and EAP estimates (for these models, full.scores=TRUE will always be used). Finally, plausible value imputation is also available, and will also account for latent regression predictor effects.
mirt fits an unconditional maximum likelihood factor analysis model to any mixture of dichotomous and polytomous data under the item response theory paradigm using either Cai's (2010) Metropolis-Hastings Robbins-Monro (MHRM) algorithm or with an EM algorithm approach outlined by Bock and Aiken (1981) using rectangular or quasi-Monte Carlo integration grids. Models containing 'explanatory' person or item level predictors can only be included by using the mixedmirt function, though latent regression models can be fit using the formula input below. Tests that form a two-tier or bi-factor structure should be estimated with the bfactor function, which uses a dimension reduction EM algorithm for modeling item parcels. Multiple group analyses (useful for DIF and DTF testing) are also available using the multipleGroup function.
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