The function sim.gdina.prepare creates necessary design matrices Mj, Aj and necc.attr. In most cases, only the list of item parameters delta must be modified by the user when applying the simulation function sim.gdina. The distribution of latent classes α is represented by an underlying multivariate normal distribution α^ast for which a mean vector thresh.alpha and a covariance matrix cov.alpha must be specified. Alternatively, a matrix of skill classes alpha can be given as an input.
The function mcdina implements the multiple choice DINA model (de la Torre, 2009) for multiple groups. Note that the dataset must contain integer values 1,… , K_j for each item. The multiple choice DINA model assumes that each item category possesses some diagnostic capacity. Using this modeling approach, different distractors of a multiple choice item can be of different diagnostic value. The Q-matrix can also contain integer values which allows the definition of polytomous attributes.
● Data Source:
CranContrib
● Keywords: DINA, Multiple choice, Multiple choice DINA model (MCDINA), print, summary
● Alias: mcdina, print.mcdina, summary.mcdina
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Q-matrix entries can be modified by the Q-matrix validation method of de la Torre (2008). After estimating a mixed DINA/DINO model using the din function, item parameters and the item discrimination parameters IDI_j are recalculated. Q-matrix rows are determined by maximizing the estimated item discrimination index IDI_j = 1-s_j -g_j.
This function computes several measures of absolute model fit and local dependence indices for dichotomous item responses which are based on comparing observed and expected frequencies of item pairs (Chen, de la Torre & Zhang, 2013; see Details).
● Data Source:
CranContrib
● Keywords: Local dependence, Model fit, summary
● Alias: modelfit.cor, modelfit.cor.din, modelfit.cor2, summary.modelfit.cor.din
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Performs the generalized distance discriminating method (GDD; Sun, Xin, Zhang, & de la Torre, 2013) for dichotomous data which is a method for classifying students into skill profiles based on a preliminary unidimensional calibration.
Computes the S-X2 item fit statistic (Orlando & Thissen; 2000, 2003) for dichotomous data. Note that completely observed data is necessary for applying this function.
This function constructs dichotomous pseudo items from polytomous ordered items (Tutz, 1997). Using this method, developed test models for dichotomous data can be applied for polytomous item responses after transforming them into dichotomous data. See Details for the construction.
● Data Source:
CranContrib
● Keywords: Sequential item response model
● Alias: sequential.items
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providing 
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