This function estimates the multiplechoice DINA model (de la Torre, 2009).
MCmodel(dat, Qc, model = "MCDINA", key = NULL, conv.crit = 0.001, maxitr = 2000, conv.type = "pr")
dat  A required \(N \times J\) data matrix of N examinees to J items. Values must be 1, 2,... representing nominal categories. Missing values are currently not allowed. 

Qc  A required category and attribute association matrix. The first column gives the item number, which must be numeric (i.e., 1,2,...) and match the number of column in the data. The second column indicates the coded category of each item. The number in the second column must match with the number in the data, but if a category is not coded, it should not be included in the Qmatrix. Entry 1 indicates that the attribute is measured by the category, and 0 otherwise. Note that the MCDINA model assumes that the category with the largest number of 1s is the key and that the coded distractors should allow to assign examinees uniquely. 
model 

key  a numeric vector giving the key of each item. See 
conv.crit  The convergence criterion for max absolute change in 
maxitr  The maximum iterations allowed. 
conv.type  convergence criteria; Can be 
an object of class MCmodel
with the following components:
A list of success probabilities for each reduced latent class on each item (IRF)
A list of standard errors of item parameters
A list of estimated attribute profiles including EAP, MLE and MAP estimates.
A list of test fit statistics including deviance, number of parameters, AIC and BIC
expected # of individuals in each latent group choosing each option
posterior probability
Total # of iterations
De La Torre, J. (2009). A cognitive diagnosis model for cognitively based multiplechoice options. Applied Psychological Measurement, 33, 163183.
GDINA
for GDINA model
if (FALSE) { # check the format of the data # Entry 0 is not allowed head(sim10MCDINA$simdat) # # check the format of the Qmatrix # # Take item 1 as an example: # category 2 has a qvector (1,0,0) # category 1 has a qvector (0,1,0) # category 4 has a qvector (1,1,0) # category 3 is not included in the Qmatrix because it is not coded # the order of the coded categories in the Qmatrix doesn't matter sim10MCDINA$simQ # Item coded cat A1 A2 A3 # 1 2 1 0 0 # 1 1 0 1 0 # 1 4 1 1 0 #... est < MCmodel(sim10MCDINA$simdat,sim10MCDINA$simQ) est est$testfit # # Distractors involving more attributes # # some distractors may involve attributes that are not invovled by the key option # this is not allowed by the "original" MCDINA (de la Torre, 2009) but is allowed # in the current implementation # Users need to specify the key for each item to appropriate handle such an issue # Note item 1 below: category 1 is the key (as indicated in the key argument below) # The distractor (category 4) involves an attribute not included by the key option Qc < matrix(c(1, 1, 1, 1, 0, 1, 2, 0, 1, 0, 1, 3, 1, 0, 0, 1, 4, 1, 0, 1, 2, 1, 1, 0, 0, 2, 3, 1, 1, 0, 2, 2, 1, 1, 1, 3, 4, 1, 1, 1, 3, 2, 1, 1, 0, 3, 3, 0, 1, 1, 4, 1, 0, 1, 1, 4, 2, 0, 0, 1, 5, 1, 1, 0, 0, 6, 3, 0, 1, 0, 7, 2, 0, 0, 1, 8, 4, 1, 0, 0, 9, 1, 0, 1, 0, 10, 4, 0, 0, 1),ncol = 5,byrow = TRUE) est2 < MCmodel(sim10MCDINA$simdat,Qc, key = c(1,2,4,1,1,3,2,4,1,4)) est2 est2$prob.parm est2$testfit est2$attribute }