eval_likelihood.Rd
The function eval_likelihood
evaluates the likelihood given item
responses and item response probabilities.
The function prep_data_long_format
stores the matrix of
item responses in a long format omitted all missing responses.
eval_likelihood(data, irfprob, prior=NULL, normalization=FALSE, N=NULL) prep_data_long_format(data)
data | Dataset containing item responses in wide format or long format
(generated by |
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irfprob | Array containing item responses probabilities, format
see |
prior | Optional prior (matrix or vector) |
normalization | Logical indicating whether posterior should be normalized |
N | Number of persons (optional) |
Numeric matrix
if (FALSE) { ############################################################################# # EXAMPLE 1: Likelihood data.ecpe ############################################################################# data(data.ecpe, package="CDM") dat <- data.ecpe$dat[,-1] Q <- data.ecpe$q.matrix #*** store data matrix in long format data_long <- CDM::prep_data_long_format(data) str(data_long) #** estimate GDINA model mod <- CDM::gdina(dat, q.matrix=Q) summary(mod) #** extract data, item response functions and prior data <- CDM::IRT.data(mod) irfprob <- CDM::IRT.irfprob(mod) prob_theta <- attr( irfprob, "prob.theta") #** compute likelihood lmod <- CDM::eval_likelihood(data=data, irfprob=irfprob) max( abs( lmod - CDM::IRT.likelihood(mod) )) #** compute posterior pmod <- CDM::eval_likelihood(data=data, irfprob=irfprob, prior=prob.theta, normalization=TRUE) max( abs( pmod - CDM::IRT.posterior(mod) )) }