rpf.drm and polytomous
(graded response rpf.grm, partial
credit/generalized partial credit rpf.gpcm,
nominal rpf.nrm, and multiple-choice model
rpf.mcm) items. Both unidimensional and
multidimensional versions of the models are available.Item model parameters are passed around as a numeric vector. A 1D matrix is also acceptable. Regardless of model, parameters are always ordered as follows: discrimination ("a"), difficulty ("b"), and guessing ("c"). If person ability ranges from low negative to high positive then probabilities are output from incorrect to correct. That is, a low ability person (e.g., ability = -2) will be more likely to get an item incorrect than correct. For example, a dichotomous model that returns [.25, .75] indicates a probability of .25 for incorrect and .75 for correct. A polytomous model will have the most incorrect probability at index 1 and the most correct probability at the maximum index.
All models are always in the logistic metric. To obtain
normal ogive discrimination parameters, divide slope
parameters by rpf.ogive. Item models are
estimated in slope-intercept form unless the traditional
parameterization is specifically requested.
This package could also accrete functions to support plotting (but not the actual plot functions).
rpf.rparam to create item parameters.