rpf.drm and polytomous (graded
response rpf.grm, partial credit/generalized
partial credit (via the nominal model), and nominal
rpf.nrm items.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/slope ("a"), difficulty/intercept ("b"), and pseudo guessing/upper-bound ("g"/"u"). If person ability ranges from negative to 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. Input/output matrices
arranged in the way most convenient for low-level
processing in C. The maximum absolute logit is 35 because
f(x) := 1-exp(x) loses accuracy around f(-35) and equals 1
at f(-38) due to the limited accuracy of double precision
floating point.
This package could also accrete functions to support plotting (but not the actual plot functions).
rpf.rparam to create item parameters.