rpf.logprob: Map an item model, item parameters, and person trait score into a
probability vector
Description
Note that in general, exp(rpf.logprob(..)) != rpf.prob(..) because
the range of logits is much wider than the range of probabilities
due to limitations of floating point numerical precision.
Usage
rpf.logprob(m, param, theta)
Value
a vector of probabilities. For dichotomous items,
probabilities are returned in the order incorrect, correct.
Although redundent, both incorrect and correct probabilities are
returned in the dichotomous case for API consistency with
polytomous item models.