Returns data matrix based on model probabilites. So far implemented for dichotomous models only.
Usage
## S3 method for class 'ppar':
predict(object, cutpoint = "randomized", ...)
Arguments
object
Object of class ppar (from person.parameter()).
cutpoint
Either single integer value between 0 and 1 or "randomized" for randomized 0-1 assignment (see details)
...
Additional arguments ignored
Value
Returns data matrix based on model probabilities
Details
A randomized assignment implies that for each cell an additional random number is drawn. If the model probability is larger than this value, the person gets 1 on this particular item, if smaller, 0 is assigned. Alternatively, a numeric probability cutpoint can be assigned and the 0-1 scoring is carried out according to the same rule.