Given response data that form a connected design, compute the probability to pass on the reference set conditional on each score on one or more target tests.
probability_to_pass( dataSrc, parms, ref_items, pass_fail, predicate = NULL, target_booklets = NULL, nDraws = 1000 )
a connection to a dexter database, a matrix, or a data.frame with columns: person_id, item_id, item_score
parameters returned from fit_enorm. If uncertainty about parameter estimation should be included in the computations, use `method='Bayes'` and nDraws equal or larger than nDraws in probability_to_pass
vector with id's of items in the reference set, they must all occur in dataSrc
pass-fail score on the reference set, the lowest score with which one passes
An optional expression to subset data in dataSrc, if NULL all data is used
The target test booklet(s). A data.frame with columns booklet_id (if multiple booklets) and item_id, if NULL (default) this will be derived from the dataSrc and the probability to pass will be computed for each test score for each booklet in your data.
The function uses an Markov-Chain Monte-Carlo method to calculate the probability to pass and this is the number of Monte-Carlo samples used.
An object of type
coef() to extract the
probablity to pass for each booklet and score. Use
plot() to plot
the probabilities, sensitivity and specificity or a ROC-curve.
Note that this function is computationally intensive and can take some time to run, especially when computing the probability to pass for multiple target booklets. Further technical details can be found in a vignette.
The function used to plot the results: