This is a alternative implementation of the
function for when each event is has two ratings from k available raters.
For each event, one rater is randomly selected and given a rating from the
distribution defined by the
response.probs parameter. A second randomly
selected rater will have the same rating with a probability defined by the
simulateRatingMatrix2(nLevels, k, agree, nEvents = 100, response.probs = rep(1/nLevels, nLevels))
the number of possible outcomes there are for each rating.
the total number of available raters.
the percent of time the raters agree. Note that the actual agreement of the simulated matrix will vary from this value (see sample).
the number of rating events within each matrix.
probability weights for the distribution of scores. By default, each of the levels has equal probability of being selected. This allows situations where some responses are more common than others (e.g. 50% of students get a 3, 30% get a 2, and 20% get a 1). This is independent of the percent agreement parameter.