Classification Accuracy and Consistency under IRT models.

IRT classification uses the probability that candidates of a given ability, will answer correctly questions of a specified difficulty to calculate the probability of their achieving every possible score in a test. Due to the IRT assumption of conditional independence (that is every answer given is assumed to depend only on the latent trait being measured) the probability of candidates achieving these potential scores can be expressed by multiplication of probabilities for item responses for a given ability. Once the true score and the probabilities of achieving all other scores have been determined for a candidate the probability of their score lying in the same category as that of their true score (classification accuracy), or the probability of consistent classification in a category over administrations (classification consistency), can be calculated.


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install.packages("classify")

1.3 by Dr Chris Wheadon, 3 years ago


Browse source code at https://github.com/cran/classify


Authors: Dr Chris Wheadon and Dr Ian Stockford


Documentation:   PDF Manual  


Task views: Psychometric Models and Methods


GPL (>= 2) license


Imports Rcpp, plyr, ggplot2, lattice, methods, R2jags, reshape2

Suggests R2WinBUGS

Linking to Rcpp


See at CRAN