Computes classification accuracy and consistency with Lee's approach. The probability of each possible total score conditional on ability is found with `recursive.raw`

. Those probabilities are grouped according to the cut scores and used to estimate the indices. See references or code for details.

1 2 3 |

`cutscore` |
A scalar or vector of cut scores on the True Score scale. If you have cut scores on the theta scale, you can transform them with |

`ip` |
Matrix of item parameters, columns are discrimination, difficultly, guessing, respectively. For 1PL and 2PL, still give a Jx3 matrix, with |

`ability, theta` |
Ability estimates for each subject. |

`rdm` |
The response data matrix with rows as subjects and columns as items |

`quadrature` |
A list containing 1) The quadrature points and 2) Their corresponding weights |

`D` |
Scaling constant for IRT parameters, defaults to 1.7, alternatively often set to 1. |

Must give only one ability, rdm, or quadrature. If ability is given, those scores are used for the P method. If rdm is given, ability is estimated with MLE (perfect response patterns given a -4 or 4) and used for the P method. If quadrature, the D method is used. `class.Lee`

calls `Lee.D`

or `Lee.P`

.

`Marginal` |
A matrix with two columns of marginal accuracy and consistency per cut score (and simultaneous if multiple cutscores are given) |

`Conditional` |
A list of two matrixes, one for conditional accuracy and one for conditional consistency. Each matrix has one row per subject (or quadrature point). |

In order to score above a cut, an examinee must score at or above the cut score. Since we are working on the total score scale, be aware that if a cut score is given with a decimal (like 2.4), the examinee must have a total score at the next integer or more (so 3 or more) to score above the cut.

Quinn N. Lathrop

Lee, W. (2010) Classification consistency and accuracy for complex assessments using item response theory. Journal of Educational Measurement, 47, 1–17.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
##from rdm, item parameters denote 4 item 1PL test, cut score at x=2
##only print marginal indices
params<-matrix(c(1,1,1,1,-2,1,0,1,0,0,0,0),4,3)
rdm<-sim(params, rnorm(100))
class.Lee(2, params, rdm = rdm)$Marginal
##or from 40 quadrature points and weights, 2 cut scores
quad <- normal.qu(40)
class.Lee(c(2,3), params, quadrature = quad, D = 1)$Marginal
``` |

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