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.

Author | Dr Chris Wheadon and Dr Ian Stockford |

Date of publication | 2014-08-17 12:17:00 |

Maintainer | Dr Chris Wheadon <chris.wheadon@gmail.com> |

License | GPL (>= 2) |

Version | 1.3 |

**across.reps-methods:** Summarises classification values across bugs or jags...

**beta.list:** Extract Beta Values from Bugs Sims File

**biology:** Polytomous Responses from 200 Candidates to 31 Questions

**classification-class:** Class '"classification"'

**classify:** Calculate Classification Statistics

**classify.bug:** Classification Accuracy and Consistency from Bugs Replicate...

**classify-package:** Classification Accuracy and Consistency under IRT models.

**expected.rc:** Expected scores under the PCM or the GPCM.

**gpcm:** Generalised Partial Credit Model Derived Probabilities

**gpcm.bug:** Extract IRT Model Parameters from Bugs Models

**gpcm.rc:** IRT Derived Predicted Conditional Number Correct Score...

**pcm:** Partial Credit Model Derived Probabilities

**physics:** Dichotomous Responses from 200 Candidates to 25 Questions

**plot-methods:** Plot Methods for Classification and Scores s4 objects

**rasch:** Rasch Derived Probabilities

**scores-class:** Class '"scores"'

**scores.gpcm.bug:** Expected and Conditional Summed Score Distributions

**summary-methods:** Summary Statistics for S4 Class Classification

**thpl:** Three Parameter IRT Model Derived Probabilities

**tpl:** Two Parameter IRT Model Derived Probabilities

**w_lord:** Lord and Wingersky Recursion Formula

classify

classify/inst

classify/inst/CITATION

classify/inst/bugs

classify/inst/bugs/rasch.bug

classify/inst/bugs/gpcm.bug

classify/inst/bugs/tpl.bug

classify/inst/bugs/pcm.bug

classify/src

classify/src/Makevars

classify/src/exp.cpp

classify/src/rcpp_w_lord.h

classify/src/rcpp_w_lord.cpp

classify/src/Makevars.win

classify/src/gpcm.cpp

classify/src/gpcm.h

classify/src/exp.h

classify/NAMESPACE

classify/data

classify/data/biology.rda

classify/data/datalist

classify/data/physics.rda

classify/R

classify/R/bugs.R
classify/R/w_lord.R
classify/R/scores.R
classify/R/prob_functions.R
classify/R/classify.R
classify/R/gpcm.rc.R
classify/MD5

classify/DESCRIPTION

classify/man

classify/man/gpcm.bug.Rd
classify/man/rasch.Rd
classify/man/gpcm.Rd
classify/man/summary-methods.Rd
classify/man/thpl.Rd
classify/man/w_lord.Rd
classify/man/plot-methods.Rd
classify/man/classify-package.Rd
classify/man/physics.Rd
classify/man/scores-class.Rd
classify/man/gpcm.rc.Rd
classify/man/classify.Rd
classify/man/across.reps-methods.Rd
classify/man/classify.bug.Rd
classify/man/scores.gpcm.bug.Rd
classify/man/classification-class.Rd
classify/man/pcm.Rd
classify/man/biology.Rd
classify/man/expected.rc.Rd
classify/man/beta.list.Rd
classify/man/tpl.Rd
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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