classify: Classification Accuracy and Consistency under IRT models.
Version 1.3

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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AuthorDr Chris Wheadon and Dr Ian Stockford
Date of publication2014-08-17 12:17:00
MaintainerDr Chris Wheadon <chris.wheadon@gmail.com>
LicenseGPL (>= 2)
Version1.3
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("classify")

Man pages

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

Functions

across-reps Man page
across.reps Man page
across.reps,classification-method Man page
across.reps-methods Man page
beta.list Man page Source code
biology Man page
classification Man page
classification-class Man page
classify Man page Source code
classify-package Man page
classify.bug Man page Source code
expected.rc Man page Source code
gpcm Man page Source code
gpcm.bug Man page Source code
gpcm.rc Man page Source code
pcm Man page Source code
physics Man page
plot.classification Man page Source code
plot.scores Man page Source code
rasch Man page Source code
scores,missing-method Man page
scores-class Man page
scores.gpcm.bug Man page Source code
summary Man page
summary,classification-method Man page
summary-methods Man page
thpl Man page Source code
tpl Man page Source code
wlord Man page Source code

Files

inst
inst/CITATION
inst/bugs
inst/bugs/rasch.bug
inst/bugs/gpcm.bug
inst/bugs/tpl.bug
inst/bugs/pcm.bug
src
src/Makevars
src/exp.cpp
src/rcpp_w_lord.h
src/rcpp_w_lord.cpp
src/Makevars.win
src/gpcm.cpp
src/gpcm.h
src/exp.h
NAMESPACE
data
data/biology.rda
data/datalist
data/physics.rda
R
R/bugs.R
R/w_lord.R
R/scores.R
R/prob_functions.R
R/classify.R
R/gpcm.rc.R
MD5
DESCRIPTION
man
man/gpcm.bug.Rd
man/rasch.Rd
man/gpcm.Rd
man/summary-methods.Rd
man/thpl.Rd
man/w_lord.Rd
man/plot-methods.Rd
man/classify-package.Rd
man/physics.Rd
man/scores-class.Rd
man/gpcm.rc.Rd
man/classify.Rd
man/across.reps-methods.Rd
man/classify.bug.Rd
man/scores.gpcm.bug.Rd
man/classification-class.Rd
man/pcm.Rd
man/biology.Rd
man/expected.rc.Rd
man/beta.list.Rd
man/tpl.Rd
classify documentation built on May 29, 2017, 7:49 p.m.