irtProb (version 1.0)

m4plModelShow: Results For Each Subject To Each Model

Description

Show all the information about the estimation of all the possible m4pl models for each subjects.

Usage

m4plModelShow(x, ...)

Arguments

x

data.frame: a matrix of binary 0-1 item responses.

...

varying: parameters to be passed to the m4plPersonParameters function.

Value

• ID
  integer: subject identificator.
• MODEL
  charavter: model identification (T,TS,TC,TD,TSC,TSD,TCD or TSCD)
• LL
  numeric: loglikelihood.
• AIC
  numeric: Akaike information criteria.
• BIC
  numeric: Bayes (Schwartz) information criteria.
• T
  numeric: theta parameter value.
• SeT
  numeric: theta parameter theoretical standard error.
• S
  numeric: person fluctuation parameter value.
• SeS
  numeric: person fluctuation theoretical standard error
• C
  numeric: person pseudo-guessing parameter value.
• SeC
  numeric: person pseudo-guessing theoretical standard error
• D
  numeric: person inattention parameter value.
• SeD
  numeric: person inattention theoretical standard error

References

Blais, J.-G., Raiche, G. and Magis, D. (2009). La detection des patrons de reponses problematiques dans le contexte des tests informatises. In Blais, J.-G. (Ed.): Evaluation des apprentissages et technologies de l'information et de la communication : enjeux, applications et modeles de mesure. Ste-Foy, Quebec: Presses de l'Universite Laval. Raiche, G., Magis, D. and Beland, S. (2009). La correction du resultat d'un etudiant en presence de tentatives de fraudes. Communication presentee a l'Universite du Quebec a Montreal. Retrieved from http://www.camri.uqam.ca/camri/camriBase/ Raiche, G., Magis, D. and Blais, J.-G. (2008). Multidimensional item response theory models integrating additional inattention, pseudo-guessing, and discrimination person parameters. Communication at the annual international Psychometric Society meeting, Durham, New Hamshire. Retrieved from http://www.camri.uqam.ca/camri/camriBase/

See Also

m4plPersonParameters

Examples

## GENERATION OF VECTORS OF RESPONSES
 # NOTE THE USUAL PARAMETRIZATION OF THE ITEM DISCRIMINATION,
 # THE VALUE OF THE PERSONNAL FLUCTUATION FIXED AT 0,
 # AND THE VALUE OF THE PERSONNAL PSEUDO-GUESSING FIXED AT 0.30.
 # IT COULD BE TYPICAL OF PLAGIARISM BEHAVIOR.
 nItems <- 40
 a      <- rep(1.702,nItems); b <- seq(-5,5,length=nItems)
 c      <- rep(0,nItems); d <- rep(1,nItems)
 nSubjects <- 1; rep <- 100
 theta     <- seq(-1,-1,length=nSubjects)
 S         <- runif(n=nSubjects,min=0.0,max=0.0)
 C         <- runif(n=nSubjects,min=0.3,max=0.3)
 D         <- runif(n=nSubjects,min=0.0,max=0.0)
 set.seed(seed = 100)
 X         <- ggrm4pl(n=nItems, rep=rep,
                      theta=theta, S=S, C=C, D=D,
                      s=1/a, b=b,c=c,d=d)

## Results for each subjects for each models
 essai <- m4plModelShow(X, b=b, s=1/a, c=c, d=d, m=0, prior="uniform")
 
## Mean results for some speficic models
 median(essai[which(essai$MODEL == "TSCD") ,]$SeT, na.rm=TRUE)
 mean(  essai[which(essai$MODEL == "TSCD") ,]$SeT, na.rm=TRUE)
 mean(  essai[which(essai$MODEL ==   "TD") ,]$SeT, na.rm=TRUE)
 sd(    essai[which(essai$MODEL ==   "TD") ,]$T, na.rm=TRUE)
 
## Result for each models for the first subject
 essai[which(essai$ID == 1) ,]
 max(essai[which(essai$ID == 1) ,]$LL)

## Difference between the estimated values with the T and TSCD models for the
## first subject
 essai[which(essai$ID == 1 & essai$MODEL == "T"),]$T
       - essai[which(essai$ID == 1 & essai$MODEL == "TSCD"),]$T