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

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/ Raiche, G., Magis, D., Blais, J.-G., and Brochu, P. (2013). Taking atypical response patterns into account: a multidimensional measurement model from item response theory. In M. Simon, K. Ercikan, and M. Rousseau (Eds), Improving large-scale assessment in education. New York, New York: Routledge.

Examples

Run this code

## Not run: 
# ## 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
# ## End(Not run)