Last data update: 2014.03.03

R: Summary of the Results of Estimation with the m4pl Models
m4plSummaryR Documentation

Summary of the Results of Estimation with the m4pl Models

Description

Summary of the results of estimation with the m4pl models.

Usage

 m4plSummary(      X, ...)

 m4plMoreSummary(  x, out = "result", thetaInitial = NULL)

 m4plNoMoreSummary(x)
 

Arguments

X

data.frame or list: if a list results from m4plPersonParameters function, if a data.frame, any all numeric data.frame.

x

list: result from m4plPersonParameters with more set to TRUE.

out

character: if out="results", the output is for each subjects. If out="report", statistics on all results are computed.

thetaInitial

numeric: if initial theta valeus are used the error of estimation is also reported.

...

generic: to be able to pass parameters from the m4plMoreSummary function.

Value

..............

m4plSummary

..............

The result of m4plSummary depends of the out condition and the class of X. If X is a data.frame, the function m4plNoMoreSummary is called and a data.frame with 2 rows is returned: mean and sd rows.

If out="result" and X is a list, the function m4plMoreSummary is called and a data.frame with the mean of the parameters and their theoretical standard errors is returned:

If out="report" and X is a list, m4plMoreSummary is called and the following list taking in account each parameters is returned:

parameters

data.frame: with mean, median, sd an N observations for each parameters.

se

data.frame: with mean, median, sd an N observations for the theoretical values of the standard error for each parameters.

logLikelihood

data.frame: mean, median, sd an N observations loglikelihood, AIC and BIC for the model.

eCorrelation

matrix: empirical correlations between the parameters.

tCorrelation

matrix: theoretical correlations between the parameters.

..............

m4plNoMoreSummary

..............

A data.frame with 2 rows is returned: mean and sd rows.

..............

m4plMoreSummary

..............

All other outputs from the m4plSummary function.

Author(s)

Gilles Raiche, Universite du Quebec a Montreal (UQAM),

Departement d'education et pedagogie

Raiche.Gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/

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.

See Also

m4plPersonParameters

Examples


## GENERATION OF VECTORS OF RESPONSE
 # 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.
 nSubjects <- 1
 nItems <- 40
 a      <- rep(1.702,nItems); b <- seq(-5,5,length=nItems)
 c      <- rep(0,nItems); d <- rep(1,nItems)
 theta     <- seq(-2,-2,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)
 rep <- 100
 set.seed(seed = 10)
 X         <- ggrm4pl(n=nItems, rep=rep,
                      theta=theta, S=S, C=C, D=D,
                      s=1/a, b=b,c=c,d=d)

## Estimation of the model integrating the T and the C parameters
 model <- "C"
 test  <- m4plPersonParameters(x=X, b=b, s=1/a, c=c, d=d, m=0, model=model,
                               prior="uniform", more=TRUE)

## Summary of the preceding model (report and first 5 subjects)
 essai <- m4plSummary(X=test, out="report")
 # Rounding the result of the list to two decimals
 lapply(essai, round, 2)
 essai <- m4plSummary(X=test, out="result")[1:5,]
 lapply(essai, round, 2)
 essai <- m4plSummary(X=test, out="report", thetaInitial=theta)
 lapply(essai, round, 2)
 essai <- m4plSummary(X=test, out="result", thetaInitial=theta)[1:5,]
 lapply(essai, round, 2)

## Results directly from m4plMoreSummary()
 essai <- m4plMoreSummary(x=test, out="report")
 lapply(essai, round, 2)
 essai <- m4plMoreSummary(x=test, out="result")[1:5,]
 round(essai, 2)

## To obtain more general statistics on the result report
 essai <- m4plMoreSummary(x=test, out="result")
 m4plNoMoreSummary(essai)
 summary(m4plMoreSummary(x=test, out="result"))

Results