m4plSummary: Summary of the Results of Estimation with the m4pl Models

In irtProb: Utilities and Probability Distributions Related to Multidimensional Person Item Response 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

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m4plSummary

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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.

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m4plNoMoreSummary

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

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m4plMoreSummary

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All other outputs from the m4plSummary function.

Author(s)

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

Departement d'education et pedagogie

[e-mail], 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"))

irtProb documentation built on May 2, 2019, 1:30 p.m.