m4plSummary {irtProb} | R Documentation |
Summary of the results of estimation with the m4pl models.
m4plSummary( X, ...) m4plMoreSummary( x, out = "result", thetaInitial = NULL) m4plNoMoreSummary(x)
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. |
..............
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.
Gilles Raiche, Universite du Quebec a Montreal (UQAM),
Departement d'education et pedagogie
Raiche.Gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/
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/
## 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"))