Log-likelihood and information criteria for an object of the "difNLR" class.

S3 methods for extracting log-likelihood, Akaike's information criterion (AIC) and Schwarz's Bayesian criterion (BIC) for an object of the "difNLR" class.

Usage

# S3 method for class 'difNLR'
logLik(object, item = "all", ...)

# S3 method for class 'difNLR'
AIC(object, item = "all", ...)

# S3 method for class 'difNLR'
BIC(object, item = "all", ...)

Arguments

object

an object of the "difNLR" class.

item

numeric or character: either character "all" to apply for all converged items (default), or a vector of item names (column names of the Data), or item identifiers (integers specifying the column number).

...

other generic parameters for S3 methods.

References

Drabinova, A. & Martinkova, P. (2017). Detection of differential item functioning with nonlinear regression: A non-IRT approach accounting for guessing. Journal of Educational Measurement, 54(4), 498–517, doi:10.1111/jedm.12158 .

Hladka, A. & Martinkova, P. (2020). difNLR: Generalized logistic regression models for DIF and DDF detection. The R Journal, 12(1), 300–323, doi:10.32614/RJ-2020-014 .

See also

difNLR for DIF detection among binary data using the generalized logistic regression model.
logLik for a generic function extracting log-likelihood.
AIC for a generic function calculating AIC and BIC.

Author

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
hladka@cs.cas.cz

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz

Karel Zvara
Faculty of Mathematics and Physics, Charles University

Examples

if (FALSE) { # \dontrun{
# loading data
data(GMAT)
Data <- GMAT[, 1:20] # items
group <- GMAT[, "group"] # group membership variable

# testing both DIF effects using likelihood-ratio test and
# 3PL model with fixed guessing for groups
(x <- difNLR(Data, group, focal.name = 1, model = "3PLcg"))

# AIC, BIC, log-likelihood
AIC(x)
BIC(x)
logLik(x)

# AIC, BIC, log-likelihood for the first item
AIC(x, item = 1)
BIC(x, item = 1)
logLik(x, item = 1)
} # }