difNLR (version 1.5.1-1)

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

Description

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

Author

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences

Karel Zvara
Faculty of Mathematics and Physics, Charles University

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, tools:::Rd_expr_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, tools:::Rd_expr_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.

Examples

if (FALSE) {
# 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)
}