difNLR (version 1.3.7)

logLik.difNLR: Loglikelihood and information criteria for an object of "difNLR" class.

Description

S3 methods for extracting loglikelihood, Akaike's information criterion (AIC) and Schwarz's Bayesian criterion (BIC) for an object of "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 "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 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, 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, 10.32614/RJ-2020-014.

Swaminathan, H. & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361--370, 10.1111/j.1745-3984.1990.tb00754.x

See Also

difNLR for DIF detection among binary data using generalized logistic regression model. logLik for generic function extracting loglikelihood. AIC for generic function calculating AIC and BIC.

Examples

# NOT RUN {
# Loading data based on GMAT
data(GMAT)

Data <- GMAT[, 1:20]
group <- GMAT[, "group"]

# 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)
# }