logLik.difNLR {difNLR} | R Documentation |
"difNLR"
class.S3 methods for extracting log-likelihood, Akaike's information criterion (AIC) and
Schwarz's Bayesian criterion (BIC) for an object of "difNLR"
class.
## 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", ...)
object |
an object of |
item |
numeric or character: either character |
... |
other generic parameters for S3 methods. |
Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
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
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.
Swaminathan, H. & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370, doi: 10.1111/j.1745-3984.1990.tb00754.x
difNLR
for DIF detection among binary data using generalized logistic regression model.
logLik
for generic function extracting log-likelihood.
AIC
for generic function calculating AIC and BIC.
## Not run: # 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) ## End(Not run)