coef.difNLR {difNLR} | R Documentation |
"difNLR"
class.S3 method for extracting model coefficients from an
object of "difNLR"
class.
## S3 method for class 'difNLR' coef(object, SE = FALSE, simplify = FALSE, IRTpars = TRUE, CI = 0.95, ...)
object |
an object of |
SE |
logical: should the standard errors of estimated
parameters be also returned? (default is |
simplify |
logical: should the estimated parameters be
simplified to a matrix? (default is |
IRTpars |
logical: should the estimated parameters be returned
in IRT parameterization? (default is |
CI |
numeric: level of confidence interval for parameters,
default is |
... |
other generic parameters for |
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.
coef
for generic function extracting model coefficients.
## 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")) # estimated parameters coef(x) # includes standard errors coef(x, SE = TRUE) # includes standard errors and simplifies to matrix coef(x, SE = TRUE, simplify = TRUE) # intercept-slope parameterization coef(x, IRTpars = FALSE) # intercept-slope parameterization, simplifies to matrix, turn off confidence intervals coef(x, IRTpars = FALSE, simplify = TRUE, CI = 0) ## End(Not run)