coef.difNLR {difNLR}

Extract model coefficients from an object of "difNLR" class.

Description

S3 method for extracting model coefficients from an object of "difNLR" class.

Usage

## S3 method for class 'difNLR'
coef(object, SE = FALSE, simplify = FALSE, IRTpars = TRUE, CI = 0.95, ...)

Arguments

object

an object of "difNLR" class.

SE

logical: should the standard errors of estimated parameters be also returned? (default is FALSE).

simplify

logical: should the estimated parameters be simplified to a matrix? (default is FALSE).

IRTpars

logical: should the estimated parameters be returned in IRT parameterization? (default is TRUE).

CI

numeric: level of confidence interval for parameters, default is 0.95 for 95% confidence interval.

...

other generic parameters for coef() function.

Author(s)

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
[e-mail]

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
[e-mail]

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, 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

See Also

difNLR for DIF detection among binary data using generalized logistic regression model.
coef for generic function extracting model coefficients.

Examples

## 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)