Extract item parameter estimates from an object of the "difNLR" class.

S3 method for extracting the item parameter estimates from an object of the "difNLR" class.

Usage

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

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).

SE

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

simplify

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

IRTpars

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

CI

numeric: a significance level for confidence intervals (CIs) of item parameter estimates (the default is 0.95 for 95% CI). With 0 value, no CIs are displayed.

...

other generic parameters for the coef() method.

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 .

See also

difNLR for DIF detection among binary data using the generalized logistic regression model.
coef for a generic function for extracting parameter estimates.

Author

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

Examples

if (FALSE) { # \dontrun{
# 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)
# for DIF items only
coef(x, item = x$DIFitems, IRTpars = FALSE, simplify = TRUE, CI = 0)
} # }