difNLR (version 1.5.1-1)

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

Description

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

Usage

# S3 method for 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.

Author

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

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences

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, tools:::Rd_expr_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, tools:::Rd_expr_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.

Examples

if (FALSE) {
# 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)
}