difNLR (version 1.3.0)

predict.difNLR: Predicted values for difNLR object

Description

Predicted values based on "difNLR" object.

Usage

# S3 method for difNLR
predict(object, item = "all", match, group, ...)

Arguments

object

an object of "difNLR" class

item

either character ("all"), or numeric vector, or single number corresponding to column indicators.

match

numeric: specifies matching criterion for new observation. See Details.

group

numeric: specifies group membership for new observation. See Details.

...

other generic parameters for predict() function.

Details

Arguments match and group represent matching criterion and group membership of new observations and need to have the same length.

References

Drabinova, A. & Martinkova P. (2017). Detection of Differential Item Functioning with NonLinear Regression: Non-IRT Approach Accounting for Guessing. Journal of Educational Measurement, 54(4), 498-517.

Swaminathan, H. & Rogers, H. J. (1990). Detecting Differential Item Functioning Using Logistic Regression Procedures. Journal of Educational Measurement, 27, 361-370.

See Also

difNLR

Examples

# NOT RUN {
# loading data based on GMAT
data(GMAT)

Data  <- GMAT[, 1:20]
group <- GMAT[, "group"]

# 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"))

# Predicted values
predict(x)
predict(x, item = 1)

# Predicted values for new observations
predict(x, item = 1, match = 0, group = 1)
predict(x, item = 1, match = 0, group = 0)
# }
# NOT RUN {
# }