predict.difNLR: Predicted values for an object of '"difNLR"' class.

In drabinova/difNLR: DIF and DDF Detection by Non-Linear Regression Models

Predicted values for an object of "difNLR" class.

Description

S3 method for predictions from the model used in the object of "difNLR" class.

Usage

## S3 method for class 'difNLR'
predict(
  object,
  item = "all",
  match,
  group,
  interval = "none",
  level = 0.95,
  ...
)

Arguments

object	an object of "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 Data), or item identifiers (integers specifying the column number).
match	numeric: matching criterion for new observations.
group	numeric: group membership for new observations.
interval	character: type of interval calculation, either "none" (default) or "confidence" for confidence interval.
level	numeric: confidence level.
...	other generic parameters for predict() 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, \Sexpr[results=rd]{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, \Sexpr[results=rd]{tools:::Rd_expr_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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1745-3984.1990.tb00754.x")}

See Also

difNLR for DIF detection among binary data using generalized logistic regression model.
predict for generic function for prediction.

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"))

# predicted values
summary(predict(x))
predict(x, item = 1)
predict(x, item = "Item1")

# predicted values for new observations - average score
predict(x, item = 1, match = 0, group = 0) # reference group
predict(x, item = 1, match = 0, group = 1) # focal group

# predicted values for new observations - various z-scores and groups
new.match <- rep(c(-1, 0, 1), 2)
new.group <- rep(c(0, 1), each = 3)
predict(x, item = 1, match = new.match, group = new.group)

# predicted values for new observations with confidence intervals
predict(x, item = 1, match = new.match, group = new.group, interval = "confidence")
predict(x, item = c(2, 4), match = new.match, group = new.group, interval = "confidence")

## End(Not run)

drabinova/difNLR documentation built on Feb. 2, 2024, 7:14 p.m.