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

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

Predicted values for an object of the "difNLR" class.

Description

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

Usage

## S3 method for class 'difNLR'
predict(object, item = "all", match, group, interval = "none", 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).
match	numeric: a matching criterion for new observations.
group	numeric: a group membership variable for new observations.
interval	character: a type of interval calculation, either "none" (default) or "confidence" for confidence interval.
CI	numeric: a significance level for confidence interval (the default is 0.95 for 95% confidence interval).
...	other generic parameters for the predict() method.

Author(s)

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
[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")}.

See Also

difNLR for DIF detection among binary data using the generalized logistic regression model.
predict for a 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
predict(x, item = 1, match = 0, group = c(0, 1)) # both groups

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

adelahladka/difNLR documentation built on Dec. 23, 2024, 2:20 a.m.