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

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

Predicted values for an object of "ddfMLR" class.

Description

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

Usage

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

Arguments

object	an object of "ddfMLR" 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.
...	other generic parameters for predict() function.

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]

References

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

ddfMLR for DDF detection among nominal data using multinomial log-linear regression model.
predict for generic function for prediction.

Examples

## Not run: 
# loading data
data(GMATtest, GMATkey)
Data <- GMATtest[, 1:20] # items
group <- GMATtest[, "group"] # group membership variable
key <- GMATkey # correct answers

# testing both DDF effects
(x <- ddfMLR(Data, group, focal.name = 1, key))

# fitted values
predict(x, item = 1)

# predicted values
predict(x, item = 1, match = 0, group = c(0, 1))
predict(x, item = x$DDFitems, match = 0, group = c(0, 1))

## End(Not run)

difNLR documentation built on May 3, 2023, 5:11 p.m.