difNLR (version 1.5.1-1)

predict.difORD: Predicted values for an object of "difORD" class.

Description

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

Usage

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

Arguments

object

an object of "difORD" 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.

type

character: type of probability to be computed. Either "category" for category probabilities or "cumulative" for cumulative probabilities. Cumulative probabilities are available only for cumulative logit model.

...

other generic parameters for predict() function.

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

References

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

difORD for DIF detection among ordinal data using either cumulative logit or adjacent category logit model.
predict for generic function for prediction.

Examples

if (FALSE) {
# loading data
data(Anxiety, package = "ShinyItemAnalysis")
Data <- Anxiety[, paste0("R", 1:29)] # items
group <- Anxiety[, "gender"] # group membership variable

# testing both DIF effects with cumulative logit model
(x <- difORD(Data, group, focal.name = 1, model = "cumulative"))

# fitted values
predict(x, item = "R6")

# predicted values
predict(x, item = "R6", match = 0, group = c(0, 1))
predict(x, item = "R6", match = 0, group = c(0, 1), type = "cumulative")
predict(x, item = c("R6", "R7"), match = 0, group = c(0, 1))

# testing both DIF effects with adjacent category logit model
(x <- difORD(Data, group, focal.name = 1, model = "adjacent"))

# fitted values
predict(x, item = "R6")

# predicted values
predict(x, item = "R6", match = 0, group = c(0, 1))
predict(x, item = c("R6", "R7"), match = 0, group = c(0, 1))
}