ICC plots for an object of "difORD" class.

Plot method for an object of "difORD" class using ggplot2.

The characteristic curves (category probabilities) for an item specified in item argument are plotted. Plotted curves represent the best model. For cumulative logit model, also cumulative probabilities may be plotted.

Usage

# S3 method for class 'difORD'
plot(x, item = "all", plot.type, group.names, ...)

Arguments

x

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).

plot.type

character: which plot should be displayed for cumulative logit regression model. Either "category" (default) for category probabilities or "cumulative" for cumulative probabilities.

group.names

character: names of reference and focal group.

...

other generic parameters for plot() function.

Value

Returns list of objects of class "ggplot".

See also

difORD for DIF detection among ordinal data.
ggplot for general function to plot a "ggplot" object.

Author

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
hladka@cs.cas.cz

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz

Examples

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

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

# graphical devices
plot(x, item = 6)
plot(x, item = "R6", group.names = c("Males", "Females"))

# testing both DIF effects with cumulative logit model
(x <- difORD(Data, group, focal.name = 1, model = "cumulative"))
plot(x, item = 7, plot.type = "cumulative")
plot(x, item = 7, plot.type = "category")
} # }