ICC and test statistics plots for an object of the "difNLR" class.

A plotting method for an object of the "difNLR" class using the ggplot2 package.

Two types of plots are available. The first one is obtained by setting plot.type = "cc" (default). The characteristic curves for items specified in the item argument are plotted. Plotted curves represent the best fitted model.

The second plot is obtained by setting plot.type = "stat". The test statistics (either LR-test, F-test, or Wald test; depending on argument test) are displayed on the Y axis, for each converged item. The detection threshold is displayed by a horizontal line and items detected as DIF are printed with the red color. Only parameters size and title are used.

Usage

# S3 method for class 'difNLR'
plot(
  x,
  plot.type = "cc",
  item = "all",
  group.names,
  draw.empirical = TRUE,
  draw.CI = FALSE,
  ...
)

Arguments

x: an object of the "difNLR" class.
plot.type: character: a type of a plot to be plotted (either "cc" for characteristic curves (default), or "stat" for test statistics).
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).
group.names: character: names of the reference and focal groups.
draw.empirical: logical: should empirical probabilities be plotted as points? (the default value is TRUE).
draw.CI: logical: should confidence intervals for predicted values be plotted? (the default value is FALSE).
...: other generic parameters for the plot() method.

Value

For an option plot.type = "stat", returns object of the "ggplot" class. In the case of plot.type = "cc", returns a list of objects of the "ggplot" class.

Outputs can be edited and modified as a standard "ggplot" object including colours, titles, shapes, or linetypes.

Note that the option draw.CI = TRUE returns confidence intervals for predicted values as calculated by the predict.difNLR. Confidence intervals may overlap even in the case that item functions differently.

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, 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, doi:10.32614/RJ-2020-014 .

Author

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
hladka@cs.cas.cz

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

Karel Zvara
Faculty of Mathematics and Physics, Charles University

Examples

if (FALSE) { # \dontrun{
# 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"))

# item characteristic curves
plot(x)
plot(x, item = x$DIFitems)
plot(x, item = 1)
plot(x, item = "Item2", group.names = c("Group 1", "Group 2"))

# item characteristic curves without empirical probabilities
plot(x, item = 1, draw.empirical = FALSE)

# item characteristic curves without empirical probabilities but with CI
plot(x, item = 1, draw.empirical = FALSE, draw.CI = TRUE)

# graphical devices - test statistics
plot(x, plot.type = "stat")
} # }

ICC and test statistics plots for an object of the `"difNLR"` class.