plot.difNLR: ICC and test statistics plots for an object of the '"difNLR"'...

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

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

Description

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.

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.difNLR for prediction. ggplot for a general function to plot with the "ggplot2" package.

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"))

# 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")

## End(Not run)

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