difNLR (version 1.3.0)

plot.difNLR: ICC and test statistics plots for difNLR object

Description

Two types of plots are available. The first one is obtained by setting plot.type = "cc" (default). The characteristic curve for item specified in item option is plotted. For default option "all" of item, characteristic curves of all converged items are plotted. The drawn curves represent best model.

The second plot is obtained by setting plot.type = "stat". The test statistics (either LR-test, or F-test, depends 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 difNLR
plot(x, plot.type = "cc", item = "all",
  col = c("dodgerblue2", "goldenrod2"), shape = 21, size = 0.8,
  linetype = c(2, 1), title, group.names, ...)

Arguments

x

an object of "difNLR" class

plot.type

character: type of plot to be plotted (either "cc" for characteristic curve (default), or "stat" for test statistics). See Details.

item

either character ("all"), or numeric vector, or single number corresponding to column indicators. See Details.

col

character: single value, or vector of two values representing colors for plot.

shape

integer: shape parameter for plot.

size

numeric: single number, or vector of two numbers representing line width in plot.

linetype

numeric: single number, or vector of two numbers representing line type in plot for reference and focal group.

title

string: title of plot.

group.names

character: names of reference and focal group.

...

other generic parameters for plot() function.

References

Drabinova, A. & Martinkova P. (2017). Detection of Differential Item Functioning with NonLinear Regression: Non-IRT Approach Accounting for Guessing. Journal of Educational Measurement, 54(4), 498-517.

Swaminathan, H. & Rogers, H. J. (1990). Detecting Differential Item Functioning Using Logistic Regression Procedures. Journal of Educational Measurement, 27, 361-370.

See Also

difNLR

Examples

# NOT RUN {
# loading data based on GMAT
data(GMAT)

Data  <- GMAT[, 1:20]
group <- GMAT[, "group"]

# 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"))

# graphical devices
plot(x)
plot(x, item = x$DIFitems)
plot(x, plot.type = "stat")
# }