plot.ksIRT: Plot Method for ksIRT - kernel smoothing in Item Response...

In KernSmoothIRT: Nonparametric Item Response Theory

Description

The plot method for ksIRT objects includes a variety of exploratory plotting tools.

Usage

## S3 method for class 'ksIRT'
plot(x, plottype = c("OCC", "EIS", "density", "expected", "sd", 
"triangle", "tetrahedron", "RCC", "EISDIF", "OCCDIF", "PCA", "expectedDIF", 
"densityDIF"), items = "all", subjects, axistype = c("scores", "distribution"), 
alpha, main, xlab, ylab, xlim, ylim, cex, ...)

Arguments

x	a ksIRT created with ksIRT()
plottype	the type of plot to be created (see section Details below). With the default value, plottype="OCC", option characteristic curves are plotted.
items	a vector containing the items to be plotted. With the default value, items="all", all items are plotted.
alpha	either alpha=FALSE or a scalar indicating the confidence level to be used for creating confidence intervals. It is used with plottype="EIS", and the default value is alpha=.05, or with plottype="OCC", and the default value is alpha=FALSE.
subjects	a vector specifying the subjects to plot. This argument is only used when plottype="RCC".
axistype	a character string specifying the display variable to be used on the x-axis. The default is axistype="distribution", which uses the subjscoresummary of the distribution specified in thetadist. The alternative is axistype="scores" which displays the expected score.
main, xlab, ylab, xlim, ylim, cex	plotting parameters (see plot()) with useful defaults.
...	further plotting parameters.

Details

Possible values for plottype are:

plottype="density"

produces a simple kernel density plot of the observed scores.

plottype="EIS"

plot of the expected item scores for each of the item numbers in the items argument.

plottype="OCC"

plot of the option characteristic curves for each of the item numbers in the items argument.

plottype="expected"

plot of the observed vs. expected scores.

plottype="sd"

plot of the standard deviation of observed scores.

plottype="RCC"

plots the RCC and actual score for each subject specified by the subjects argument.

plottype="triangle"

produces a triangle simplex plot with the highest 3 probability options for each item specified by the items argument.

plottype="tetrahedron"

produces a tetrahedron simplex plot with the highest 4 probability options for each item specified by the items argument. The tetrahedron plot can be rotated by using the mouse.

plottype="PCA"

produces Principle Component Analysis plot of the test.

Below are values for plottype used for Differential Item Functioning (DIF) plots. They are available only if the groups argument is specified when creating the ksIRT object:

plottype="densityDIF"

plots density of observed scores for each of the different groups.

plottype="expectedDIF"

plots pairwise expected value comparison plots for each of the different groups.

plottype="EISDIF"

plots expected item scores for each of the different groups. Accepts the same arguments as plottype="EIS", but by default does not show confidence intervals. This can be changed with the alpha argument.

plottype="OCCDIF"

plots option characteristic curves for each of the different groups. Accepts the same arguments as plottype="OCC"

Value

No values are returned from the plot function.

References

Mazza A, Punzo A, McGuire B. (2014). KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory. Journal of Statistical Software, 58 6, 1-34. URL: http://www.jstatsoft.org/v58/i06/.

Ramsay, J.O. (2000). TestGraf: A program for the graphical analysis of multiple choice test and questionnaire data.

KernSmoothIRT documentation built on March 26, 2020, 7:42 p.m.