piplot: Person-Item Plots for IRT Models

Description

Base graphics plotting function for person-item plot visualization of IRT models.

Usage

piplot(object, ref = NULL, items = NULL, xlim = NULL, names = NULL, labels = TRUE, main = "Person-Item Plot", xlab = "Latent trait", abbreviate = FALSE, cex.axis = 0.8, cex.text = 0.5, cex.points = 1.5, ...)

Arguments

object

a fitted model object of class "raschmodel", "rsmodel" or "pcmodel".

ref

argument passed over to internal calls of threshpar and itempar.

items

character or numeric, specifying the items which should be visualized in the person-item plot.

xlim

numeric, specifying the x axis limits.

names

character, specifying labels for the items.

labels

logical, whether to draw the number of the threshold as text below the threshold.

main

character, specifying the overall title of the plot.

xlab

character, specifying the x axis labels.

abbreviate

logical or numeric, specifying whether object names are to be abbreviated. If numeric this controls the length of the abbreviation.

cex.axis

numeric, the magnification to be used for the axis notation relative to the current setting of cex.

cex.text

numeric, the magnification to be used for the symbols relative to the current setting of cex.

cex.points

numeric, the magnification to be used for the points relative to the current setting of cex.

...

further arguments passed to internal calls of lines, points and text

Details

The person-item plot visualization illustrates the distribution of the person parameters against the absolute item threshold parameters under a certain data set and IRT model.

Examples

Run this code

## Verbal aggression data
data("VerbalAggression", package = "psychotools")

## Fit a partial credit model to VerbalAggression data
pcmod <- pcmodel(VerbalAggression$resp)

## Create a person-item plot visualization of the fitted PCM
plot(pcmod, type = "piplot")

## Just visualize the first six items
plot(pcmod, type = "piplot", ref = 1:6)

Run the code above in your browser using DataLab

Data Engineering and BI courses are free this week!