infoplot: Information Plots for IRT Models

In psychotools: Psychometric Modeling Infrastructure

Information Plots for IRT Models

Description

Base graphics plotting function for information plot visualization of IRT models.

Usage

  infoplot(object, what = c("categories", "items", "test"),
    ref = NULL, items = NULL, names = NULL, layout = NULL, xlim = NULL,
    ylim = NULL, col = NULL, lty = NULL, lwd = NULL, main = NULL, legend = TRUE,
    xlab = "Latent trait", ylab = "Information", add = FALSE, ...)

Arguments

object	a fitted model object of class "raschmodel", "rsmodel", "pcmodel", "nplmodel" or "gpcmodel".
what	character, specifying the type of information to visualize.
ref	argument passed over to internal calls of predict.
items	character or numeric, specifying the items for which information curves should be visualized.
names	character, specifying labels for the items.
layout	matrix, specifying how the item or category information curves of different items should be arranged. If null and what is set to "items", the item information curves are overlayed within a single plot.
xlim, ylim	numeric, specifying the x and y axis limits.
col	character, specifying the colors of the test, item or category information curves.
lty	numeric, specifying the line type of the information curves.
lwd	numeric, specifying the line width of the information curves.
main	character, specifying the overall title of the plot.
legend	logical, specifying if a legend is drawn when multiple item information curves are overlayed. The labels in the legend correspond to the item names (which can be specified in the argument names).
xlab, ylab	character, specifying the x and y axis labels.
add	logical. If TRUE, new information curves are added to an existing plot. Only possible for a test or a single item information curve.
...	further arguments passed to internal calls of matplot.

Details

The information plot visualization illustrates the test, item or category information as a function of the ability parameter \theta under a certain IRT model. Further details on the computation of the displayed information can be found on the help page of the function predict.pcmodel.

See Also

curveplot, regionplot, profileplot, piplot

Examples

## load verbal aggression data
data("VerbalAggression", package = "psychotools")

## fit Rasch and partial credit model to verbal aggression data
rmmod <- raschmodel(VerbalAggression$resp2)
pcmod <- pcmodel(VerbalAggression$resp)

## category information plots for all items under the dichotomous RM
plot(rmmod, type = "information", what = "categories")

## category information plots for all items under the PCM
plot(pcmod, type = "information", what = "categories")

## overlayed item information plots for the first six items of the
## data set under the PCM
plot(pcmod, type = "information", what = "items", items = 1:6)

## a comparison of the item information for the first six items under the
## dichotomous RM and the PCM
plot(pcmod, type = "information", what = "items", items = 1:6,
  xlim = c(-5, 5))
plot(rmmod, type = "information", what = "items", items = 1:6,
  lty = 2, add = TRUE)
legend(x = "topright", legend = c("PCM", "RM"), lty = 1:2, bty = "n")

## a comparison of the test information based on all items of the
## data set under the dichotomous RM and the PCM
plot(pcmod, type = "information", what = "test", items = 1:6, xlim = c(-5, 5))
plot(rmmod, type = "information", what = "test", items = 1:6, lty = 2,
  add = TRUE)
legend(x = "topright", legend = c("PCM", "RM"), lty = 1:2, bty = "n")

if(requireNamespace("mirt")) {
## fit 2PL to verbal aggression data
twoplmod <- nplmodel(VerbalAggression$resp2)

## category information plots for all items under the dichotomous 2PL
plot(twoplmod, type = "information", what = "categories")
}

psychotools documentation built on May 29, 2024, 8:12 a.m.