psychotools (version 0.5-1)

infoplot: 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", "plmodel" 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

# NOT RUN {
## 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 <- plmodel(VerbalAggression$resp2)

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