infoplot
Information Plots for IRT Models
Base graphics plotting function for information plot visualization of IRT models.
- Keywords
- aplot
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
nullandwhatis 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
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")
}
# }