Person-Item Plots for IRT Models
Base graphics plotting function for person-item plot visualization of IRT models.
piplot(object, pcol = NULL, histogram = TRUE, 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, grid = TRUE, ...)object |
a fitted model object of class |
pcol |
optional character (vector), specifying the color(s) used for the person parameter plot. |
histogram |
logical. For models estimated via MML ( |
ref |
argument passed over to internal calls of |
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.text |
numeric, the magnification to be used for the symbols
relative to the current setting of |
cex.points |
numeric, the magnification to be used for the points
relative to the current setting of |
grid |
logical or color specification of horizontal grid lines. If set to
|
... |
further arguments passed to internal calls of
|
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. For models estimated via MML (plmodels and
gpcmodels), the normal distribution density of the person parameters is
drawn. If histogram is set to TRUE (the default), a histogram of
the person-wise (individual) person parameters is drawn additionally. If a
multiple group model has been fitted by supplying an impact variable,
multiple person parameter plots are drawn, each corresponding to a specific
level of this variable.
## load verbal agression data
data("VerbalAggression", package = "psychotools")
## fit partial credit model to verbal aggression data
pcmod <- pcmodel(VerbalAggression$resp)
## create a person-item plot visualization of the fitted PCM
plot(pcmod, type = "piplot")
## just visualize the first six items and the person parameter plot
plot(pcmod, type = "piplot", items = 1:6, pcol = "lightblue")
if(requireNamespace("mirt")) {
## fit generalized partial credit model to verbal aggression data
gpcmod <- gpcmodel(VerbalAggression$resp)
## create a person-item plot visualization of the fitted GPCM
plot(gpcmod, type = "piplot")
## turn off the histogram and grid
plot(gpcmod, type = "piplot", histogram = FALSE, grid = FALSE)
## fit GPCM to verbal aggression data accounting for gender impact
mgpcmod <- gpcmodel(VerbalAggression$resp, impact = VerbalAggression$gender)
## create a person-item plot visualization of the fitted GPCM
plot(mgpcmod, type = "piplot", pcol = c("darkgreen", "darkorange"))
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