Base graphics plotting function for region plot visualization of IRT models.
1 2 3 4 
object 
a fitted model object of class

parg 
list of arguments passed over to internal calls
of 
names 
logical or character. If 
main 
character, specifying the overall title of the plot. 
xlab, ylab 
character, specifying the x and y axis labels. 
ylim 
numeric, specifying the y axis limits. 
off 
numeric, the distance (in scale units) between two item rectangles. 
col 
character, list or function, specifying the colors of
the regions. Either a single vector with k color names,
a list with m elements and each element is a character
vector with color names for the regions of item j or a
colorgenerating function like, e.g., 
linecol 
color for lines indicating “hidden” categories. 
srt, adj 
numeric. Angle ( 
axes 
logical. Should axes be drawn? 
... 
further arguments passed to 
The region plot visualization implemented here was already
used by Van der Linden and Hambleton (1997) in the context of IRT and
has been called "effect plots" by Fox & Hong (2009). In our
implementation, these plots show, dependent on the chosen type of
threshold parameters, different regions for the categories of an item
over the theta axis. If type
is set to "modus"
, the
cutpoints correspond to the threshold parameters and the rectangles
mark the theta regions where a category is the single most probable
category chosen with a certain value of the latent trait. If
type
is set to "median"
, the cutpoints correspond to the
point on the theta axis, where the cumulative probability to score in
category k or higher is 0.5, i.e., P(X_{ij} ≥q k) =
0.5. If set to "mean"
, the cutpoints correspond to the point
on the theta axis where the expected score E(X_{ij}) is
exactly between two categories, e.g., 0.5 for a dichotomous item.
If type
is set to "mode"
and there are unordered
threshold parameters, the location of the original threshold
parameters are indicated by red dashed lines.
Fox J, Hong J (2009). Effect Displays in R for Multinomial and ProportionalOdds Logit Models: Extensions to the effects Package. Journal of Statistical Software, 32(1), 1–24. doi: 10.18637/jss.v032.i01
Van der Linden WJ, Hambleton RK (1997). Handbook of Modern Item Response Theory. SpringerVerlag, New York.
curveplot
, profileplot
,
infoplot
, piplot
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Load Verbal aggression data
data("VerbalAggression", package = "psychotools")
## Fit a Partial credit model to the items of the
## first othertoblame situation: 'A bus fails to stop for me'
pcm < pcmodel(VerbalAggression$resp[, 1:6])
## A region plot with modus as cutpoint and custom labels.
lab < paste(rep(c("Curse", "Scold", "Shout"), each = 2),
rep(c("Want", "Do"), 3 ), sep = "")
plot(pcm, type = "regions", names = lab)
## Compare the cutpoints (with ylim specified manually)
opar < par(no.readonly = TRUE)
ylim < c(2, 2)
layout(matrix(1:3, ncol = 1))
plot(pcm, type = "regions", parg = list(type = "mode"),
main = "Modus as Cutpoint", ylim = ylim)
plot(pcm, type = "regions", parg = list(type = "median"),
main = "Median as Cutpoint", ylim = ylim)
plot(pcm, type = "regions", parg = list(type = "mean"),
main = "Mean as Cutpoint", ylim = ylim)
par(opar)
## Partial credit model for full VerbalAggression data set
pcm_va < pcmodel(VerbalAggression$resp)
plot(pcm_va, type = "regions")

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