psychotools (version 0.7-0)

regionplot: Region Plots for IRT Models

Description

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

Usage

regionplot(object, parg = list(type = NULL, ref = NULL, alias = TRUE),
    names = TRUE, main = NULL, xlab = "", ylab = "Latent trait", ylim = NULL,
    off = 0.1, col = NULL, linecol = 2, srt = 45, adj = c(1.1, 1.1),
    axes = TRUE, …)

Arguments

object

a fitted model object of class "raschmodel", "rsmodel", "pcmodel", "plmodel" or "gpcmodel".

parg

list of arguments passed over to internal calls of threshpar. See the help page of threshpar for more details.

names

logical or character. If TRUE, the names of the items are displayed on the x-axis. If FALSE, numbers of items are shown. Alternatively a character vector of the same length as the number of items can be supplied.

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 color-generating function like, e.g., gray.colors, which is then directly used to create the color names.

linecol

color for lines indicating “hidden” categories.

srt, adj

numeric. Angle (srt) and adjustment (adj) in case names (rather than numbers) are used as x-axis labels. These are passed to text.

axes

logical. Should axes be drawn?

…

further arguments passed to plot.

Details

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., $P(X_{ij}\ge k)=0.5$. 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.

References

Fox J, Hong J (2009). Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package. Journal of Statistical Software, 32(1), 1--24.

Van der Linden WJ, Hambleton RK (1997). Handbook of Modern Item Response Theory. Springer, New York.

See Also

curveplot, profileplot, infoplot, piplot

Examples

# NOT RUN {
## load verbal aggression data
data("VerbalAggression", package = "psychotools")

## fit a Partial credit model to the items of the first other-to-blame
## 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)

## PCM for full verbal aggression data set
pcm_va <- pcmodel(VerbalAggression$resp)
plot(pcm_va, type = "regions")

if(requireNamespace("mirt")) {
## generalized partial credit model for full verbal aggression data set
gpcm_va <- gpcmodel(VerbalAggression$resp)
plot(gpcm_va, type = "regions")
}
# }