curveplot {psychotools} | R Documentation |
Base graphics plotting function for response curve plot visualization of IRT models.
curveplot(object, ref = NULL, items = NULL, names = NULL, layout = NULL, xlim = NULL, ylim = c(0, 1), col = NULL, lty = NULL, main = NULL, xlab = "Latent trait", ylab = "Probability", add = FALSE, ...)
object |
a fitted model object of class
|
ref |
argument passed over to internal calls of |
items |
character or numeric, specifying the items for which response curves should be visualized. |
names |
character, specifying labels for the items. |
layout |
matrix, specifying how the response curve plots of different items should be arranged. |
xlim, ylim |
numeric, specifying the x and y axis limits. |
col |
character, specifying the colors of the response curve lines. The
length of |
lty |
numeric, specifying the line type of the response curve lines. The
length of |
main |
character, specifying the overall title of the plot. |
xlab, ylab |
character, specifying the x and y axis labels. |
add |
logical. If |
... |
further arguments passed to internal calls of |
The response curve plot visualization illustrates the predicted probabilities as function of the ability parameter θ under a certain IRT model. This type of visualization is sometimes also called item/category operating curves or item/category characteristic curves.
regionplot
, profileplot
,
infoplot
, piplot
## Load Verbal Aggression data data("VerbalAggression", package = "psychotools") ## Fit Rasch, rating scale and partial credit ## model to VerbalAggression data rmmod <- raschmodel(VerbalAggression$resp2) rsmod <- rsmodel(VerbalAggression$resp) pcmod <- pcmodel(VerbalAggression$resp) ## Curve plots of the dichotomous RM plot(rmmod, type = "curves") ## Curve plots under the rating scale model ## for the first six items of the data set plot(rsmod, type = "curves", items = 1:6) ## Curve plots under the partial credit model ## for the first six items of the data set ## with custom labels plot(pcmod, type = "curves", items = 1:6, names = paste("Item", 1:6)) ## Compare the predicted probabilities under the rating ## scale model and the partial credit model for a single item plot(rsmod, type = "curves", item = 1) plot(pcmod, type = "curves", item = 1, lty = 2, add = TRUE) legend(x = "topleft", y = 1.0, legend = c("RSM", "PCM"), lty = 1:2, bty = "n")