curveplot: Response Curve Plots for IRT Models

In psychotools: Psychometric Modeling Infrastructure

Response Curve Plots for IRT Models

Description

Base graphics plotting function for response curve plot visualization of IRT models.

Usage

  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, ...)

Arguments

object	a fitted model object of class "raschmodel", "rsmodel", "pcmodel", "nplmodel" or "gpcmodel".
ref	argument passed over to internal calls of predict.
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 col should be the maximum number of available categories.
lty	numeric, specifying the line type of the response curve lines. The length of lty should either be one or the maximum number of available categories. In the first case, a single line type is used for all category response curves. In the latter case, separate line types for each category response curve are used.
main	character, specifying the overall title of the plot.
xlab, ylab	character, specifying the x and y axis labels.
add	logical. If TRUE, new response curves are added to an existing plot. Only possible when a single item is visualized.
...	further arguments passed to internal calls of matplot.

Details

The response curve plot visualization illustrates the predicted probabilities as a function of the ability parameter \theta under a certain IRT model. This type of visualization is sometimes also called item/category operating curves or item/category characteristic curves.

See Also

regionplot, profileplot, infoplot, piplot

Examples

## load verbal aggression data
data("VerbalAggression", package = "psychotools")

## fit Rasch, rating scale and partial credit model to verbal aggression 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 RSM for the first six items of the data set
plot(rsmod, type = "curves", items = 1:6)

## curve plots under the PCM 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 RSM and the PCM 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")


if(requireNamespace("mirt")) {
## fit 2PL and generaliced partial credit model to verbal aggression data
twoplmod <- nplmodel(VerbalAggression$resp2)
gpcmod <- gpcmodel(VerbalAggression$resp)

## curve plots of the dichotomous 2PL
plot(twoplmod, type = "curves", xlim = c(-6, 6))

## curve plots under the GPCM for the first six items of the data set
plot(gpcmod, type = "curves", items = 1:6, xlim = c(-6, 6))
}

psychotools documentation built on May 29, 2024, 8:12 a.m.