R: Response Curve Plots for IRT Models

curveplot {psychotools} | R Documentation

## 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"` or `"pcmodel"`.
`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 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.

### See Also

`regionplot`, `profileplot`, `infoplot`, `piplot`

### Examples

    ## 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")

[Package psychotools version 0.4-2 Index]