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Plot Item Category Response Function (ICRF) for GRM from exametrika

Source: R/GRM.R
plotICRF_gg.Rd

This function takes exametrika GRM output as input and generates Item Category Response Function (ICRF) plots using ggplot2. Each plot shows the probability of selecting each response category as a function of ability (theta).

Usage

plotICRF_gg(
  data,
  items = NULL,
  xvariable = c(-4, 4),
  title = TRUE,
  colors = NULL,
  linetype = "solid",
  show_legend = TRUE,
  legend_position = "right"
)

Arguments

data

An object of class c("exametrika", "GRM") from exametrika::GRM().

items

Numeric vector specifying which items to plot. If NULL (default), all items are plotted.

xvariable

A numeric vector of length 2 specifying the range of the x-axis (ability). Default is c(-4, 4).

title

Logical or character. If TRUE (default), display an auto-generated title. If FALSE, no title. If a character string, use it as a custom title (only for single-item plots).

colors

Character vector of colors for each category. If NULL (default), a colorblind-friendly palette is used.

linetype

Character or numeric specifying the line type. Default is "solid".

show_legend

Logical. If TRUE (default), display the legend.

legend_position

Character. Position of the legend. One of "right" (default), "top", "bottom", "left", "none".

Value

A list of ggplot objects, one for each item. Each plot shows the Item Category Response Function for that item.

Details

The Graded Response Model (GRM) estimates the probability of selecting each ordered response category. For an item with \(K\) categories, the ICRF shows \(K\) curves, one for each category. At any given ability level, the probabilities across all categories sum to 1.

The category probabilities are derived from cumulative probabilities: $$P_k^*(\theta) = \frac{1}{1 + \exp(-a(\theta - b_k))}$$ $$P_k(\theta) = P_k^*(\theta) - P_{k+1}^*(\theta)$$

See also

plotICC_gg

Examples

library(exametrika)
# \donttest{
result <- GRM(J5S1000)
#> Parameters: 18 | Initial LL: -6252.352 
#> initial  value 6252.351598 
#> iter  10 value 6032.463982
#> iter  20 value 6010.861094
#> final  value 6008.297278 
#> converged
plots <- plotICRF_gg(result)
plots[[1]] # Show ICRF for the first item

combinePlots_gg(plots, selectPlots = 1:5)

# }