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Plot Test Information Curve (TIC) from exametrika

Source: R/ICCtoTIC.R
plotTIC_gg.Rd

This function takes exametrika IRT or GRM output as input and generates a Test Information Curve (TIC) using ggplot2. TIC shows the total information provided by all items at each ability level.

Usage

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

Arguments

data

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

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.

colors

Character vector. Color(s) for the curve. 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, display the legend. Default is FALSE.

legend_position

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

Value

A single ggplot object showing the Test Information Curve.

Details

The Test Information Function is the sum of all Item Information Functions. It indicates how precisely the test as a whole measures ability at each point on the theta scale. The reciprocal of test information is approximately equal to the squared standard error of measurement.

The function supports IRT models (2PL, 3PL, 4PL) and GRM.

See also

plotICC_gg, plotIIC_gg, plotICRF_gg

Examples

library(exametrika)
# \donttest{
# IRT example
result_irt <- IRT(J15S500, model = 3)
#> No ID column detected. All columns treated as response data. Sequential IDs (Student1, Student2, ...) were generated. Use id= parameter to specify the ID column explicitly.
#> No ID column detected. All columns treated as response data. Sequential IDs (Student1, Student2, ...) were generated. Use id= parameter to specify the ID column explicitly.
#> 
iter 1 LogLik -3960.28                                                          
#> 
iter 2 LogLik -3938.35                                                          
#> 
iter 3 LogLik -3931.82                                                          
#> 
iter 4 LogLik -3928.68                                                          
#> 
iter 5 LogLik -3926.99                                                          
#> 
iter 6 LogLik -3926.05                                                          
#> 
iter 7 LogLik -3925.51                                                          
#> 
iter 8 LogLik -3925.19                                                          
#> 
iter 9 LogLik -3925.01                                                          
#> 
iter 10 LogLik -3924.9                                                          
#> 
iter 11 LogLik -3924.83                                                         
#> 
iter 12 LogLik -3924.8                                                          
#> 
iter 13 LogLik -3924.77                                                         
plot_irt <- plotTIC_gg(result_irt)
plot_irt # Show Test Information Curve


# GRM example
result_grm <- 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
plot_grm <- plotTIC_gg(result_grm)
plot_grm # Show Test Information Curve

# }