Get simulation based cutoff values for item fit values

This function uses your response data to simulate datasets that fit the Rasch model to find a credible range of item fit values. The function outputs an object that is strongly recommended to save to an object, since it takes some time to run this function when using many iterations/simulations.

Usage

RIgetfit(data, iterations = 250, cpu = 4, na.omit = TRUE)

Arguments

data

Dataframe with response data

iterations

Number of simulation iterations (use 200-400)

cpu

Number of CPU cores to use

na.omit

Defaults to TRUE to produce conditional fit comparable values

Details

The output is a list object, which can in turn be used with two different functions. Most importantly, you can use it with RIitemfit() to get conditional highlighting of cutoff values based on your sample size and item parameters. Each item gets its own cutoff thresholds.

The function RIgetfitPlot() uses the package ggdist to plot the distribution of fit values from the simulation results.

Uses multi-core processing. To find how many cores you have on your computer, use parallel::detectCores(). Remember to keep 1-2 cores free.

Since version 0.2.4.2, the default is to only use complete cases in the simulations, since this is what the conditional item fit function uses and numbers should be more comparable using this method.