SBGalvin/psketti: Generating Investigatory Plots and Tables for Rasch Analysis

psketti generates investigatory plots and tables to assist in Rasch Analysis by using a number of accessor, table, and plotting functions. Data are extracted from dichotmous (RM) and partial credit (PCM) Rasch models fitted by Conditional Maximum Likelihood (CML) estimation in the eRm package. Empirical Item Characteristic Curves (ICC) are computed by dividing the latent dimension into class intervals in which the frequency of response to a category is counted and presented as a proportion of that class interval. Confidence Intervals for the Empirical ICC are also calculated. Infit and Outfit measures are also extracted for presentation as a simple diagnostic plot. Plots are compiled using ggplot2.

install.packages("remotes")
remotes::install_github("SBGalvin/psketti")