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RMX

“But Rasch would have wondered about what happened to the use of graphs. And I think he would have been quite justified in this. Could it be that we have used computers in a wrong way? Since Rasch retired from active duty, have we emphasized the power of computers to do complicated calculations and solving complicated equations over the power of the computers to make nice and illustrative graphs?” — Erling B. Andersen1

Overview

The RMX package is a tool to visualize the various aspects of Item-Response-Theory (IRT) models. The major feature is the plotPIccc() function which draws the so called “PIccc”.

The PIccc is a modified version of the classical Person-Item Map (also called Wright-Map). In the classical PI map the person parameter distribution is contrasted with the item parameters. However, the item parameters are only shown as simple dots. Furthermore, it does not provide any information for models involving discrimination, guessing, or laziness parameters. The plotPIccc function overcomes these restrictions in several aspects. In its core the plotPIccc function shows the Person-Item confrontation using category characteristic curves (CCC, hence the name PIccc for the diagram) but also many other functions, that can help visualize different aspects of the IRT model.

Why RMX?

  • additional information compared to the classical Person-Item Map
  • flexible selection of items and dimensions
  • flexible arrangement of items accoding to characteristics other than difficulty
  • various graphical options
  • support of all major IRT packages of R (mirt, eRm, ltm, TAM and psychotools)

Features

The plotPIccc() function supports two modi:

  • drawing one type of curve for a set of items (default)
  • drawing several types for one item (by providing a vector of types)

Additionally you can plot:

  • the test information function (TIF) for the entire set of items (TIF=TRUE)
  • the TIF of the selected items (sTIF=TRUE)
  • the standard error (SE) for all items (SE=TRUE)
  • the SE of the selected items (sSE=TRUE)
  • the kernel density estimate (dens=TRUE)

Installation

You can install the latest release from CRAN :

Usage

Check out the examples for a more detailed description or go to the gallery for a demonstration of most features of the plotPIccc function in every supported package.

library(RMX)

data(big5)

dat_extra = big5[,c(1,6,11,16)]

library(eRm)

res0 = PCM(dat_extra)

plotPIccc(res0)

example