# class.accuracy.rasch: Classification Accuracy in the Rasch Model In alexanderrobitzsch/sirt: Supplementary Item Response Theory Models

 class.accuracy.rasch R Documentation

## Classification Accuracy in the Rasch Model

### Description

This function computes the classification accuracy in the Rasch model for the maximum likelihood (person parameter) estimate according to the method of Rudner (2001).

### Usage

``````class.accuracy.rasch(cutscores, b, meantheta, sdtheta, theta.l, n.sims=0)
``````

### Arguments

 `cutscores` Vector of cut scores `b` Vector of item difficulties `meantheta` Mean of the trait distribution `sdtheta` Standard deviation of the trait distribution `theta.l` Discretized theta distribution `n.sims` Number of simulated persons in a data set. The default is 0 which means that no simulation is performed.

### Value

A list with following entries:

 `class.stats` Data frame containing classification accuracy statistics. The column `agree0` refers to absolute agreement, `agree1` to the agreement of at most a difference of one level. `class.prob` Probability table of classification

### References

Rudner, L.M. (2001). Computing the expected proportions of misclassified examinees. Practical Assessment, Research & Evaluation, 7(14).

Classification accuracy of other IRT models can be obtained with the R package cacIRT.

### Examples

``````#############################################################################
#############################################################################

# estimate the Rasch model
mod <- sirt::rasch.mml2( dat )

# estimate classification accuracy (3 levels)
cutscores <- c( -1, .3 )    # cut scores at theta=-1 and theta=.3
sirt::class.accuracy.rasch( cutscores=cutscores, b=mod\$item\$b,
meantheta=0,  sdtheta=mod\$sd.trait,
theta.l=seq(-4,4,len=200), n.sims=3000)
##   Cut Scores
##   [1] -1.0  0.3
##
##   WLE reliability (by simulation)=0.671
##   WLE consistency (correlation between two parallel forms)=0.649
##
##   Classification accuracy and consistency
##              agree0 agree1 kappa consistency
##   analytical   0.68  0.990 0.492          NA
##   simulated    0.70  0.997 0.489       0.599
##
##   Probability classification table
##               Est_Class1 Est_Class2 Est_Class3
##   True_Class1      0.136      0.041      0.001
##   True_Class2      0.081      0.249      0.093
##   True_Class3      0.009      0.095      0.294
``````

alexanderrobitzsch/sirt documentation built on Feb. 16, 2024, 10:06 a.m.