effectiveThetas: Assigns effective theta levels for categorical variable
In ralmond/CPTtools: Tools for Creating Conditional Probability Tables
effectiveThetas R Documentation
Assigns effective theta levels for categorical variable
Description
Calculates a vector of normal quantiles corresponding to effective
theta levels for a categorical variable for use in a DiBello-Samejima
distribution.
Usage
effectiveThetas(nlevels)
Arguments
nlevels
Integer giving the number of levels of the categorical
variable.
Details
The DiBello–Samejima models map levels of categorical values into
effective “theta” values, or corresponding continuous values.
These can then be input into IRT equations to calculate cell
probabilities.
The default algorithm works by assuming that the categories are
created by cutting the normal distribution into equal probability
intervals. The value returned for each interval is the midpoint (wrt
the normal measure) of that interval.
Value
A vector of normal quantiles of length nlevels.
Author(s)
Russell Almond
References
Almond, R.G., Mislevy, R.J., Steinberg, L.S., Yan, D. and Williamson, D.M.
(2015). Bayesian Networks in Educational Assessment.
Springer. Chapter 8.
Examples
effectiveThetas(5)
ralmond/CPTtools documentation built on Dec. 27, 2024, 7:15 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| effectiveThetas | R Documentation |
Assigns effective theta levels for categorical variable
Description
Calculates a vector of normal quantiles corresponding to effective theta levels for a categorical variable for use in a DiBello-Samejima distribution.
Usage
effectiveThetas(nlevels)
Arguments
nlevels |
Integer giving the number of levels of the categorical variable. |
Details
The DiBello–Samejima models map levels of categorical values into effective “theta” values, or corresponding continuous values. These can then be input into IRT equations to calculate cell probabilities.
The default algorithm works by assuming that the categories are created by cutting the normal distribution into equal probability intervals. The value returned for each interval is the midpoint (wrt the normal measure) of that interval.
Value
A vector of normal quantiles of length nlevels.
Author(s)
Russell Almond
References
Almond, R.G., Mislevy, R.J., Steinberg, L.S., Yan, D. and Williamson, D.M. (2015). Bayesian Networks in Educational Assessment. Springer. Chapter 8.
Examples
effectiveThetas(5)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.