mRm-package: Parameter estimation and model selection in mixed Rasch...

In mRm: An R Package for Conditional Maximum Likelihood Estimation in Mixed Rasch Models

Description

This package provides routines for cML estimation and model selection in binary mixed Rasch models (Rost 1990). For a detailed discussion of the accuracy of parameter estimates and reliability of AIC- and BIC- based model selection techniques see Preinerstorfer and Formann (2011).

The core part of the algorithm has been implemented in C++, using parts of the Scythe Statistical Library (2007) for matrix manipulations. Rows with missing values and constant rows are excluded.

Author(s)

David Preinerstorfer

[e-mail]

http://homepage.univie.ac.at/david.preinerstorfer.

References

Pemstein, D., Quinn, K. M. and Martin, A. D. (2007). Scythe Statistical Library: Application programmer's interface. http://scythe.berkeley.edu/.

Preinerstorfer, D. and Formann, A. K. (2012) Parameter recovery and model selection in mixed Rasch models. British Journal of Mathematical and Statistical Psychology, 65, 251-262.

Rost (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271-282.

mRm documentation built on May 1, 2019, 9:13 p.m.