Abstract
Linear logistic models with relaxed assumptions (LLRA) are a flexible tool for item-based measurement of change or multidimensional Rasch models. Their key features are to allow for multidimensional items and mutual dependencies of items as well as imposing no assumptions on the distribution of the latent trait in the population. Inference for such models becomes possible within a framework of conditional maximum likelihood estimation. In this paper we introduce and illustrate new functionality from the R package eRm for fitting, comparing and plotting of LLRA models for dichotomous and polytomous responses with any number of time points, treatment groups and categorical covariates.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The most recent version can be obtained from http://r-forge.r-project.org/projects/erm/.
- 2.
We plan to support continuous covariates in a future version.
References
Fischer, G. (1993). Linear logistic models for change. In G. Fischer & I. Molenaar (Eds.), Rasch models: Foundations, recent developments and applications. New York: Springer.
Fischer, G., & Ponocny, I. (1993). Extending rating scale and partial credit model for assessing change. In G. Fischer & I. Molenaar (Eds.), Rasch models: Foundations, recent developments and applications. New York: Springer.
Hatzinger, R., & Rusch, T. (2009). IRT models with relaxed assumptions in eRm: A manual-like instruction. Psychological Science Quarterly, 51, 87–120.
Mair, P., Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20, 1–20
R Core Development Team. (2011). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Sarkar, D. (2008). Lattice: Multivariate data visualization with R. New York: Springer.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Rusch, T., Maier, M.J., Hatzinger, R. (2013). Linear Logistic Models with Relaxed Assumptions in R. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-00035-0_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00034-3
Online ISBN: 978-3-319-00035-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)