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.
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Notes
- 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
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© 2013 Springer International Publishing Switzerland
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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
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DOI: https://doi.org/10.1007/978-3-319-00035-0_34
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Keywords
- Relaxed Assumptions
- Conditional Maximum Likelihood Estimation
- Multidimensional Rasch Model
- Trend Effect
- Trait Parameters
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
