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mixEMM: A Mixed-Effects Model for Analyzing Cluster-Level Non-Ignorable Missing Data
Contains functions for estimating a mixed-effects model for clustered data (or batch-processed data) with cluster-level (or batch- level) missing values in the outcome, i.e., the outcomes of some clusters are either all observed or missing altogether. The model is developed for analyzing incomplete data from labeling-based quantitative proteomics experiments but is not limited to this type of data. We used an expectation conditional maximization (ECM) algorithm for model estimation. The cluster-level missingness may depend on the average value of the outcome in the cluster (missing not at random).
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Package details |
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| Author | Lin S. Chen, Pei Wang, and Jiebiao Wang |
| Maintainer | Lin S. Chen <lchen@health.bsd.uchicago.edu> |
| License | GPL |
| Version | 1.0 |
| Package repository | View on CRAN |
| Installation |
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