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).
| Version: | 1.0 |
| Published: | 2017-06-08 |
| Author: | Lin S. Chen, Pei Wang, and Jiebiao Wang |
| Maintainer: | Lin S. Chen <lchen at health.bsd.uchicago.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | mixEMM results |
| Reference manual: | mixEMM.pdf |
| Package source: | mixEMM_1.0.tar.gz |
| Windows binaries: | r-devel: mixEMM_1.0.zip, r-release: mixEMM_1.0.zip, r-oldrel: mixEMM_1.0.zip |
| macOS binaries: | r-release: mixEMM_1.0.tgz, r-oldrel: mixEMM_1.0.tgz |
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