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).
|Author||Lin S. Chen, Pei Wang, and Jiebiao Wang|
|Date of publication||2017-06-08 15:21:36 UTC|
|Maintainer||Lin S. Chen <[email protected]>|
|Package repository||View on CRAN|
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