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).
| 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 |
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