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


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
OS X El Capitan binaries: r-release: mixEMM_1.0.tgz
OS X Mavericks binaries: r-oldrel: mixEMM_1.0.tgz


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