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).

Getting started

Package details

AuthorLin S. Chen, Pei Wang, and Jiebiao Wang
MaintainerLin S. Chen <lchen@health.bsd.uchicago.edu>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the mixEMM package in your browser

Any scripts or data that you put into this service are public.

mixEMM documentation built on May 2, 2019, 9:31 a.m.