A General Framework of Multivariate Mixed-Effects Selection Models

Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) . The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness.


Reference manual

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1.0 by Jiebiao Wang, 3 years ago


Report a bug at https://github.com/randel/mvMISE/issues

Browse source code at https://github.com/cran/mvMISE

Authors: Jiebiao Wang and Lin S. Chen

Documentation:   PDF Manual  

GPL license

Depends on lme4, MASS

See at CRAN