mvMISE: 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) <doi:10.1093/biostatistics/kxy022>. 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.
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