MMGFM
Multi-Study Multi-Modality Generalized Factor Model
We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors. More details can be referred to Liu et al. (2025)
MMGFM
High-dimensional multi-study multi-modality covariate-augmented generalized factor model
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Latent factor models that integrate data from multiple sources/studies or modalities have garnered considerable attention across various disciplines. However, existing methods predominantly focus either on multi-study integration or multi-modality integration, rendering them insufficient for analyzing the diverse modalities measured across multiple studies. To address this limitation and cater to practical needs, we introduce a high-dimensional generalized factor model that seamlessly integrates multi-modality data from multiple studies, while also accommodating additional covariates.
Check out our Biometric paper and Package Website for a more complete description of the methods and analyses.
For more details, see:
Installation
"MMGFM" depends on the 'Rcpp' and 'RcppArmadillo' package, which requires appropriate setup of computer. For the users that have set up system properly for compiling C++ files, the following installation command will work.
## Method 1：
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("feiyoung/MMGFM")
## Method 2: install from CRAN
install.packages("MMGFM")
Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Simulated codes
For the codes in simulation study, check the simu_code directory of the repo.
News
MMGFM version 1.1 released! (2024-09-17)