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:

• Liu, W., & Zhong, Q. (2025). High-dimensional multi-study multi-modality covariate-augmented generalized factor model. Biometrics, 81(3), ujaf107.

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 example1
• Simulated example2

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)