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:
“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")For usage examples and guided walkthroughs, check the
vignettes directory of the repo.
For the codes in simulation study, check the simu_code
directory of the repo.
MMGFM version 1.1 released! (2024-09-17)