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GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively, which ensures that the factor matrix and loading matrix together with the number of factors can be well estimated.
Check out our JASA paper and our Package vignette for a more complete description of the methods and analyses.
GFM can be used to analyze experimental dataset from different areas, for instance:
- Social and behavioral sciences
- Economy and finance
- Genomics...
Please see our new paper for model details:
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")
Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")
Except this R version, there is also the Matlab version.
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.