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GFM

Generalized factor model for ultrahigh dimensional mixed-type data

https://github.com/feiyoung/gfm

Science Score: 26.0%

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    Found 6 DOI reference(s) in README
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    Low similarity (13.8%) to scientific vocabulary

Keywords

approximate-factor-model feature-extraction nonlinear-dimension-reduction number-of-factors
Last synced: 6 months ago · JSON representation

Repository

Generalized factor model for ultrahigh dimensional mixed-type data

Basic Info
  • Host: GitHub
  • Owner: feiyoung
  • Language: HTML
  • Default Branch: main
  • Homepage:
  • Size: 11.6 MB
Statistics
  • Stars: 2
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Topics
approximate-factor-model feature-extraction nonlinear-dimension-reduction number-of-factors
Created about 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

GFM

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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. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.

Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.

GFM and OverGFM 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:

Installation

To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package. {Rmd} install.packages("remotes") remotes::install_github("feiyoung/GFM") Or install the the packages "GFM" from 'CRAN' {Rmd} install.packages("GFM")

Usage

For usage examples and guided walkthroughs, check the vignettes directory of the repo.

Demonstration

For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.

NEWs

GFM version 1.2.1 (2023-08-10)

The function overdispersedGFM() that implements the overdispersed generalized factor model is added. In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.

Owner

  • Name: Wei Liu
  • Login: feiyoung
  • Kind: user
  • Company: SWUFE

Stastistics

GitHub Events

Total
Last Year

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 25
  • Total Committers: 1
  • Avg Commits per committer: 25.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 8
  • Committers: 1
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
feiyoung 1****3@q****m 25
Committer Domains (Top 20 + Academic)
qq.com: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 283 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
cran.r-project.org: GFM

Generalized Factor Model

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 283 Last month
Rankings
Forks count: 17.8%
Stargazers count: 28.5%
Dependent packages count: 29.8%
Average: 32.4%
Dependent repos count: 35.5%
Downloads: 50.4%
Maintainers (1)
LiuWeideng@gmail.com
Last synced: 7 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • doSNOW * depends
  • parallel * depends
  • MASS * imports
  • stats * imports
  • knitr * suggests
  • rmarkdown * suggests