sgs
Repository for the sgs R package, which fits the sparse-group SLOPE and group SLOPE models
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Repository
Repository for the sgs R package, which fits the sparse-group SLOPE and group SLOPE models
Basic Info
Statistics
- Stars: 1
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- Forks: 1
- Open Issues: 1
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Metadata Files
README.md
sgs 
Implementation of Sparse-group SLOPE (SGS), a sparse-group penalisation regression approach. SGS performs adaptive bi-level selection, controlling the FDR under orthogonal designs. The package also has an implementation of Group SLOPE (gSLOPE), which performs group selection and controls the group FDR under orthogonal designs, as well as group-based OSCAR models. Linear and logistic regression are supported, both with dense and sparse matrix implementations. Both models have strong screening rules to improve computational speed. Cross-validation functionality is also supported. Both models are implemented using adaptive three operator splitting (ATOS) and the package also contains a general implementation of ATOS.
A detailed description of SGS can be found in Feser, F., Evangelou, M. (2023). "Sparse-group SLOPE: adaptive bi-level selection with FDR-control".
gSLOPE was proposed in Brzyski, D., Gossmann, A., Su, W., Bodgan, M. (2019). "Group SLOPE – Adaptive Selection of Groups of Predictors".
The strong screening rules are described in Feser, F., Evangelou, M. (2024). "Strong screening rules for group-based SLOPE models".
Installation
You can install the current stable release from CRAN with
r
install.packages("sgs")
Your R configuration must allow for a working Rcpp. To install a develop the development version from GitHub run
r
library(devtools)
install_github("ff1201/sgs")
Example
The code for fitting a basic SGS model is:
``` r library(sgs) groups = c(rep(1:20, each=3), rep(21:40, each=4), rep(41:60, each=5), rep(61:80, each=6), rep(81:100, each=7))
data = gentoydata(p=500, n=400, groups = groups, seed_id=3)
model = fit_sgs(X = data$X, y = data$y, groups = groups, vFDR=0.1, gFDR=0.1)
plot(model)
```
where X is the input matrix, y the response vector, groups a vector containing indices for the groups of the predictors, and vFDR and gFDR are the the target variable/group false discovery rates.
For gSLOPE, run:
``` r library(sgs) groups = c(rep(1:20, each=3), rep(21:40, each=4), rep(41:60, each=5), rep(61:80, each=6), rep(81:100, each=7))
data = gentoydata(p=500, n=400, groups = groups, seed_id=3)
model = fit_gslope(X = data$X, y = data$y, groups = groups, gFDR=0.1)
plot(model)
```
Owner
- Name: Fabio Feser
- Login: ff1201
- Kind: user
- Location: London, UK
- Company: Imperial College London
- Website: www.imperial.ac.uk/people/fabio.feser20
- Repositories: 1
- Profile: https://github.com/ff1201
PhD student on the StatML programme at Imperial & Oxford
GitHub Events
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- Issues event: 1
- Issue comment event: 2
- Push event: 11
- Pull request review event: 1
- Pull request event: 1
- Fork event: 1
Last Year
- Issues event: 1
- Issue comment event: 2
- Push event: 11
- Pull request review event: 1
- Pull request event: 1
- Fork event: 1
Packages
- Total packages: 1
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Total downloads:
- cran 360 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 12
- Total maintainers: 1
cran.r-project.org: sgs
Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
- Homepage: https://github.com/ff1201/sgs
- Documentation: http://cran.r-project.org/web/packages/sgs/sgs.pdf
- License: GPL (≥ 3)
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Latest release: 0.3.8
published about 1 year ago
Rankings
Maintainers (1)
Dependencies
- MASS * imports
- Matrix * imports
- Rcpp >= 1.0.10 imports
- Rlab * imports
- SLOPE * imports
- caret * imports
- faux * imports
- grDevices * imports
- graphics * imports
- methods * imports
- stats * imports
- SGL * suggests
- gglasso * suggests
- glmnet * suggests
- knitr * suggests
- rmarkdown * suggests
- testthat * suggests