seagull: lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent
Copyright policy )seagull: lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent
Summary Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalisation approaches are often the methods of choice. They are especially useful in case of multicollinearity which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalised by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realised by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, the package produces complete regularisation paths. Availability and implementation seagull is an R package that is freely available on the Comprehensive R Archive Network ( CRAN ; https://CRAN.R-project.org/package=seagull ; vignette included). The source code is available on https://github.com/jklosa/seagull . Contact wittenburg@fbn-dummerstorf.de
- Leibniz Association Germany
- University of Mary United States
- University of Washington United States
- University of Greifswald Germany
Optimization, QH301-705.5, R package, Computer applications to medicine. Medical informatics, R858-859.7, Machine Learning, High-dimensional data, Machine learning, Linear Models, Humans, Biology (General), Software, Algorithms
Optimization, QH301-705.5, R package, Computer applications to medicine. Medical informatics, R858-859.7, Machine Learning, High-dimensional data, Machine learning, Linear Models, Humans, Biology (General), Software, Algorithms
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