https://github.com/xiaoruizhu/spsp
A novel approach for feature selection based on the entire solution paths rather than the choice of a single tuning parameter, which significantly improves the accuracy of the selection.
https://github.com/xiaoruizhu/spsp
feature-selection r-package statistics variable-selection
Last synced: 3 months ago
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A novel approach for feature selection based on the entire solution paths rather than the choice of a single tuning parameter, which significantly improves the accuracy of the selection.
- Host: GitHub
- URL: https://github.com/xiaoruizhu/spsp
- Owner: XiaoruiZhu
- Created: 2021-07-26T03:27:57.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-12-06T05:15:31.000Z (about 2 years ago)
- Last Synced: 2025-10-22T03:49:02.295Z (3 months ago)
- Topics: feature-selection, r-package, statistics, variable-selection
- Language: R
- Homepage: https://xiaorui.site/SPSP/
- Size: 1.47 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
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README
# SPSP: an R Package for Selecting the relevant predictors by Partitioning the Solution Paths of the Penalized Likelihood Approach
[](https://cran.r-project.org/package=SPSP)
[](https://cran.r-project.org/web/checks/check_results_SPSP.html)
[](https://cranlogs.r-pkg.org/badges/grand-total/SPSP)
[](https://cranlogs.r-pkg.org/badges/last-month/SPSP?color=green)
[](https://cranlogs.r-pkg.org/badges/last-week/SPSP?color=yellow)
Overview
--------
An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
(namely SPSP) to identify the relevant features rather than using a single tuning parameter.
By utilizing the entire solution paths, this procedure can obtain better selection accuracy than
the commonly used approach of selecting only one tuning parameter based on existing criteria,
cross-validation (CV), generalized CV, AIC, BIC, and EBIC (Liu, Y., & Wang, P. (2018)
https://doi.org/10.1214/18-EJS1434). It is more stable and accurate (low false positive and false negative
rates) than other variable selection approaches. In addition, it can be flexibly coupled with
the solution paths of Lasso, adaptive Lasso, SCAD, MCP, ridge regression, and other penalized estimators.
## Installation
The `SPSP` package is currently available on [SPSP CRAN](https://CRAN.R-project.org/package=SPSP).
### Install `SPSP` development version from GitHub (recommended)
``` r
# Install the development version from GitHub
if (!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("XiaoruiZhu/SPSP")
```
### Install `SPSP` from the CRAN
``` r
# Install from CRAN
install.packages("SPSP")
```
## Example
The user-friendly function SPSP() conducts the selection by Partitioning the Solution
Paths (the SPSP procedure) to selects the relevant predictors. The user only needs
to specify the independent variables matrix, response, family, and a penalized method
that can generate the solution paths, for example, Lasso, adaptive Lasso, SCAD, MCP,
ridge regression. The embedded selection methods in this package can be called using
`fitfun.SP = lasso.glmnet`. Currently, six methods are included: `lasso.glmnet`,
`adalasso.glmnet`, `adalassoCV.glmnet`, `SCAD.ncvreg`, `MCP.ncvreg`,
and `ridge.glmnet`.
The following example shows the R codes:
``` r
library(SPSP)
data(HihgDim)
library(glmnet)
x <- as.matrix(HighDim[,-1])
y <- HighDim[,1]
# SPSP + lasso
spsp_lasso_1 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = lasso.glmnet,
init = 1, standardize = FALSE, intercept = FALSE)
head(spsp_lasso_1$nonzero)
head(spsp_lasso_1$beta_SPSP)
# SPSP + adalasso
spsp_adalasso_5 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = adalasso.glmnet,
init = 5, standardize = T, intercept = FALSE)
head(spsp_adalasso_5$nonzero)
head(spsp_adalasso_5$beta_SPSP)
# SPSP + SCAD
spsp_scad_5 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = SCAD.ncvreg,
init = 5, standardize = T, intercept = FALSE)
head(spsp_scad_5$nonzero)
head(spsp_scad_5$beta_SPSP)
```
References
----------
Liu, Y., & Wang, P. (2018). Selection by partitioning the solution paths. *Electronic Journal of Statistics*, 12(1), 1988-2017. <10.1214/18-EJS1434>