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.
Science Score: 13.0%
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Keywords
Repository
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.
Basic Info
- Host: GitHub
- Owner: XiaoruiZhu
- Language: R
- Default Branch: master
- Homepage: https://xiaorui.site/SPSP/
- Size: 1.47 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 1
- Releases: 3
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Metadata Files
README.md
SPSP: an R Package for Selecting the relevant predictors by Partitioning the Solution Paths of the Penalized Likelihood Approach
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.
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
spsplasso1 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = lasso.glmnet, init = 1, standardize = FALSE, intercept = FALSE)
head(spsplasso1$nonzero) head(spsplasso1$beta_SPSP)
SPSP + adalasso
spspadalasso5 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = adalasso.glmnet, init = 5, standardize = T, intercept = FALSE)
head(spspadalasso5$nonzero) head(spspadalasso5$beta_SPSP)
SPSP + SCAD
spspscad5 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = SCAD.ncvreg, init = 5, standardize = T, intercept = FALSE)
head(spspscad5$nonzero) head(spspscad5$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>
Owner
- Name: Jeremy-Zhu
- Login: XiaoruiZhu
- Kind: user
- Location: Towson, MD
- Website: https://xiaorui.site/
- Twitter: jeremy_zhu89
- Repositories: 29
- Profile: https://github.com/XiaoruiZhu
Xiaorui (Jeremy) Zhu is an assistant professor at Towson University. His research interests include Business Analytics, Statistics, and Finance.
GitHub Events
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Top Committers
| Name | Commits | |
|---|---|---|
| XiaoruiZhu | z****9@g****m | 14 |
| Jeremy-Zhu | X****u | 10 |
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Last synced: 7 months ago
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- XiaoruiZhu (1)
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Packages
- Total packages: 1
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Total downloads:
- cran 212 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
cran.r-project.org: SPSP
Selection by Partitioning the Solution Paths
- Homepage: https://xiaorui.site/SPSP/
- Documentation: http://cran.r-project.org/web/packages/SPSP/SPSP.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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Latest release: 0.2.0
published over 2 years ago
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Maintainers (1)
Dependencies
- R >= 3.5.0 depends
- glmnet * depends
- lars * depends
- Rcpp >= 1.0.7 imports
- MASS * suggests
- testthat >= 3.0.0 suggests